Decoding digital disruption
The theory of digital disruption can identify and explain the cause of disruption by a wide variety of challengers and in different industries. But just because a challenger poses a genuine disruptive threat does not mean that others in the industry are doomed. Incumbents may have some choices in how they respond. And the nature of the disrupter itself its value proposition and its roi value matrix can predict much of how the disruption will play out. http://ift.tt/2eWt5fYThree important variables that complete the theory of business model disruption are customer trajectory, disruptive scope, and multiple incumbents. Customer TrajectoryThe first variable to consider in any case of business model disruption is the customer trajectory. Which customers will provide the initial basis for the challenger’s market entry, and are they already customers of the incumbent? Business model disrupters can enter the market through one of two trajectories:
Christensen’s new market theory of disruption is based solely on cases that follow the outside-in customer trajectory. Indeed, one of the fundamental keys to his theory is that by starting outside the incumbent’s customer base, the disrupter makes it very hard for the incumbent to respond. However, many cases of business disruption today take the opposite customer trajectory: inside-out. All three of the cases we just saw were inside-out cases. The iPhone did not start by selling to buyers who were not previously in the market for a mobile phone. Rather, it began with a small subsegment of the type of customers who would certainly have owned a Nokia previously. At first, Nokia could reason that Apple was stealing a profitable but small part of the market and that Nokia could aim to hold on to the much larger majority of customers who were so far unwilling to pay the higher monthly fees for a smartphone. But over time, the iPhone’s customer base expanded outward to attract more and more of these customers. Similarly, Netflix did not start by appealing to customers who had never used video rental services like Blockbuster. Instead, its appeal was specifically to those who had pointing to their frustration with late fees and promising a better customer experience. And Warby Parker obviously had no option but to go after customers served by the incumbents like Luxottica. If you didn’t already own or need prescription glasses, you were unlikely to sign up for Warby Parker. The company’s rise may have started with some of the more price-sensitive customers from the current customer base (those who would give online ordering a try primarily for the $95 price tag), but it then expanded outward as it proved itself capable of delivering a true high-fashion brand as well as a superior customer experience. The second important variable in cases of business model disruption is the likely scope of the disruption. There is sometimes an assumption that whenever disruption occurs, the incumbent’s business, product, or service will be replaced 100 percent by the disruptive challenger. Out with the old, in with the new. In some cases, this does happen. When Henry Ford’s mass-produced automobile arrived, it was only a matter of years before the horse and buggy had basically vanished as a means of transportation. (Kevin Kelly has argued persuasively that no technology ever disappears from use entirely and, indeed, you can still enjoy a carriage ride around New York’s Central Park as an expensive tourist treat.) But in many cases of business disruption, the scope is not 100 percent. Even after being disrupted, the incumbent’s product or business model hangs on, confined to a diminished portion of the market but still a notable player in the industry. A recent example of this can be seen in bookselling, with the arrival of e-books. Thanks to Amazon’s development of the Kindle e-book format and electronic readers, consumers discovered they had a new choice for reading. The e-book and its online bookstore offered many compelling advantages: a lower price per book, a vast selection of choices, nearly instant purchase and download, and the ability to carry hundreds of books in your purse or bag at the weight of a paperback. The threat to booksellers was clear: there is no need for a customer to walk into their local bookstore to download an e-book. In the first few years after the launch of the Kindle, e-books enjoyed steady growth in market share. Many in the publishing industry looked at that growth curve, projected it outward, and nervously predicted that in a few short years, e-books would comprise the majority of book sales and publishers would no longer be able to afford to produce print editions. But then something unexpected happened. After a spurt of rapid growth, e-book sales leveled off. Various reports, confirmed to me by insiders in the industry, say that the plateau was about 30 percent of book sales by revenue. This was still enough to spark major disruption and shifts in the balance of power in the industry. (Borders, one of the largest retail booksellers in the United States, filed for bankruptcy in 2011.) Yet printed books, while diminished, certainly did not disappear into obsolescence. Although this surprised many observers, it was no fluke. In fact, I believe that by looking at the behavior of book buyers, it would have been quite easy to predict the scope of this particular disruption. One important lens for predicting disruptive scope is the product’s different use cases. Customers buy books on a variety of occasions, and they read books in a variety of settings. In some use cases for reading, it is quite clear that the e-book provides a far superior customer value proposition for example, when you are going on a trip and would like to have a variety of reading options but don’t want to be weighed down by a bag of books. In other reading use cases, however, a printed book may be better for example, if you want to take notes in the margin or read on the beach in direct sunlight (cases where e-book software and screens have continued to lag the paper medium). We can also look at use cases for book purchase. When the customer is seeking to try a new book while lying in bed, there is no match for the benefit of being able to download a sample chapter in seconds to their e-reader (and purchase the rest if they quickly decide they like it). But what about gift giving? No one I have ever asked has thought that an e-book was an acceptable substitute for a printed book when giving a gift. This is not a small point: a large portion of book sales takes place around holidays and other gift-giving occasions. If only a few use cases favor the old value proposition, we might expect consumers to sacrifice those benefits to shift entirely to a new value proposition. But in cases like books, where the customer can easily alternate purchases of the old product and the new one, it is predictable that we will wind up with a split market with some sales shifting to the disrupter’s offer and others remaining with the incumbent. In addition to use cases, the scope of disruption of a new business model can be influenced by customer segments. Sometimes the disrupter’s value proposition is highly preferable for some types of customers but not for others with different needs. In the Warby Parker case, we may see that certain eyeglasses wearers are likely to shift to its sales model, whereas others (those that buy luxury brands and specialty lenses or those that have better access to retail options) will stay with an incumbent like Luxottica. Lastly, network effects can play an important role in determining the scope of disruption. (This is particularly true for platform businesses). If a disrupter’s product or service increases in value as more customers use it (think of a platform like Airbnb, which relies on ample hosts and renters), this will initially be a hurdle to the new business. But it also means that if the disrupter manages to achieve a certain critical mass of adopters, its continued growth is nearly assured, and it will more likely end up with a very large share of the market. Multiple IncumbentsThe third variable to consider is multiple incumbents. A single disruptive business model can actually disrupt more than one incumbent. By multiple incumbents, I don’t mean similar companies in the same industry (e.g., the iPhone disrupting Motorola along with Nokia) but entirely different industries or classes of companies that are each challenged by the same new disruptive business model. The iPhone posed a disruptive threat not just to mobile phone companies (like Nokia) but also to desktop software companies (as Microsoft discovered that Windows was no longer the world’s dominant operating system) and online advertising companies (as Google had to move rapidly to stay relevant as computing moved to the small screen). Another interesting case of disrupting multiple incumbents can be seen in the meteoric rise of online messaging apps, such as WhatsApp, WeChat, LINE, and Viber (each of which has grown initially in somewhat different global markets). Their full range of features may vary, but at their core, each service has attracted hundreds of millions of customers with the ability to send mobile messages for free over Internet connections rather than being charged per message by the mobile phone’s service provider. Obviously, one incumbent industry that is being disrupted by this business model is telecommunications companies like Vodafone and América Móvil. For years, text messages had been a large source of revenue for these companies. By one estimate, services like WhatsApp cost the phone companies over $30 billion in texting fees in a single year. But telecommunications is not the only incumbent industry threatened by the free messaging apps. When Facebook chose to buy the largest one, WhatsApp, for 10 percent of its own stock (a $22 billion price), it was not because WhatsApp promised to generate huge new revenues for the social network. It was purely a defensive strategy against a new app that was on track to attract 1 billion customers of its own. If consumers spent more and more of their mobile screen time in apps like this one, they would spend less time in the world of Facebook-driven socializing. There may be another, even less likely industry that is being disrupted in part by WhatsApp. A long article by Courtney Rubin in the New York Times detailed the rise of mobile social networking (via text messaging, Instagram, Facebook, and Grindr) in the social life of multiple American college towns. Rubin’s ethnographic reporting uncovered a broad shift, described by both students and owners of college bars. Each described how students are spending less time and less money in the bars and coordinating more of their socializing through mobile networking, with alcohol purchased in stores and consumed in residences. College bars have always made their money charging for drinks. But the value they provided to customers was mostly the opportunity for serendipitous encounters and socializing. Now students find they can get that through their phones and are showing up to the bars sometimes only for a last drink before closing time (hardly enough to keep a bar in business). Many college bars are struggling, and some that have operated for decades are closing down. Yet another incumbent industry has been disrupted by the rise of mobile messaging. Now that we’ve examined the theory of business model disruption, how it expands on previous theories, and some of the key variables in its application, let’s put it to work with two strategic planning tools. These tools will allow businesses to gauge whether a threat they’re facing is disruptive to their business and, if so, to assess its likely course and then select among six possible incumbent responses. Tool: The Disruptive Business Model MapThe first tool is the Disruptive Business Model Map. This strategy mapping tool is designed to help you assess whether or not a new challenger poses a disruptive threat to an incumbent industry or business. If your business is the incumbent, you can use the map as a threat assessor to judge whether a challenger poses a traditional competitive threat that you can respond to with traditional countermeasures or whether it is a genuine disrupter. You can also use the map if your business is a start-up or an innovator within an enterprise. As you develop new ventures, the map will help you to identify the industries where you may pose a disruptive threat and those that may be less affected or more able to respond to your challenge. It includes eight blocks, each of which you will fill out in making an assessment of a potentially disruptive threat. Let’s look at each block and the question you must answer to fill it in. Step 1: ChallengerThe first step of the Business Model Disruption Map is to answer this question: What is the potentially disruptive business? The challenger you identify here may be a new competitor to your own established business. It may be your own start-up, attempting to disrupt an existing industry. Or it may be a potential new venture or initiative within your organization whose disruptive potential you are seeking to judge. Note that we are not yet labeling this challenger as “the disrupter.” The point of the map is to apply business model disruption theory to analyze the challenger, incumbent, and customer to determine if there really is a threat of disruption. In my experience running this scenario with numerous executives both to analyze existing threats and to test the market for a proposed new venture many challengers who have been dubbed disruptive do not in the end pass the test. In describing the challenger, you need to include its key offering: What are its unique products and services? What is it bringing to the market that does not exist yet? If your challenger were Netflix, you would include not just the name of the company but also a description of the monthly subscription service model that it is offering for movie rentals. Step 2: IncumbentThe second question of the Business Model Disruption Map is, Who is the incumbent? You may choose either a category of related businesses (e.g., video rental retail chains) or a leading example of the category (e.g., Blockbuster) in order to make the analysis more concrete as you compare the business models of the challenger and the incumbent. The other key point here is that, as we have seen, a challenger may pose a disruptive threat to more than one incumbent. Especially if you are the challenger, you should try to identify multiple incumbents who may be threatened by your new business model. Whenever you do identify more than one possible incumbent, you should complete the map multiple times once per incumbent. You may well find that your new business model poses a disruptive threat to one incumbent industry but that another incumbent can accommodate the success of your model or can co-opt and imitate it. Step 3: CustomerThe third question of the Business Model Disruption Map is, Who is the target customer? This is the customer being served by the challenger. In some cases, it may be a direct customer of the incumbent, but it also could be another key business constituency (e.g., a challenger could disrupt an incumbent by stealing away all its employees). It is critical to state who the challenger’s target is before you move on to the next stage to consider the value proposition being offered to that target customer. Once again, it is possible that a challenger could aim to usurp the incumbent’s relationship with more than one type of customer. In this case, you should also complete the map multiple times once per customer type. Step 4: Value PropositionThe next question of the Business Model Disruption Map is, What is the value offered by the challenger to the target customer? It is very important to answer this question from the point of view of the customer: What benefits do they stand to gain? Remember, the aim here is not to describe the product or service offered by the challenger (that should have been done in step 1). Nor it is to describe how the challenger will get customers to pay it (the revenue model will come in step 6, as part of the value network). The focus here is exclusively on the benefit to the customer: What value could they gain from the challenger’s offer? You can refer back to the list of value proposition generatives earlier on this blog to consider some of the many ways that digital business models provide value for customers. Step 5: Value Proposition DifferentialAfter you have described the challenger’s value proposition, the next question is, How does the challenger’s value proposition differ from that of the incumbent? The point here is to identify those elements of the challenger’s value proposition that are unique and different this is the value proposition differential. There is certain to be some overlap between the values offered by incumbent and challenger (e.g., Craigslist and newspapers both offer users the same core benefit of being able to advertise personal items for sale to a large local audience looking for them). You do not need to include those commonalities here. For some challengers, such as Craigslist, the differences in value proposition may all be positive that is, they are ways that the challenger offers additional customer value. In other cases, the value proposition differential may include benefits but also deficits, which you should indicate as such for example, for e-books as a challenger to print, you might indicate “less easy to read in direct sunlight.” Step 6: Value NetworkThe next question of the Business Model Disruption Map concerns the value network: What enables the challenger to create, deliver, and earn value from its offering to the customer? You can refer back to the list of value network components earlier on this blog as you map out the value network that makes the challenger’s offering possible. Your goal is to identify everything people, partners, assets, and processes that enables the challenger to offer its value proposition. If the challenger is new and unproven, this step should help to identify unanswered questions about its business model and whether it will actually be able to deliver the value proposition it is promising to the market. Step 7: Value Network DifferentialAfter you have described the challenger’s value network, the next question is, How does the challenger’s value network differ from that of the incumbent? Again, there may be some points of overlap between the challenger and the incumbent. If so, you can leave these out. The point here is to identify those elements of the challenger’s value network that are unique and different. Does the challenger’s offering rely on a unique enterprise data management asset or on specific skills that the incumbent currently lacks? Does it come to market via different channels than the incumbent uses? Does the challenger have a different pricing model or a different cost structure (e.g., less overhead costs for retail space or staff) than the incumbent? Is the challenger launching with a focus on a different market segment? The set of all these differences between the challenger and the incumbent is the value network differential. Step 8: Two-Part TestYou are now ready to answer the ultimate question of the Business Model Disruption Map: Does the challenger pose a disruptive threat to the incumbent? As described by the business model disruption theory, this question is answered by a two-part test. First, you need to assess how significant the differential in value is to the customer. Is the challenger’s value proposition only slightly better than the incumbent’s? Or does it radically displace the value of the incumbent? In some cases, this could be because the challenger offers a comparable product or service but with much better terms (think of Craigslist’s free version of classified ads). In other cases, the challenger may solve the same customer problems as the incumbent but also meet other customer needs at the same time (think of the iPhone, which was both a great cell phone and much more). In still other cases, the challenger may provide an offering that simply makes the incumbent’s offer much less relevant to the customer (as mobile social networking apps have made college bar rituals less relevant to American students). The first question of the disruption test, then, is this: Does the challenger’s value proposition dramatically displace the value proposition provided by the incumbent? If the answer is no, then the challenger does not pose a disruptive threat to the incumbent. The challenger may be a great innovator with a terrific new value proposition for customers. But if that offer grows to threaten too much of the incumbent’s business, the incumbent should be able to respond by matching, or remaining closely competitive with, the challenger’s value to the customer. If the answer to the first test is yes, then you can move to the second test of disruption. Here you need to assess the barriers that are posed by the differences in value networks between incumbent and challenger. Could the incumbent bridge these gaps, if it wished, so that it could deliver the same value to customers that the challenger does? For example, could the incumbent strike deals with channel partners similar to those employed by the challenger? Could the incumbent eliminate any difference in its fixed costs or compensate for them otherwise? Is it possible for the incumbent to overcome the network effects that the challenger may have already built up to its own benefit? Any major difference in value network could be the hurdle that prevents the incumbent from responding effectively. The second question of the disruption test is this: Do any of the differences in value networks create a barrier that will prevent the incumbent from imitating the challenger? If the answer is no, then the challenger does not pose a disruptive threat to the incumbent. It may be a dire asymmetric competitor, but there is no fundamental obstacle to the incumbent responding by matching its strategy. The incumbent may have to sacrifice some of its current profit margins in the process, just as it would in a price war with a traditional competitor. But the challenger is not truly disruptive. On the other hand, if the answer is yes, then the challenger has passed both tests of business model disruption. The value it offers to the customer will dramatically outstrip or undermine the value delivered by the incumbent, and the incumbent will face intrinsic structural barriers that prevent it from responding directly. Business disruption happens when an existing industry faces a challenger that offers far greater value to the customer in a way that existing firms cannot compete with directly. The challenger is a disruptive threat. But is all hope lost? In the face of a real disruptive threat, can the incumbent expect complete and rapid extinction (like the horse carriage industry facing automobiles), or is there an opportunity for the incumbent to respond or at least hold on to some of its glory? That is where the next tool comes in. Tool: The Disruptive Response PlannerIf you have determined that you are, in fact, looking at a true disruptive challenger to an incumbent business, you are now ready to apply the second tool. The Disruptive Response Planner is designed to help you map out how a disruptive challenge will likely play out and identify your best options for response. The first three steps help you to assess the threat from the disrupter in terms of three dimensions: customer trajectory, disruptive scope, and other incumbents that may be affected. You can then use these insights in the last step to choose among six possible incumbent responses to a disruptive challenger. Step 1: Customer TrajectoryThe first step in predicting the possible impact of a new disruptive business model is to understand its customer trajectory: What customers are likely to adopt the disrupter’s offer first, and how will its market spread from there if it is successful? OUTSIDE-IN OR INSIDE-OUT?As we have seen, there are two types of customer trajectories for disruptive business models: outside-in and inside-out. It is critical to start by judging which of these paths your disrupter is likely to take in entering the market. Outside-in disrupters begin by selling to noncustomers of the incumbent and then work their way inward to encroach on the incumbent’s own customers. As described by Christensen, outside-in disrupters don’t appeal at first to the incumbent’s customers because of their lesser features, but they do appeal to customers who could not afford or access the traditional incumbent’s services. As the disrupter improves, it begins to attract the incumbent’s customers as well. Christensen’s theory has shown how industries with barriers that exclude many potential customers higher education, health care, financial services are ripe for disruption. As he and Derek van Bever write: “If only the skilled and the rich have access to a product or a service, you can reasonably assume the existence of a market-creating opportunity.” Inside-out disrupters follow a different path. They begin by selling to a segment of the incumbent’s current customers and then work their way outward to take more of its market. We have seen many examples of these: iPhone versus Nokia (started by selling to existing mobile phone users) and Netflix versus Blockbuster (explicitly marketed to existing movie renters as a better alternative). Rather than starting out as inferior to the incumbent’s offer but “good enough” for buyers who could not afford the incumbent, these disrupters offer much better value from the beginning. These are business model innovations that would quickly draw a competitive response from the incumbent except that they rely on a value network that the incumbent finds impossible to imitate. WHO IS FIRST?Once you know if the disruption will be outside-in or inside-out, you will want to identify which specific types of customers will likely be first to adopt the disrupter’s product or service. For inside-out disruptions, you should ask these questions: Who among your current customers would be most attracted to the disruptive offer? Are there any hurdles to their early adoption (e.g., reliability is not yet proven)? Are there some current customers for whom those hurdles matter less (e.g., they are eager to try out new products or are less concerned about established brands)? For outside-in disruptions, you should ask these questions: Who is currently most motivated but unable to afford or access your products or services? Which of these hurdles (price or access) is the bigger barrier for them? Which hurdle does the disrupter’s offer help them more to surmount? WHO IS NEXT, AND WHAT WILL TRIGGER THEM?Once you identify the likely first customers for a disrupter’s offer, you need to identify who will be attracted to the offer next. For inside-out disrupters, that is likely another subgroup of your customers. For instance, if Warby Parker starts by appealing to the supporters of social causes, will its next customers be tech-savvy eyeglasses wearers? For outside-in disrupters, the key question here is this: When will the disrupter “tip” from selling to noncustomers and start to reach your own customers? You also need to think about what will trigger these second-wave customers to come on board. These triggers can often be other customers’ behaviors; wait-and-see customers, for example, may become interested as they see others using a product, or they may be persuaded by word of mouth. The trigger may be some further innovation by the disrupter, such as dropping prices further or improving features or both. Or the trigger may simply be visibility as press coverage, marketing, or geographical distribution brings the disrupter’s offer to the attention of the next wave of new customers. IMPLICATIONSKnowing the likely customer trajectory has important implications. As the incumbent, you need to know which of your current customers to keep an eye on first to see if they defect. You must also know if the challenger doesn’t need any of your customers to get started (an outside-in disrupter). In that case, you should develop a strategy to compete for these same “outside” customers, where the disrupter may grow first before moving into your own market. Step 2: Disruptive ScopeThe next step in assessing the threat from a disruptive business model is to consider its likely scope. This describes how much of the market (how many customers) are likely to wind up switching to the disrupter once it is well established. Disruptive scope can be predicted by looking at three factors: use case, customer segments, and network effects. USE CASEYou should first identify various use cases where customers purchase and use your product or service. Make two lists: In what situations do customers purchase your offering? In what situations do they utilize it? (There should be overlap in the lists but also some differences.) Then, for each use case on both lists, consider the disrupter’s value proposition. In which cases is the disrupter clearly preferable for the customer? In which cases is there an advantage for your offer? As we saw in the case of e-books versus print books, a disrupter may have a clear advantage for some use cases (e.g., boarding a plane with a variety of reading material) but be at a disadvantage in other use cases (e.g., giving a gift to a friend). You should also consider whether there are costs to multihoming. How difficult is it for a customer to buy from your business for some use cases and from the disrupter for others? For readers, it is not that difficult to buy printed books as gifts while keeping an e-reader stocked for their own travel. CUSTOMER SEGMENTSNext you should subdivide the customers for which you and the disrupter are competing. Rather than seeing them as one monolithic group, try to divide these customers into segments based on their shared needs. What drives them to use this product category? What are their relevant needs? (This may sometimes correspond to some of your use cases.) Then, for each segment, consider whether the disrupter is extremely attractive in comparison to your business. Recall Zipcar? This on-demand car rental service seemed to pose a disruptive challenge to traditional car rental companies when it launched. Zipcar members pay a small monthly fee to have access to any of the Zipcars parked in their metropolitan area. They simply look on their phone app, walk up to a nearby car, and type an entry code into the keypad lock on the car door. This self-service model appears much more convenient than the customer service experience of picking up a car at a traditional rental agency. But Zipcar never supplanted the traditional rental model for most customers. It turns out that certain types of consumers (e.g., those in dense cities with regular needs for short-term car rentals) were ideally suited to the membership model. But other consumers (e.g., those in rural areas or those with more infrequent rental needs) did not benefit as much from that model. While expanding to four countries and nearly a million members, Zipcar has stayed focused on college campuses and major cities. NETWORK EFFECTSThe third factor to consider in predicting a disrupter’s scope is network effects. Many services, especially platform businesses, become more valuable with each new customer that participates. As more customers bought iPhones, it became easier for Apple to attract more developers to create apps for the platform. As more developers built apps, the advantages of the iPhone versus an incumbent like Nokia grew as well. If you look at a cryptocurrency like Bitcoin, there is certainly the possibility that it could disrupt various incumbents that provide traditional financial services (credit card payments, savings accounts, foreign exchange). But the biggest hurdle to a currency like Bitcoin is that currencies are extremely dependent on network effects. As long as few merchants accept Bitcoin and few other customers are using it, the benefits to a new user are mostly hypothetical. On the other hand, incumbents watching Bitcoin need to realize that enough momentum in user adoption could quickly lead to a snowballing effect (much like users flocking to a fast-growing social network such as Instagram or Snapchat) that transforms it quickly from a curiosity to a major disruptive force. IMPLICATIONSNow that you have examined use cases, customer segments, and network effects, you should be able to make an informed prediction of the likely scope of impact of a new disrupter. Broadly, we can think of three likely outcomes of a disruptive business model. One is a niche case, where the disrupter is attractive to only a very specific portion of the market. Other disrupters may wind up splitting the market, with the disrupter’s and the incumbent’s business models each taking large shares. And in cases of a landslide, the disrupter quickly takes over the entire market, pushing the incumbent into obscurity. Step 3: Other IncumbentsWe saw earlier how a single new business model can disrupt multiple incumbent industries. When assessing a disrupter to your business, it is easy to focus on its impact on only one industry (your own). But to understand the competitive dynamics at work, it is critical to expand your reference frame to consider other incumbent businesses and how they will be impacted and respond to the disrupter. VALUE TRAINThe first place to look for additional businesses that may be disrupted is in your own value train. Start by asking which product or service the disrupter most resembles. For example, the product most like e-books would be printed books. You can then look at a value train of everyone involved in delivering that product or service from the originator (authors), to producers (book publishers), to distributors (book printers, distribution companies, and retail and e-tail booksellers) until the value reaches the final consumer. Then ask which of these different types of companies may be disrupted if the new business model is successful? For e-books, the answer would likely be retail booksellers, printers, and distributors; authors and publishing houses are most likely able to adapt to the new business model. SUBSTITUTIONAnother way of identifying additional incumbents is to think of products or services for which the customer may substitute the disrupter’s offering. Ask yourself two questions: If a customer starts spending more money on the disrupter’s product or service, where else might they spend less money? If the customer starts spending more time on the disrupter, where might they spend less time? Considering the early iPhone, you can easily see that if customers spend money on an iPhone, they are less likely to spend money on a phone by another handset maker like Nokia. (Digging deeper, you might determine that if they spend more money on iPhone apps, they are likely to spend less on other entertainment.) If you ask where avid iPhone users spend their time, you might realize that they spend less time conducting Web searches on their desktops (a hugely profitable business for Google) and more time on mobile Web searches (much less profitable). One other question about substitutes is worth asking: If the disrupter’s current product continues to become much better in terms of performance and quality, for what other products or services might it start to become a substitute? Looking at the initial iPhone, it is possible to imagine that if it continues to get faster, more powerful, and a bit bigger, it does indeed pose a threat as a substitute for laptop computers, televisions, and other categories. LADDERINGThe last way to identify more incumbents who may be impacted by a disrupter is to look at both immediate and higher-order customer needs. You start by asking these questions: What problem or need does the disrupter solve or meet for its customers? Who else tries to solve that problem? For example, looking at messaging apps like WhatsApp, you can see that customers use them to meet their need for expedient text messaging with friends (especially friends in different countries). That need was previously met by telecommunications providers, which, as we saw, lost billions of dollars in texting fees due to this disruption. Next you can attempt to unearth higher-order customer needs through a process known as laddering. In this market research technique, you ask a customer a series of “Why?” questions to get at the reasons behind their immediate motivations. For example, if you ask college students why they use WhatsApp, they might say “to message easily with my friends.” If you ask why they use it for that, they might say “to be able to make plans and swap photos.” If you ask why that matters, they might say “so we can meet up and find out wherever the cool get-togethers are happening.” This might lead you to realize that mobile messaging apps are meeting the need for convening social interactions, which was formerly met by visiting the college bar. This kind of laddering can reveal products or services that are made less necessary for customers by the disrupter, even though the disrupter doesn’t appear to be competing directly. IMPLICATIONSBy looking at value trains, different means of substitution, and different levels of customer needs, you may have identified multiple incumbents types of companies that will be disruptively challenged by the same new disrupter. As an incumbent, it is always valuable to know who else may be threatened by the same disrupter that is threatening you. In planning your own response, it is important to see how these other incumbents are responding or consider how their responses might parallel yours. You may also find that these “enemies of my enemy” could serve as allies in response to the disruptive threat. As described above, Google saw that it was threatened just as much by the rapid rise of the iPhone as were cell-phone handset makers. As we will see, this led to Google’s choice of response to the disruptive threat. Step 4: Six Incumbent Responses to DisruptionThe final step of the Disruptive Response Planner is to plan your response as an incumbent. To do so, you will use what you have learned regarding the trajectory, scope, and other incumbents of the disrupter you are facing to help you choose which strategic responses are most promising for your circumstances. As an incumbent, you have six possible responses when faced with a disruptive challenger: THREE STRATEGIES TO BECOME THE DISRUPTER
THREE STRATEGIES TO MITIGATE LOSSES FROM THE DISRUPTER
These six strategies are not exclusive; you can combine them (and, in fact, some of them work best together). The first three responses seek to occupy the same ground as the disrupter. The last three responses seek to reduce its impact on your core business. Depending on your own circumstances, only one or a few of these incumbent responses may be workable, so it is best to be familiar with each of them. Let’s look at each response and see where and how you might best apply it. ACQUIRE THE DISRUPTERThe most direct response for an incumbent faced with a disruptive challenger is to simply acquire the challenger. This is how Facebook dealt with the challenge of WhatsApp. When Google’s Maps product faced a potential disrupter in Waze, it bought the company. When the car rental giant Avis saw that Zipcar had invented a disruptive business model, Avis also bought its challenger. If you are considering buying your disrupter, knowing who the other incumbents are will help you predict who else might compete with you to drive up the price. If you do acquire your disrupter, you should continue to run it as an independent division. That’s what Facebook, Google, and Avis did in all the above cases. That means the disrupter you own will continue to steal customers from your core business (and possibly at a lower profit margin). But if you don’t take measures to keep the acquired disrupter independent, you will inevitably put the interests of your core business above the goal of serving your customers. And that will create an opportunity for someone else to launch a similar business and steal away your disappointed customers. Acquiring the disrupter is not always possible. A start-up with sufficient venture capital may refuse to sell, as was the case with Facebook’s failed $3 billion bid for messaging app Snapchat. Or the disrupter may be part of a bigger company than the incumbent. Amazon’s e-books posed a clear disruptive threat to retail booksellers like Barnes & Noble, but the retailers were much smaller than Amazon (for whom e-books was just a part of its business). Often, acquiring the disrupter is overlooked or rejected in the early stages, when acquisition is still an option. In 2000, shortly after Netflix launched its subscription DVD model, the start-up’s CEO, Reed Hastings, flew to Dallas to meet with Blockbuster’s CEO, John Antioco. Hastings proposed the video giant and the newcomer form a partnership, with Netflix handling online distribution and Blockbuster the retail channel. Hastings was laughed out of the office.25 Blockbuster didn’t get a second chance. Acquisition does not always need to be 100 percent (a partnership with Netflix would have proved a godsend for Blockbuster), but it does require swallowing your pride and recognizing the disrupter’s advantages before it scales so big as to no longer need your help. LAUNCH AN INDEPENDENT DISRUPTERThe second incumbent response is to launch a new business of its own that imitates the business model of the disrupter. Instead of purchasing the disrupter outright, the incumbent leverages its scale and resources to try to beat the disrupter at its own game. This is the response Christensen proposes: “Develop a disruption of your own before it’s too late to reap the rewards of participation in new, high-growth markets.” In order to launch your own disrupter, however, you, the incumbent must be willing to cannibalize your own core business. After all, you are trying to re-create the very business model that is disruptively attacking your traditional business. Charles Schwab implemented this strategy when it saw the growth of online brokerages like Joe Ricketts’s TD Ameritrade, launching its own online service that competed with its full-service offerings. This strategy again requires you to keep the new disruptive initiative walled off in an independent part of your company. You should run it on its own P&L, with no responsibility to save or support your core business. Although the independent unit should have access to some of the main company’s resources, it should maintain a small and lean organization so that it can evolve quickly rather than becoming a sclerotic version of the nimble disrupter it is trying to beat. You may even launch an independent disrupter preemptively as you see a possible new business model based on emerging trends and technology. Saint-Gobain, a leading global retailer of construction materials, looked at the trends in e-commerce and recognized the opportunity for an online store in its industry. Rather than waiting for a start-up to capture this opportunity, Saint-Gobain launched Outiz, an online-only retailer in the French market. Outiz has been tasked with competing directly with the parent company’s own brick-and-mortar retail brands. Launching an independent disrupter is not easy, but it is plausible if the differences in value networks are your company’s organizational culture, cost structure, revenue model, and customer segments. You can potentially overcome these kinds of barriers by insulating the self-launched disrupter from the rest of your business. SPLIT THE DISRUPTER’S BUSINESS MODELWhat if the incumbent lacks some core capabilities like intellectual property, brand reputation, essential skills, or the right partners that are needed to re-create the disrupter? In that case, simply insulating a new initiative from the rest of the organization is not sufficient. But the incumbent may still be able to re-create the disrupter’s business model by splitting the job with other businesses. This may be a good strategy if your prior analysis uncovered multiple incumbents and their value networks are complementary to your own. This was the strategy used by Google when it launched the Android operating system in response to Apple’s iPhone, which was threatening its advertising business. Google already had a core mobile operating system from its 2005 acquisition of Android Inc. It also had the key software assets required for an iPhone-like device: Google Search, Google Maps, YouTube video, and the Chrome Web browser. But Google knew it lacked the skills and assets required to design and manufacture hardware to compete with Apple, so it licensed its operating system and mobile software to diverse companies Samsung, Sony, HTC, and others with the capabilities to build great smartphone hardware. By splitting the iPhone’s business model with these firms, Google was able to bring Android phones to market with a value proposition that rivaled that of the iPhone. The key to splitting a disrupter’s business model is to find other businesses that complement your own value network and partner with them to bridge the gaps that are preventing you from launching your own disrupter. Ideally, those partners are also threatened by the same disrupter, so they will be motivated to collaborate. REFOCUS ON YOUR DEFENSIBLE CUSTOMERSIncumbents don’t have to react just by becoming the disrupter; they can also act defensively in shoring up their own core business. That is the focus of the next two incumbent responses. These strategies can often be deployed in combination with the previous ones. The first of these defensive strategies is to refocus the incumbent’s core business on those customers it has the best chance of retaining. You should use this strategy whenever you have identified a likely split market or niche market for your disrupter. It is essential that you not engage in wishful thinking and simply continue to invest in your traditional business as if its future will look the same as its recent past. Refocusing should appeal to the customers that you think are most likely to stay with you despite the disrupter. Remember, they won’t stay with you out of loyalty; they will stay because your business model still offers more value to them. Look back at your scope analysis and the customer segments and use cases that favored your product. Look also at the customer trajectory you predicted: Who will likely depart for the disrupter first, and who may follow? Then plan to shift your core business to focus on them, even while that business is likely shrinking. When book retailer Barnes & Noble found its business disrupted by online book delivery, it refocused its business model on high-margin products like children’s books and coffee-table books because the customers buying these still valued the ability to browse the products in a store environment. In refocusing your core business, you should aim your marketing, messaging, and continued product innovations at these most defensible customers. If your strategy involves cutbacks, focus on reducing the operations serving those customers that you are likely to lose and on continuing to deliver value to those you are likely to retain. DIVERSIFY YOUR PORTFOLIOThe next way that incumbents can mitigate the disruption of their core business is by diversifying their portfolio of products, services, and business units. They can accomplish this by repurposing the firm’s unique skills and assets in new areas and by acquiring smaller firms in the areas into which they want to extend. When digital photography was going mainstream and disrupting the business of photographic film, the top two incumbent businesses were Kodak and Fujifilm. While Kodak slid into a long decline that ended in bankruptcy, Fujifilm managed to adapt and survive. “Both Fujifilm and Kodak knew the digital age was surging towards us. The question was, what to do about it,” said Fujifilm’s CEO, Shigetaka Komori. “Fujifilm was able to overcome by diversifying.” Under Komori’s leadership, the firm spent years applying its technical expertise in chemicals, developed in producing film, in diverse areas such as flat-panel electronic screens, drug delivery, and skin care. By the time Kodak filed for bankruptcy, Fujifilm’s film business was only 1 percent of its revenue, but health care and flat-panel displays were 12 percent and 10 percent, respectively. Diversification allows you to leverage the strengths in your value network in new business areas, and although these areas may not initially be as profitable as your core business, they can create new opportunities for growth and make your firm less susceptible to total disruption. PLAN FOR A FAST EXITThe last strategy for an incumbent response to disruption is the least desirable one. When a disruptive challenger poses an irresistible threat to an incumbent’s entire market and there is no feasible way to launch a disruption of its own, the incumbent needs to plan for a fast exit. This is the case when the disruptive scope is a landslide because all customers and use cases are vulnerable or because strong network effects lead to a winner-take-all scenario. In planning to exit a market, you should assess all your firm’s assets, especially intangible assets (patents, brand names, etc.) that can be sold. You may also choose to spin off the indefensible part of your business from other divisions that can survive on their own rather than letting the vulnerable part bring down your entire enterprise. In most cases, you can pursue one or a combination of the first five incumbent responses, but sometimes an orderly liquidation of assets is the necessary call. Beyond DisruptionThe fact of disruption is inescapable. The very strategies that comprise the digital transformation playbook for traditional enterprises are also the source of their biggest disruptive threats. And yet disruption is both more and less than it seems.Disruption is more diverse than our prevailing theory has held. Disruption is driven by more than just lower prices and accessibility for new customers; it can be triggered by any dramatically greater value proposition for the customer. Disruption can happen not just on the familiar trajectory of outside-in but from inside an existing market outward as well. But disruption is also less than we sometimes imagine it to be. First and foremost, not every innovation (no matter how breathtaking) is necessarily a disrupter of an existing industry. Disruption is rarely total; most disrupters attract a significant part of an incumbent’s market without taking 100 percent. Disruption is also less than irresistible. Even though it may pose an existential threat to an incumbent’s business model, there are strategies the incumbent can use to adapt, diversify, and continue its enterprise by adding new value for customers. More than anything else, responding to disruption requires that a business be willing to question its own assumptions and focus on the unique mission of how it serves customers. via Blogger http://ift.tt/2ffGviB November 21, 2016 at 03:50PM
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Developing customer value proposition
The Value Proposition Roadmap is a tool that any organization can use to assess and adapt its value proposition for its customers. You can use it to identify new and emerging threats as well as new opportunities to create value for your customers. It will help you synthesize those findings into a plan to create new, differentiated value in a changing landscape. Above all, if your company is under pressure, the tool will force you to challenge your assumptions, step back from focusing on defending your past business, and use your customers’ perspective to imagine new ways forward. http://ift.tt/2fgNPxRThe Value Proposition Roadmap uses a six-step process to map out new options for your business. Let’s look at each of the steps in detail. Step 1: Identify Key Customer Types by Value ReceivedThe first step is to identify your key customer types, distinguished by the different kinds of value they receive from your business.For a hypothetical University XYZ, for example, the key customer types might include undergraduate students, their parents, alumni, and employers (looking to recruit students and alumni). Note that each of these customer types gains somewhat different value from the university. For undergraduate students, the value may be a mix of education, social environment, and certification to help in job seeking. For alumni, the value of their ongoing relationship with the university may be based more on career networking or a sense of pride in the school’s athletics, research efforts, or reputation. For employers, the value of the school may be in preparing graduates with certain skills (topical knowledge, critical thinking, or technical skills) as well as credentialing and assisting in finding the right recruits. If you are having trouble identifying different customer types, look to differences in customers’ motivations or jobs to be done (For what different reasons do they do business with me?) or in their use cases (In what different circumstances do they do business with me?). Looking at these is more useful than looking at differences in demographics (students come from all over the world; alumni are of all different ages; neither of these factors is as critical to their relationship with the university as the different kinds of roi value matrix they receive). Step 2: Define Current Value for Each CustomerThe next step is to define your current value proposition for each customer type. This starts with a list of value elements the various benefits that each customer type gains from the relationship with your business. After listing the value elements, write a summary statement of the value that this type of customer receives from your business the overall value proposition. In table, you can see value proposition definitions for University XYZ’s: Notice that nowhere in the university’s value propositions is there a list of products or services or a list of fees paid or ways that it will monetize each customer type. Your value proposition should always be defined in terms of benefits that matter to your customers. Notice also that each of the university’s customer types has a distinct overall value proposition. Customer types may have some value elements in common (undergraduate students and alumni both care about a career network; parents and employers both care about credentialing). But no two customer types should have identical lists of value elements. If you arrive at identical value propositions for two customer types, dig deeper. If you still don’t find a significant difference in the value they receive from your business, combine them into a single customer type. Step 3: Identify Emerging ThreatsNow that you understand your current value to customers, it is important to understand emerging threats that could undermine it. They could do so by competing with the value you offer, substituting for it, or simply making it less important to your customers. At this point, you are not looking for factors that you know will undermine your business but simply ones that might have the potential. Following are three sources to consider for potential threats to your current value proposition: New technologies: Look for emerging technologies that seem relevant to your industry and your customers’ experience. For the recorded music industry, the MP3 compression format was one such technology. For pinball machine maker Williams, early video games like Pong were identified as a potential threat to established games. Changing customer needs: These can include changes in consumers’ habits, lifestyles, and social behaviors. Facebook recognized the shift in its users’ computing time from desktop to mobile devices as a potential threat. For B2B companies, changing customer needs may include changes in laws, regulations, or the business environment. Think of Mohawk Fine Papers and the shift in financial reporting rules, which meant that its client businesses had less need of printed documents. New competitors and substitutes: A threat to your current value proposition can often come from an asymmetric competitor entering from another industry. For Encyclopædia Britannica, Inc., that included Microsoft, when the software maker bundled a free encyclopedia with its operating system. Other times, the new entrant may substitute for your value proposition by meeting your customers’ need in a new way. The publishers of The Deseret News saw this as websites like Craigslist filled the need that used to be met by newspaper classified ads. Step 4: Assess the Strength of Current Value ElementsAt this point, you should return to the lists of value elements you developed for your customer types in step 2. You can now assess the strength of the specific elements of value that you provide. For each value element that you listed, ask three questions: Are there any ways that this is a source of decreasing value to the customer? This decrease could come from one of the emerging threats identified in step 3 (a new technology, customer need, or competitor). Other factors could include declining relevancy to the customer, cheaper options, and underinvestment by your business (e.g., if cost cutting has led you to deliver less value here than in the past). Are there any ways that this is a source of increasing value to the customer? New innovations by your business may mean you are increasing the value you deliver through this particular element. Or the value may be increasing due to this element’s growing importance to the customer, scarcity in the market, or differentiation compared to your competitors. What is the overall verdict? Based on these combined factors, you should now make an overall assessment for each value element. Is it strong (still a powerful source of value for your customer); challenged (under threat and perhaps not as strong a source of value as in the past); or disrupted (no longer relevant or meaningful to this customer type and uncertain to recover in value). This process should provide a clear assessment of the strength of your current value elements. Table 6.5 shows University XYZ’s assessment of value elements for its undergraduate students. Step 5: Generate New Potential Value ElementsYour next step is to try to identify new value elements that you could offer to this customer type. This is a chance to examine some of the external forces that may be weakening your value proposition and use them as a source of opportunity for new value that you can create for your customers. To generate new value elements that you could offer to your customers, look in three areas: New technologies: How could new technologies allow you to create additional elements of value for your customers? Trends in your customers’ sociocultural or business environment: Consumer lifestyle and business trends may provide new opportunities for you to create value, even with the same products. Unmet customer needs: Get close to your customers. Observe them directly. Talk to lead users. You’re sure to find some unmet needs that no one is fulfilling; one of them may be an opportunity for your business to add new value. Step 6: Synthesize a New Forward-Looking Value PropositionThe final step of the Value Proposition Roadmap is to synthesize everything you have learned about your value proposition for each customer type. Review your value elements, and place each into one of four columns: Core elements to build on: These elements are a source of strength that you plan to use as a focus of continuing innovation. Weakened elements to bolster: These are current value elements that are losing their impact for your customers and that you have chosen to try to reinforce and improve. Disrupted elements to deprioritize: These are former sources of value that have lost their ability to deliver for your customers and that you have chosen to move away from and drop from your strategic focus. New elements to create: These are new value elements that you have identified as opportunities to add more value for your customers and that you have chosen to invest in for future growth. Now you can craft a revised overall value proposition for each customer type. This should be a forward-looking statement of how you intend to create value as you continue to evolve your offerings for this particular customer type. Finally, list any ideas you have for specific initiatives (new product features, service offerings, etc.) you can use to deliver on your revised value proposition. When you have finished, you will have in your hands a complete roadmap for adapting your value proposition. This roadmap includes a strategic analysis of emerging threats, an innovation brief that can be used by those working on your next-generation products and services, and a customer-centric analysis of where your business is today and where it is going in the future. If applied as a regular part of strategic planning, the Value Proposition Roadmap can be a helpful tool for anticipating customer needs, assessing new technologies proactively, and applying resources to new strategic opportunities. Organizational Challenges of Adapting Your Value PropositionThe benefits of continuously adapting a business’s value proposition may be clear. But that does not make it easy. It requires the business to step outside the inward-looking habit of focusing on its own products and processes and, instead, to take the point of view of the customer. It also requires the business to imagine a version of itself that is different than what perhaps worked very well in the past. In particular, a larger or longer-established organization may find it much harder to gain a clear view of its value to the customer and of the opportunity, and necessity, to adapt while it still has the chance. Dedicating LeadershipThe first challenge for value proposition adaptation is leadership. Who will be in charge of making the change happen? Even when a strategy team is effectively set up to identify opportunities for evolving the business’s value proposition, someone needs to be in charge of acting on the new opportunities. For years, the U.S. Postal Service has struggled to balance its finances as technology has changed the needs of customers for its services (When did you last send anyone a post card?). In 2014, its inspector general released a report arguing that the USPS should move into providing nonbank financial services (bill payments, money orders, prepaid cards, international money transfers, etc.) to its customers, many of whom are underserved by traditional banks. The report was praised in the press, on Capitol Hill, and even in the pages of American Banker. But more than a year later, no action had been taken, despite support for the idea from the American Postal Workers Union. A newly sworn-in postmaster general had focused on the current value proposition (e.g., whether to trim Saturday mail delivery), but no one appeared to be in charge of turning innovative ideas for new customer services into a reality. Leadership tenures may be another important factor in value proposition adaptation. As Henry Chesbrough has observed, many large firms move their general managers in two- or three-year rotations among different business units in order to develop their leadership and knowledge of the whole firm. However, undertaking significant change to a unit’s value proposition or business model often takes more than two years. These kind of short-term leadership roles encourage managers to simply continue to optimize the existing model rather than pushing the company to adapt for the future. Allocating Talent and TreasureAnother key challenge for an organization seeking to adapt is the need to allocate the necessary human and financial resources away from existing areas of business and into new, unproven ventures. New managers with appropriate skills and authority are often the driving force behind new strategic direction. At the New York Times Company, adapting the value proposition of its business for both readers and advertisers required organizational changes as well. The company hired Alexandra MacCallum, founding editor of the digital Huffington Post, to lead a unit focused on audience development in an age of social media. Chris Wiggins was named chief enterprise data management scientist and assigned to help guide a burgeoning engineering division. Its job was to harness data and analytics to help inform decisions by both editors and publishers on the Times’ content, distribution, audience, and new advertising products. Often, adapting a business’s value proposition requires changing the lines of reporting of existing employees. When Facebook began its strategic shift to focus on the best mobile experience for users and advertisers, it had to redesign the organization chart for the company’s engineering teams. In the old organization, the desktop team led the development of each new feature, and separate teams handling mobile apps for iOS and Android were left to play catch-up. To support the new strategy, all engineers were reassigned to teams focused on a single Facebook feature (photo albums, group messages, upcoming events, etc.) so they could build it for both mobile and desktop from the very beginning. Financial resources must also be allocated carefully to support the evolution to new value propositions. This often requires leveraging revenue or assets from existing units to finance the launch of new ones. During Williams’s strategic transition, the firm was simultaneously taking money out of its existing pinball machine business and launching its first casino games. Marvel Comics had to leverage its prized rights to its comic book characters as collateral to secure funding for its move into film producing. This kind of transition is critical. McGrath describes this as a process of “continuous reconfiguration” of assets, people, and capabilities as businesses adapt from one transient advantage to another. Avoiding MyopiaPerhaps the biggest challenge to adapting the value proposition of an organization is that it requires looking beyond the conventional wisdom of its current business. Bold new opportunities (like selling music as digital files over the Internet rather than as physical products) can often provoke a response of “That’s not how we do things around here!” To paraphrase entrepreneur Aaron Levie, “Businesses evolve based on assumptions that eventually become outdated. This is every incumbent’s weakness and every startup’s opportunity.” Numerous psychological experiments have illustrated the power of confirmation bias. When faced with new information, we have a strong tendency to selectively notice facts that fit our preexisting theories of the world and to discount or filter out the ones that conflict. Think of the pinball machine industry. When computer games first arrived in arcades, pinball machine sales actually improved temporarily because the new games were bringing in more customers. It would have been easy for Williams to have concluded that video games posed no threat to its legacy business. Actually, that is what their competitors concluded; almost all of them vanished while Williams was making its pivot to casino gaming. Avoiding myopia requires a business to take the customer’s point of view rather than its own. This kind of customer-centric thinking is difficult, as an organization naturally focuses its energy and attention on its own processes, strategies, and immediate self-interest. If a company has been making encyclopedias for 200 years, it would be easy for it to focus on all the hard work that goes into making them and to wish customers would just pay for its new CD-ROM version rather than cultivating the perspective to see that its CD-ROM isn’t really the best solution for those customers. via Blogger http://ift.tt/2g74txl November 20, 2016 at 08:06PM
Enterprise data management
The role of data for businesses is changing dramatically today. Many companies that have used data as a specific part of their operations for years are now discovering a data revolution: data is coming from new sources, being applied to new problems, and becoming a key driver of innovation. http://ift.tt/2gfK0vkOne innovator is The Weather Company (TWC). This media company started in 1980 with a television channel, The Weather Channel. Since then, it has branched out into third-party publishing platforms, websites, and mobile apps, including the one I use every morning to decide whether to pack an umbrella. Like most media companies, TWC is in the business of making content that draws an audience and selling ads that are placed in that content. Data has always been part of that business model: every day vast quantities of weather data need to be captured, analyzed, and turned into the colorful charts, animated graphics, and reliable forecasts that keep audiences tuning in. But TWC has discovered that its data can be much more than just the raw material it uses to create programming for its viewers. The same data that the firm collects, manages, and analyzes constitutes a key strategic asset and, increasingly, a source of new innovation and value creation. I learned about this in detail from Vikram Somaya, who was the general manager of WeatherFX (later renamed WSI), a new TWC division focused on thinking differently about weather data. Somaya was an art history major in college and is fond of quoting Shakespeare, but at TWC, he led the teams of data scientists who analyze the company’s data to generate additional value for both business customers and end consumers. Weather has a powerful impact on a wide range of businesses. By one estimate, up to one-third of the U.S. economy is shaped by variations in weather.1 Walmart has said that local weather is one of the biggest factors in its predictive models for store sales. TWC’s data scientists work with major retailers to identify when they should predict a spike or slump in their sales so they can adjust their advertising spend (to commit more resources or to hold them back) as well as their merchandising. The company also works directly with brand advertisers in categories like allergy medication, fleece jackets, and snow tires to predict the best time for them to spend on ad placements. Even our snack food purchases on a given day (nacho chips or pretzels?) have been found to be shaped by whether the weather feels bright, sticky, or gloomy. With digital advertisements (inserted on websites or in apps like TWC’s own), brands now have the opportunity to adjust and target their message on the fly, choosing which image to show specific viewers based on the weather where they are standing. TWC is even using its data to create new products and services for industries like the insurance sector. For instance, it has built an app called Hailzone for insurers like State Farm and Travelers to offer their auto insurance customers. Whenever a hailstorm is about to hit, Hailzone sends out a text message alert to those customers, warning them to move their cars inside. That saves a tremendous headache for the drivers and costly hail damage bills for the insurer. The company is even collaborating with some of its most avid customers to grow and improve its data asset. Every day TWC crowdsources data from a community of 25,000 self-described “weather junkies” who pay to subscribe to a service called The Weather Underground. These avid hobbyists spend hundreds of dollars to buy their own weather-monitoring equipment, which they set up on their own property. Findings are shared and discussed among the network of fellow enthusiasts. With typical members uploading weather measurements at their own locations every 2.5 seconds, their input helps the company greatly improve the quality of its own data sets. TWC has evolved from a media company that simply produces data as part of running its core operations to a company that is treating data as a source of innovation, new revenue, and strategic advantage. Rethinking Enterprise Data ManagementThe third domain is data. Growing a business in the digital age requires changing some fundamental assumptions about data’s meaning and importance (see table 4.1). In the past, although data played a role in every business, it was mainly used for measuring and managing business processes and assisting in forecasting and long-term planning. Data was expensive to produce through structured research, surveys, and measurements. It was expensive to store in separate databases that mimicked silos of business operations. And it was used primarily to optimize existing operations.Today, the role and possibilities for data are seemingly limitless. Generating data is often the easiest part, with great quantities continuously created by sources outside the firm. The greater challenge is harnessing this data and turning it into useful insights. Traditional analytics based on spreadsheets have given way to big data, where unstructured information joins with powerful new computational tools. But for data to become a real source of value, businesses need to change the way they think about data. They need to treat it as a key strategic asset. Data as Intangible Asset
For many of the digital titans of today’s business world, it seems clear that the data they capture regarding their customers is one of their most valuable assets. Much of Facebook’s market capitalization is rooted in the value of the rich data it collects on users and in its ability to harness that data with innovative tools for advertisers, helping them understand and reach precisely the right audience. Data is valuable not just for companies like Google and Facebook. For any business today, data like intellectual property, patents, or a brand is a key intangible asset. The relative importance of that asset will vary somewhat based on the nature of the business (just as brands have greater importance to a fashion company than an industrial manufacturer). But data is an important asset to every business today and neglected at our peril. But other kinds of data can be valuable as well. In building its Maps service, Google has invested heavily for years in developing a best-in-class set of cartographic data. This includes sending camera-equipped cars around the world to measure out every road and capture its photographic Street View (more recently, it has sent cameras by camelback to map the deserts of Arabia). The company is constantly updating and “hand-cleaning” its data with teams of human data wranglers. It tracks up to 400 data points per road segment (the stretch of asphalt between two intersections). Depending on the pace of economic development, that road data needs to be updated with daunting regularity. On the other hand, we saw Apple’s failure to invest sufficiently in mapping data which led to a famous competitive fumble in 2012. As part of its ongoing rivalry with search giant Google, Apple chose to remove Google Maps as the default mapping app on all iPhones. Instead, it gave iPhone customers its own new Maps app, running on data Apple had purchased from various third parties. True to form, the Cupertino company had designed a stunning user interface for its app. But it had underestimated the quality of Google’s data asset. Millions of iPhone users who were forced to use the new maps flooded Apple with complaints. Cities were misspelled or erased, tourist attractions were misplaced, famous buildings disappeared, and roads literally vanished into thin air. The errors were so bad that they compelled the first letter of apology by an Apple CEO to customers. In it, Tim Cook went so far as to advise customers to download and use competitor apps from the App Store until Apple’s own maps improved.
Data is valuable not just for companies like Google and Facebook. For any business today, data like intellectual property, patents, or a brand is a key intangible asset. The relative importance of that asset will vary somewhat based on the nature of the business (just as brands have greater importance to a fashion company than an industrial manufacturer). But data is an important asset to every business today and neglected at our peril.
One of the most common ways that businesses can build an asset out of customer data is through loyalty programs. For years, retailers and airlines have offered loyalty miles, points, rewards, or a tenth sandwich free in hopes of increasing customer retention and total spending over time. But, today, much of the value of loyalty programs is in the accumulated customer data that they generate. When I sign up for your loyalty program, I am explicitly asking you to track my shopping behavior in order to earn rewards. That gives your business much more than an address for direct mail; your data about me grows over time to help you better understand my unique behaviors and interests as a customer. By designing new customer experiences with data in mind, companies can extend this model of providing customer benefits in return for customer data gained. Take Walt Disney Parks and Resorts and its new MagicBand wristbands. Promoted as a way to bring the convenience of smartphones in to the traditional theme park experience, these colorful rubber bracelets (outfitted with RFID tags) allow guests to enter the park, unlock their hotel room, purchase meals and merchandise, and skip the wait on up to three rides per day. The MagicBand is the heart of a $1 billion initiative to bring digital interactivity to Disney theme parks, and it aims to earn that money back by increasing the “share of wallet” that visitors spend at Disney. But it is also designed to provide Disney with previously inaccessible data on the behaviors of its guests: Where do they go when? Which rides are popular with which types of guests? Which foods might be better moved to different areas of the sprawling park? The MagicBands even allow guests to opt to be identifiable to Disney staff so that a child can be greeted by name by costumed characters or offered a birthday wish by a talking animatronic animal on a ride. These and other types of personalized service experiences will become available as Disney builds more data around its visitors on both the large scale and the individual level. The trick is in crafting the right experience so that, just as with a loyalty program, customers willingly exchange their data for added value from the business. You don’t have to be a company as large as Disney or Google to start building your data asset. Even small businesses can now use Web-based customer relationship management tools to keep track of who opened which e-mails, tailor follow-up messages, analyze which offers are the best fit for which customers, and more. As we will see in our discussion of big data, the shift to cloud computing is putting ever more powerful data management tools into the hands of small and mid-sized businesses. Every Business Needs a Data StrategyOnce you start to treat data as an asset, you need to develop a data strategy in your organization. That includes understanding what data you need as well as how you will apply it.An explicit data strategy may seem obvious in industries like financial services and telecommunications, which are accustomed to copious amounts of customer data. But smaller firms and those in less data-rich industries must also develop forward-looking strategies for their data. The following five principles should guide any organization in developing its data strategy. Gather diverse data types: Every business should look at its data asset holistically and include diverse types of data that serve different purposes (see table 4.2). Business process data such as data on your supply chain, internal billing, and human resources management is used to manage and optimize business operations, reduce risk, and comply with reporting requirements. Product or service data is data that is essential to the core value of your products or services. Examples include weather data for TWC, cartographic data for Google Maps, and the kind of business data that Bloomberg provides to business customers. Customer data ranges widely from transaction data, to customer surveys, to reviews and comments in social media, to customer search behavior and browsing patterns on your website. Companies that do not sell directly to consumers (e.g., packaged goods companies) traditionally could gather customer data only through market research. As we will see later, even these businesses are discovering new opportunities to piece together data to get a much clearer picture of their customers than was possible before.
Use data as a predictive layer in decision making: The worst thing that companies can do with data is gather it and not apply it when making decisions. You need to plan how your organization will utilize its data to make better-informed decisions in all aspects of its business. Operations data can be used in statistical modeling to plan for and optimize the use of your resources. Customer data can be used to predict which changes in your services or communications may yield improved results. With detailed data from its MagicBands, Disney can make better-informed decisions on which merchandise to feature near different rides and how to manage variable demand and foot traffic. Amazon uses your past browsing behavior to determine which products it should show you in your next visit.
Apply data to new product innovation: Data can power your existing products or services, but it can also be used as a springboard for imagining and testing new product innovations. TWC’s Hailzone mobile app is a perfect case of a company using its existing product data (for its TV shows and apps) to build a new service that added value for multiple customers (insurance companies and their insureds). It helped that TWC was able to step outside its normal perspective as a media company and think about different business models based on things like utility and risk management rather than just viewer eyeballs and advertising. Netflix uses its vast amounts of data on viewer preferences for genres, actors, directors, and more to help it craft new television series like House of Cards. This practice lets Netflix circumvent the traditional network TV practice of investing in pilots for numerous new shows in hopes that one or more will pan out. That’s using data to innovate more quickly and cheaply. Watch what customers do, not what they say: Behavioral data is anything that directly measures actions of your customers. It can include things like transactions, online searches (a powerful measure of your customers’ intentions), clickstream data (which pages they visited, where they clicked, and what they left in their shopping carts), and direct measures of engagement data (which articles in your newsletter they clicked to read). Behavioral data is always the best customer data it is much more valuable than reported opinions or anything customers tell a market researcher in a survey. That is not just because people lie in surveys but also because, as humans, we are extremely fallible at remembering our behavior, predicting our future actions, or considering our motivations. This is why Netflix shifted its recommendation system from customers’ own rankings to behavioral data as soon as it moved customers from DVDs to streaming video, which made it possible to measure what we actually watch rather than the unopened red envelopes on our dresser. Netflix knows that there are big differences between the movies that we give a five-star ranking and those that we actually wind up watching while doing the dishes on a Wednesday night. Combine data across silos: Traditionally, businesses have allowed their data to be generated and reside in separate divisions or departments. One of the most important aspects of data strategy is to look for ways to combine your previously separate sets of data and see how they relate to each other. A memorable example of the benefits of combining data sets comes from municipal government here in New York City. Scott Stringer, the city’s comptroller (CFO), was seeking to reduce the costs of lawsuits against the city. He launched an initiative to compare the data on lawsuits and damages paid with other city data sets, including the budgets of different departments over time. A surprising correlation was discovered: after the city’s parks budget had been slashed a few years earlier and its seasonal tree pruning reduced, legal claims from citizens injured by falling tree limbs skyrocketed. The cost to the city from a single lawsuit was greater than the entire tree pruning budget for three years! Once this was discovered and the budget funding was restored, lawsuits dropped dramatically. As your business environment becomes increasingly complex, your ability to find, combine, and learn from diverse sources of data will become more important than ever. In putting together a data strategy, it is also important to understand that many of today’s data sets are very different from the spreadsheets and relational databases that drove the best practices of data-intensive industries in the pre-digital era. The entire nature of available data, and how it can be applied and used by business, has undergone a revolution in recent years. That revolution is commonly termed big data. The Impact of Big DataThe term big data first appeared in the mid-1990s, introduced in tech circles by John Mashey, chief scientist of Silicon Graphics, around the time of the birth of the World Wide Web.5 But the phrase entered the broader business conversation around 2010 as businesses of all kinds began to grapple with the vast supply of data generated by digital technologies. At first, the term seemed a bit faddish, a marketing ploy used by data storage firms to get IT departments to increase their spending on data servers. But the real changes at work have been much more profound than the size of hard drives or server farms.Make no mistake: the size of data sets is increasing rapidly. Every graph representing the amount of digital data stored worldwide each year shows the skyward leap of an exponential curve. These curves all recede exponentially into the past as well. The sheer amount of recorded data, in other words, has been growing for a long time likely since the origin of computers, maybe since the origin of writing. So what is new about big data if not the rapidly growing “bigness” of it? The phenomenon of big data is best understood in terms of two interrelated trends: the rapid growth of new types of unstructured data and the rapid development of new capabilities for managing and making sense of this kind of data for the first time. The impact of these two is shaped by a third trend: the rise of cloud computing infrastructure, which makes the potential of big data increasingly accessible to more and more businesses. Big Data Is Really Unstructured DataTraditionally, a firm’s data processes were based on analyzing structured data the kind of data sets that fill a database with neatly organized rows and columns (e.g., with addresses of customers, inventories of products, or expenses and debits of various financial accounts). But the big-data era has been marked by the profusion of new types of unstructured data information that is recorded but doesn’t fit easily into neat forms. A business may have access to the ungrammatical text posts of social media, the flood of smartphone-generated images, real-time mapping and location signals, or the data from sensors rapidly spreading over our bodies and our entire world; all these types of data are rich in meaning but difficult to parse by familiar tools like spreadsheets. One of the biggest sources of unstructured data is social media. As over a billion users worldwide participate in networks like Facebook, Twitter, and Weibo, they are constantly producing vast amounts of data in the form of their posts, comments, and updates. This social data is attitudinal (what people are saying can capture their opinions, likes, and dislikes) and can be used to measure affinity (whom they friend, follow, or link to reflects social ties and allows businesses to infer relationships between them and others in their network). And this data is real-time and continuous, allowing businesses to analyze shifts in opinion, sentiment, and conversation with precise longitudinal detail. Because of this, numerous organizations have sought to gain insight from the analysis of social data. Brands monitor their reputation over time based on what customers are saying, the Centers for Disease Control uses social media to help track the spread of flu and influenza, Hollywood predicts the opening weekend performance of new movies based on the social “chatter” after opening night, and economists have even used social media to effectively predict stock market performance. Another new kind of unstructured data is location data. The data being generated by mobile devices like smartphones comes with geolocation markers, which provide a continuous record of where we are and where we’re going in real time. The inclusion of location data with other kinds of behavioral data adds tremendous additional context. Increasingly, search engine results are shaped not just by the words we are using in our search but also by where we are when we search. (If we Google the word pizza, we are likely to be shown the closest establishments, with links to their phone numbers and addresses, instead of pizza history or recipes.) Research by my colleague Miklos Sarvary has shown that the patterns of where we go at various times of the week (as measured by our phones) reveal a great deal about who we are. By analyzing these “co-location” patterns, Sarvary and his coauthors were able to show that customers with similar location “footprints” were likely to buy similar products and could be effectively targeted for marketing based on that data alone. The biggest emerging source of unstructured data is the sensors that are becoming embedded in everything around us as we shift to a world of truly ubiquitous networks. By 2020, Cisco expects that over 50 billion devices will be connected and sharing information over the Internet and the vast majority of these devices will not be computers, smartphones, or Web servers. This phenomenon, known as the Internet of Things, encompasses smart automobiles, factories and product supply chains, and lightbulbs and home appliances as well as sensors embedded in the watches and clothing we wear and in the medicines we ingest. Together, all of these applications will soon result in billions of devices transmitting and generating new sets of data that can be put to business use. For example, GE has installed sensors on its jet engines that allow the engines to continuously post updates on their status and operating details. (GE calls the system “Facebook for jet engines.”) This real-time data lets airline mechanics monitor the status of critical aircraft equipment so they can make repairs when they actually are needed rather than on a schedule of estimated need. This makes fleet maintenance more efficient and makes air travel cheaper and more convenient. New Tools to Wrestle Unstructured DataThe second trend shaping big data is the rise of new technological capabilities for handling and making sense of all this unstructured data. If not for this, big data would be simply a giant haystack in which the needle of business insight might well be invisible. Fortunately, a range of technological developments is expanding our abilities to use the unstructured data that technology is producing.The continuing exponential growth of computer processing power is a big factor in our improved ability to use data. Moore’s law, coined by Intel cofounder Gordon Moore in 1965, predicts a doubling in the performance of computer chips roughly every eighteen months as transistors become faster and smaller. For fifty years, the prediction has held, and the results have transformed the world. ENIAC, the first modern computer, was built in 1946 and filled a room the size of a small gymnasium. But by 1983, when I first studied computing, my student-grade Texas Instruments pocket calculator had more processing power than ENIAC. Moore’s law tells us that this decade’s supercomputer is the next decade’s pocket device. Recent technologies have further enabled data processing on a large scale with acceptable costs. In-memory computing can accelerate analytics to the kind of real-time computing that allows digital advertising to select the ad seen by each visitor to a webpage, based on the weather where they are, the sites they have visited recently, or any other critical determinants that can be mined through data. Hadoop is an open-source software framework that enables distributed parallel processing of huge amounts of data across multiple servers in different locations. With Hadoop, even the biggest data sets can be managed affordably. Other tools focus less on increasing power and more on making sense out of the chaos of unstructured data. New data-mining tools allow programs to sift through the raw stuff of social media and pick out patterns that human managers then can examine to recognize trends and key words. Perhaps the biggest advances in managing unstructured data have come from new developments in “cognitive” computing. Natural language processing, for example, can interpret normal human language, whether from spoken commands, social media conversations, or books or articles, without adaptation. It is critical to the development of systems that can identify patterns in big-data sets of human language, such as recordings of customer phone calls to call centers. Another key development is machine learning resulting in computing systems that can recognize patterns and improve their own capability over time, based on experience and feedback. As computers are modeled around neural networks, they go beyond just spotting patterns in unstructured data: they receive feedback from their environment or human trainers (indicating which conclusions were wrong and which were correct) and reprogram themselves over time. Natural language processing and machine learning are combined in a system like IBM’s Watson, which can read vast amounts of written language and develop ever more accurate inferences by using feedback and coaching from human experts. Watson famously debuted on the world stage by playing the quiz show Jeopardy! where it bested the top human champions by combining encyclopedic recall with a human-like ability to have educated “hunches” (e.g., estimating that its best guess to a question had a 42 percent likelihood of being correct). Since then, Watson has moved to the real world. Physicians have trained Watson, using a library of millions of patient case histories, to the point where Watson is more accurate than many doctors in making an initial diagnosis of a new cancer patient. Watson and similar technologies will be at the forefront of the next wave of big-data analytics informing everything from customer service, to fraud detection, to advertising media planning. Big Data on Tap from the CloudAn additional trend is shaping the impact of big data: a revolution in the storage and accessibility of both data and data processing. In the old data paradigm, for a business to manage data, it needed to invest in owned infrastructure to collect and hold all of the data as well as any tools to analyze it. This significant capital requirement led to disparities among companies, with many unable to afford the sophisticated use of data. Today, businesses no longer need to store their own data, and even small businesses are increasingly able to access the leading tools for using unstructured data. The reason is the rise of cloud computing.Think of voice-recognition systems like Siri or Google Now on our smartphones. There is a reason Siri doesn’t work when our iPhones are offline: the computations required to understand spoken language and respond to it are too intensive to be managed with the processors on a current smartphone. Yet Siri works perfectly fine when able to access the cloud. All our device needs is a steady connection so that it can send our voice to a remote server with all the power necessary to process that unstructured data and respond in real time. Increasingly, more and more computing applications and services are delivered seamlessly over the Internet, with the real processing power residing in the cloud rather than on our devices and computers. Amazon Web Services (the company’s huge B2B computer services division), Microsoft, Google, and others are all driving a shift to a computing environment where businesses increasingly meet their needs through subscription and SaaS offerings rather than by buying and installing the most powerful computers on their own premises. Cloud computing has profound implications for scalability and small business. Services like Watson are available “on tap” to businesses, just like cloud-based storage and customer databases are for small businesses. This means that big data is not the exclusive terrain of the world-class companies with huge IT departments. Any business can tap into best-in-class analytics tools today from cloud providers like SAP and IBM paying only for the data and the processing it uses. Big data doesn’t have to have a big price tag. Where to Find the Data You NeedAs you begin to put together a data strategy, you will start with the data you are generating in your own business processes. However, you will likely identify gaps in the data you need for some of your goals. Finding the right additional sources of data is critical to filling in gaps and building your data asset over time. Important sources of data from outside your organization include customer data exchanges, lead users, supply chain partners, public data sets, and purchase or exchange agreements.Customer Value Data Exchange Lead User ParticipationLead users (a term coined by Eric von Hippel) are your most active, avid, or involved customers. Their greater needs lead them to have greater interest in interacting with your products or business, and they can often be a unique and powerful source of data. We saw one example in The Weather Underground: the volunteer army of meteorological enthusiasts who happily contribute real-time feeds of additional weather data to TWC as part of participating in that community. Other companies use exclusivity to identify and leverage their lead users. Alexandre Choueiri, L’Oréal’s president of international designer collections, explained to me that the cosmetics firm creates and engages confidential customer communities for designer brands such as Viktor & Rolf. The allure of joining a special club (literally called the “secret service”) appeals to consumers, and the exclusivity helps the brand learn more about loyal users not just casual one-time purchasers. “You get fewer people,” Choueiri told me. “But they’re really engaged. We sell this brand through the retailers, so this engagement tool is how we get data.” By engaging lead users, brands can solicit input and feedback from much more selective and important communities.Supply Chain PartnersBusiness partners can be crucial sources of additional data for building your data asset. Companies producing consumer packaged goods now work closely with large retailers and with retail data services like Dunhumby. Power, leverage, and levels of trust can greatly influence who shares data with whom in many industries. In the travel industry, large airlines (such as Delta) can have nearly 100 million customers enrolled in their loyalty programs. But airlines and the online travel agencies (such as Travelocity or Orbitz) share only limited data. As a result, neither the agencies nor the airlines have access to the full picture of customers’ travel behaviors when they want to customize pricing and offers at the point of sale. Increasingly, data partnerships will be a key element of how businesses negotiate terms of working together.Public Data SetsAnother important source of new data is publicly accessible data sets. Some of these are in online public forums. The car reviews website Edmunds .com, for example, contains many years’ worth of discussion forums providing huge amounts of unstructured data in customers’ conversations about car models, makes, preferences, and experiences. Many social media platforms, like Twitter, are easily searchable for real-time data. In addition, governments are increasingly providing public access to large data sets in machine-readable format. The U.S. government’s census data, for example, has been in huge demand since being made available. In addition, more and more city governments are opening up APIs to let innovative businesses make use of government data and to spur new business opportunities.Purchase or Exchange AgreementsLastly, there are many opportunities for businesses to purchase or swap legitimate, valuable data with other firms. Businesses should avoid companies that offer shady sets of customer records collected through questionable means. Instead, firms should seek out the many reputable services that enable anonymized data comparisons. Anonymized data lets a company learn things like the conversion rate of offers (the portion of customers accepting the offer sent). The company’s data shows which customers got the offer, the retailer’s data shows who made a purchase, and the third-party service measures the conversion rate without revealing customer identities (which could be a violation of privacy terms). Sometimes data can be received through an exchange or donation. During the 2014 World Cup, Waze shared anonymous driver data with city governments in Brazil to help them identify and respond more quickly to traffic buildups and road hazards. In Rio de Janeiro alone, up to 110,000 drivers a day were providing traffic data through Waze’s API. Since then, Waze has been developing partnerships with other governments, such as the State of Florida. The company is not asking for payment but rather is seeking an exchange of more data. By receiving real-time data from highway sensors and information on construction projects and city events, Waze is improving its own data asset. There are many more sources of data available today. The challenge for your business is often simply choosing which ones will best fit your needs. A recent forecast published by the Journal of Advertising Research summarized the changes anticipated in market research: as businesses are faced with a “river” of continuously generated data, the goal of research is not to expensively manufacture data, but to find the right tools to “fish” in that river in order to draw forth the insights and intelligence needed. Turning Customer Data into Business Value: Four TemplatesAs organizations gather more data and develop it into powerful assets, the next challenge is to continuously apply these assets to create new value for themselves.We’ve seen examples of how product or service data provides value by enabling a business’s core service to customers: think of TWC’s use of weather data and Google’s use of mapping data. We’ve also seen that business process data can yield value by optimizing and improving decision making, even in surprising ways like Stringer’s use of budgetary data. If we look at customer data, we can find recurring patterns of best practices used to add value across differing industries and organizations. We can think of these practices as four templates for creating value from customer data: insights: revealing the invisible; targeting: narrowing the field; personalization: tailoring to fit; and context: providing a reference frame. Let’s take a look at each of these four data value templates and see how they are applied in different industries to create new value. Insights: Revealing the InvisibleThe first template for value creation is insights. By revealing previously invisible relationships, patterns, and influences, customer data can provide immense value to businesses. Data can provide insights into customer psychology (How are my brands or products perceived in the marketplace? What motivates and influences customer decisions? Can I predict and measure customer word of mouth?). Data can reveal patterns in customer behavior (How are buying habits shifting? How are customers using my product? Where is fraud or abuse taking place?). Data can also be used to measure the impact of specific actions on customers’ psychology and behavior (What is the result of my change in messaging, marketing spending, product mix, or distribution channels?).Today, many businesses have access to large quantities of customer data in the form of online conversations about their products and brands. A good example is automobile manufacturers. My colleague Oded Netzer of Columbia Business School, along with three research coauthors, has dug into the data created by discussion forums to explore what it reveals about the automotive market structure and consumer behavior. Netzer’s team applied a variety of text-mining tools algorithms that are trained on human language and apply formulas to detect patterns in huge quantities of unstructured text from online conversations. One area of their research looked at how customers perceive brands. By examining patterns of statistical “lift,” they could identify which specific attributes are more frequently associated with one auto brand versus its closest competitors. The patterns revealed opportunities in terms of audiences to target, content for messaging, and ideas for product development. Netzer’s team also used the data to investigate the impact of long-term advertising efforts. They focused on a period when Cadillac had spent millions on brand advertising to shift customers’ perception of Cadillac from “classic American car” (like Lincoln) to “luxury brand” (like Lexus and Mercedes). A textual analysis of the conversations over several years showed that, consistent with the campaign objective, the Cadillac brand was gradually moving in customers’ associative perceptions from the first group (classic American brands) to the second (luxury brands). When the researchers compared this with public data on dealer trade-ins, they confirmed that the shift in perception was also a leading indicator of purchase behaviors. Rather than trading between Lincolns and Cadillacs, more and more customers were exchanging their luxury cars for Cadillacs. In another case, Gaylord Hotels used insights from customer data to sharpen its referral strategy. The business has a few large hotel properties that are well suited for major events as well as personal stays. With a limited advertising budget, it knew that referrals (word of mouth from happy guests) were the biggest source of new customers. So management set a priority to increase that word of mouth by improving the already good guest experience. The first step was an internal review of operations that identified eighty areas of focus that might help inspire customers not only to be pleased but also to actually mention Gaylord to others. The obvious next challenge was prioritization: Which items on this long list were most important? To help, the company undertook an analysis of social media data, looking at every instance where the hotel’s name was mentioned by customers in public platforms like Twitter. Customer recommendations and praise were examined for any clues as to what had spurred them and at what point in the customer’s stay. The results were illuminating. A short list of just five elements of the guest experience seemed to have the greatest influence in sparking word of mouth, and all of them took place in the first twenty minutes after arrival. Targeting: Narrowing the FieldThe second template for data value creation is targeting. By narrowing the field of possible audiences and identifying who is most relevant to a business, customer data can help drive greater results from every interaction with customers. In the past, customers were often divided into a few broad segments for targeting based on factors like age, zip code, and product use. Today, advanced segmentation schemes can be based on much more diverse customer data and can produce dozens or even hundreds of micro-categories. How a customer is targeted can change in real time as well, as they are assigned to one segment or another based on behavioral data such as which e-mails they clicked on, rewards they redeemed, or content they shared. Ideally, customer lifetime value should be included as one metric for targeting customers based on their long-term value to the business.Custora is a data analytics company that helps e-commerce businesses determine the likely customer lifetime value (CLV) of their website visitors that is, not just their likelihood to buy in this visit but their likely profit potential in the future. This is done by analyzing historical customer data and applying both a CLV model and Bayesian probabilistic models. For example, when a new customer makes just one purchase on a website, Custora can predict that they are likely to make six purchases in the upcoming year, totaling $275 and placing them among the top 5 percent of the company’s customers. Other predictions based on historical data include the category the customer’s next purchase will likely come from (e.g., home furnishings vs. lawn care). The model can even provide warning signs such as predicting that if this customer doesn’t place an order for three consecutive months, the business can assume they have only a 10 percent chance of returning. InterContinental Hotels Group carefully uses data on the 71 million members of its Priority Club loyalty program to understand and target them more effectively. This data includes much more than zip code and hotel room preferences. Up to 4,000 different data attributes such as their income level, their preferred booking channel, their use of rewards points, and whether they tend to stay over weekends are used to assign each member to a customer group. This level of segmentation has allowed the hotel to shift from sending out a dozen varieties of an e-mail marketing message to sending out 1,552 different variations, targeted around past behaviors and special offers such as local events. These new marketing campaigns have generated a conversion rate (the portion of customers accepting the offer sent) that is 35 percent higher than that of less targeted campaigns the year before. Using data for targeting can even have a powerful impact in a field like nonprofit health care, thanks to a practice known as “hot spotting.” Dr. Jeffrey Brenner, a family physician in Camden, New Jersey, studied medical billing records from hospitals in his hometown and discovered that 1 percent of the town’s population was responsible for 30 percent of its health-care costs. “A small sliver of patients are responsible for much of the costs, but we really ignore them,” said Brenner.17 He used that data, and small grants from philanthropies, to start the Camden Coalition of Healthcare Providers and focus on “spotting” these patients and improving their care. Over three years, the organization was able to reduce emergency room visits by 40 percent among the initial group of the “worst of the worst” patients and to reduce that group’s hospital bills by 56 percent. Personalization: Tailoring to FitOnce businesses are targeting micro-segments of customers, the next opportunity is to treat them each differently, in ways that are most relevant and valuable to them. This is the third template for creating value: personalization. By tailoring their messaging, offers, pricing, services, and products to fit the needs of each customer, businesses can increase the value they deliver.Kimberly-Clark, which sells some of the biggest brands in diapers (among other personal care products), uses an audience management platform that integrates data from sales and media channels to build an integrated view of the “customer journey” of each customer. For the company’s business, that means tracking a family’s progression through various products from Huggies newborn, to full-size diapers, to transitional pull-ups during toilet training and “Little Swimmers” (for kids just starting out in the pool). Keeping track of each customer allows it to advertise the right product to the right family. British Airways has launched a service personalization program known internally as Know Me. Its goal is to bring together diverse data to create a “single customer view” that will help airline staff to make a more personal connection with each customer. Know Me started with a two-year project to link data from commercial, operational, and engineering systems and put it at the fingertips of customer service directors. But the program works only because the data analytics are linked to the judgment and “emotional intelligence” of the British Airways service staff. Know Me data is used to deepen staff awareness of fliers’ personal needs and preferences, and staff are empowered to make their own observations and record data that helps personalize future trips. This feedback loop helps the airline deliver more-relevant offers to each customer and provide personalized recognition and service during a trip. That can include recognizing a VIP business traveler even when traveling in coach class with family so that service staff can welcome and thank them and offer a glass of champagne. It could also mean providing discreet assurances to a customer who has previously indicated they have a fear of flying. With urgent updates entered in the system in minutes, one flight crew spotted a passenger’s iPad, forgotten on board, and passed word to the connecting flight crew to notify the passenger. One of the most popular service touches has been that of welcoming customers mid-journey when they have reached Silver Tier status, the first level that offers access to lounges. The airline has seen extremely positive response from customers, both one-on-one and in long-term tracking of their satisfaction and their likelihood of recommending British Airways to others. In addition, Know Me has allowed the airline to broaden its view of customers far beyond its loyalty-program members, with a goal of knowing the needs of all of its 50 million fliers. One challenge of personalization has been the proliferation of different devices and platforms where customers interact with a business. How does a business know it is communicating with the same individual on a phone, tablet, and PC, let alone through Facebook, its own shopping portal, or a display ad being served up by Google on pages all over the Internet? The good news is that this challenge is diminishing rapidly, allowing for “addressability” of the same customer across numerous platforms. As David Williams, CEO of database powerhouse Merkle, explained, we are quickly becoming able to communicate to individual consumers with “addressability at scale” across Google, Facebook, Amazon, and all the dominant platforms of the Web. Context: Providing a Reference FrameThe final template for data value creation is context. By providing a frame of reference and illustrating how one customer’s actions or outcomes stack up against those of a broader population context can create new value for businesses and customers alike.Putting data in context is at the heart of the “quantified self” movement evidenced by customers’ rising interest in measuring their diet, exercise, heart rates, sleep patterns, and other biological markers. Nike was one of the first companies to tap into this trend with its Nike+ platform, which originally used in-shoe sensors, then the Nike Fuel wristband, and later mobile software apps. At each stage of its development, Nike+ has been designed to let customers capture their data and share it with their online communities. Nike customers who track their running data don’t just want to know how they did today; they also want to know how today’s performance compares to their own performance over the last week or month, to the goals they have set, and to the activity of friends in their social network. Context is king. Comparing their own data with the data of others can also add value by helping customers understand the probabilities of different outcomes. Naviance is a popular platform for U.S. high school students preparing for the college search and application process. One of its primary services is a tool that lets students upload their transcript data (test scores, class grades, high school attended) and compare it against a huge database of students who have applied to college while using Naviance. Based on the past results of similar applicants, the platform can show students their likely odds for admission to different colleges they are considering. Rather than applying in the dark (as we did in my day), students can use Naviance to find out which college on their list is a long shot, which one is a sure thing, and which schools fall in between. Sharing and comparing customer data can be a powerful way to identify hazards. BillGuard is a popular financial protection app that tracks its customers’ credit card statements and helps identify both fraudulent billing (e.g., if the card was one of 50 million hacked in the latest cyberscandal) and “grey” charges (hidden fees customers likely didn’t realize a company was charging them). BillGuard’s algorithms are effective precisely because they compare a customer’s bills against the anonymized bills of peers and against whatever charges were flagged as questionable by any other customers in its community. Other examples of businesses using data for context include Glassdoor, which lets job seekers compare their salaries with averages for others in their industry and role, and Pricing Engine, which helps small businesses improve their digital advertising spending (on platforms like Google AdWords) by comparing their own success rates with those of their peers. Tool: The Data Value GeneratorWe’ve looked now at the different types of data being used in business today. We’ve examined the sources where businesses can find more data to fill in their own gaps. And we’ve seen four templates for generating new value using customer data. Let’s look now at how to apply these concepts to generate new strategic options for data initiatives in your own organization. That is the focus of our next tool, the Data Value Generator.The tool follows a five-step process for generating new strategic ideas for data (see figure 4.1). Let’s look at each of the steps in detail. Step 1: Area of Impact and Key Performance IndicatorsThe first step is to define the area of your business you are seeking to impact or improve through a new data initiative. You might define it as a specific business unit (e.g., product line), a division (e.g., marketing), or a new venture. You might decide that you are looking to apply data to improve customer service at a resort, to develop better product recommendations, to improve outbound communications to existing customers, to improve the customer call center, or to develop a new app to drive customer engagement.Once you have defined the area of impact, you should identify your primary business objectives in that area. What goals are you hoping to support? In addition to broad goals, what are your established key performance indicators (KPIs) that are being used to measure performance? Because this is a data-driven project, you will want to think about highly measurable outcomes, those where you may be able to clearly measure impact. It is alright if you identify multiple objectives and KPIs at this step; you may end up seeking to influence one or more as you generate your strategic ideas. Step 2: Value Template SelectionNow that you know the domain you are focused on, look back at the four templates for value creation, and identify one or more that may be most relevant to your objectives:
Which template is most relevant to your business domain? To the KPIs you are focusing on? Which may affect those goals more indirectly? (For example, insights into customer brand perceptions could help influence a goal of market penetration if you can identify the right opportunity to reposition your product.) You could choose to pursue one template or a combination. Note that targeting and personalization often work together. Whereas targeting efforts are sometimes focused only on identifying the right audience, effective personalization requires that you have some system of targeted segmenting in place. You may already have one template or another more developed (e.g., you are strong on segmentation but weak on consumer insights). The question is, What area of value creation is the next focus for your data strategy? Step 3: Concept GenerationNow that you have selected a value template (or more than one), you will want to use it to ideate specific ways that data could deliver more value to your customers and your business.For example, if you select context, how can you best use contextual information to influence desired behaviors? Behavioral economics has revealed that seeing our data in context can be an extremely powerful motivator. Voters are more likely to be persuaded to make it to the polls when reminded of their own past voting history and that of their neighbors. Using this insight, Opower has developed a data-driven service to influence home power consumption. The company, which works with local utilities, shows consumers data on how their own energy usage compares with that of their neighbors. The result: consumers are much more likely to reduce their energy consumption when shown comparative data. Concept generation should aim for this level of concrete application so you can really define the possible data strategy. For a personalization strategy, what are the specific moments of customer interaction that you are trying to personalize? For example, hotel and casino company Caesar’s Entertainment has pursued a strategy similar to that of British Airways using data for the personalization of service, starting from a loyalty program and aiming to increase repeat business. But Caesar’s focuses on a different set of moments. For example, Caesar’s can determine when a repeat visitor is having a bad night on the gambling floor and will send service staff to offer an unexpected gift a steak dinner, tickets to a show so the customer won’t leave feeling they had “bad luck” at Caesar’s and should try another casino. At the concept generation stage, you want to produce specific ideas for putting the data to work in your business. Step 4: Data AuditNow that you have a strategy in mind, you need to assemble the data that it will require. That starts with surveying what data you already have that could be used to enable or power your strategy. You may have a large, established data set based on your core product or service (like TWC). You may be starting with a data set on website visitors, or you may have access to loyalty-program data. For some businesses, the only data may be an incomplete list of customer e-mail addresses. Next you should identify what data you still need. For the purpose of the strategy you have sketched out, what data is still lacking? What will it take to provide the full view of the customer needed by your new initiative? You may need to increase your data in terms of
Lastly, now that you’ve identified the gaps, you need to determine ways to fill them. This is where you can apply the options discussed earlier: customer value exchange, lead users, supply chain partners, public data sets, and purchase or exchange agreements. Step 5: Execution PlanFor your data strategy to be effective, you must do more than assemble the right bits of data (the zeroes and ones). You must put that strategy to use in the work of your organization. The last step is to plan for the execution of the key pieces of your data plan. What technical issues need to be worked out? This may include data warehousing, latency, or how quickly the data needs to be updated. Your IT people will need to weigh in here. What business processes will need to change? Most data initiatives assume employees of your firm will make different decisions and take different actions based on your data. You will need to identify those changes in advance of rolling out any technical solution. How can you test out your strategy and build internal support? One of the best ways is to integrate the new data strategy into an existing initiative at your company. Jo Boswell, the program lead for Know Me at British Airways, knew that it would be difficult to enlist in-flight service staff if her initiative was seen as one more competing priority in their work. Instead, she integrated Know Me with their existing customer service program, showing how its data would help staff to deliver on the same four “customer service hallmarks” that anchored all their training.22 Data-driven strategies should be in line with everything your business is doing and help people to do their jobs better. Organizational Challenges of DataWhen Mike Weaver was brought in as director of data strategy for the Coca-Cola Company, his mission was clear. “We must understand consumers’ passions, preferences, and behaviors so we can market to them as individuals,” he told me. As an expert in the area of applied analytics, Weaver saw that this required building a data asset in an industry that is not traditionally rich in consumer data. By combining its MyCokeRewards loyalty program with a variety of other data sets observed behaviors on its websites, social log-ins via Facebook, cookie stitching, and data from various partners the company was able to advance rapidly toward its goal of becoming a more data-driven marketer. But the biggest challenges, Weaver told me, were organizational, not technical. He compared the process of shifting business practices at “the world’s greatest brand/mass media company” to turning an aircraft carrier at sea. He knew that the right data models could be used to develop advanced segmentation schemes for Coca-Cola’s customers, to understand customers’ different needs and wants, and to allow the firm to better serve and communicate with them. But before installing all the data centers and analytics models that would allow for real-time targeting of customers, the company first had to plan out the changes to its business processes. Before a brand can take advantage of its ability to differentiate customer segments in real time and deliver targeted messaging to them, it first needs to learn how to create messages in a very different way. This kind of targeting doesn’t require Coke to create a single, blockbuster Super Bowl ad; rather, it has to create dozens of versions of the same message and test them to see which ones drive response among different customer segments. The first step of the journey, Weaver reiterated, is to plan the changes in your business process before you start buying all the latest hardware or cloud services. In my speaking, and work with a wide range of companies, I’ve observed a number of common organizational challenges that businesses face as they shift to a more data-driven strategy. Each of them is worth considering when developing a data strategy. Embedding Data Skill SetsThe first challenge in the transition to a more data-driven organization is finding people with the right skill sets. This starts with data scientists the folks who can do the technical work of data analysis, be it hand-cleaning the raw data, programming algorithms to apply real-time data in an automated fashion, or designing and running rigorous data experiments. Depending on the organization, it may be using an outside partner for analytics, hiring a single analyst, or building an entire team. Good data scientists have strong statistical and programming skills and often come from an academic or scientific background. They also serve as truth-tellers within the organization. These are the folks who know that data can lie very easily, and they will keep a company honest about things like sample size, significance testing, and data quality (the old “garbage in/garbage out” rule). But the data experts cannot be the only people in an organization who understand or think about data. In order to truly build data into a strategic asset, everyone in the business has to adopt a mindset that includes using data, and the questions they pose to it, as a part of their daily process. Part of this is educating the workforce about the ways data can be applied in their business. Another part must be developing a company culture that embraces data and analytical thinking. For a consumer goods company like Coke or Frito-Lay, that involves a shift from thinking about marketing as an art to thinking about it as a discipline that includes both art and science. Lastly, the company may need someone who can bridge two worlds: the world of quantitative analysts and that of business decision makers. This person will be the one who can connect the work of data science with that of the senior managers or the creative types in the marketing department. Think of Somaya, the former art history major who learned to speak the language of both the data scientists at TWC and the advertisers and brand managers who were his clients. Bridging SilosSometimes the biggest challenges to sharing data are within the organization. At Coca-Cola, Weaver found that website analytics data was sitting in one database while data on consumer purchase behavior from loyalty programs was being kept somewhere else entirely. In order to create a complete picture of the customer, he first had to bring all the data together in a unified way. In many organizations, these divisions are reinforced by departmental silos and each department’s desire for “ownership” of its data (sales data vs. marketing data, etc.). In a research study that I coauthored with my colleague Don Sexton, we spoke with hundreds of senior marketers at businesses across a wide range of B2B and B2C industries. The most commonly cited obstacle to using data effectively was internal sharing, with 51 percent of respondents reporting that “the lack of sharing data across our organization is an obstacle to measuring the ROI value matrix of our marketing.” In large organizations operating in different locations, another important question is whether or not to centralize data analytics. This is partly a matter of where the data is warehoused but also where the data scientists are. Should each business unit have its own analytics team so it is closer to local decision making? Or should one central analytics unit service the key data needs of every part of the business? As large organizations mature in terms of their data capability, they seem to be centralizing analytics while striving to raise the data savvy of managers in each business unit. Sharing Data With PartnersData sharing is critical not only within an organization; it is becoming a key element of negotiations with business partners. Contracts and deals of all kinds are no longer just about who pays what to whom but what data will be shared as well. This sharing is particularly important for businesses that don’t own the ultimate point of sale for their products. Industrial equipment manufacturer Caterpillar now requires its 189 dealers to enter into data-sharing agreements; in return, it provides them with benchmarks and tools to improve their own sales efficiency and with customer leads generated from Caterpillar’s Web analytics. Ann Mukherjee, chief marketing officer of Frito-Lay, is able to measure the impact of all kinds of innovative digital marketing for popular brands like Doritos and Lay’s, but this measurement is possible only due to partnerships with key retailers. “Retailers are unbelievable sources of analytical understanding,” and the ability to partner with them around data and measurement is critical to building store traffic and product sales. As data becomes more essential to business strategy, data sharing will become a key element of every important business partnership with suppliers, distributors, media channels, and more. via Blogger http://ift.tt/2fTPmZ5 November 16, 2016 at 01:09PM
Building block of ROI Value Matrix
No matter what product or service you sell, a well-constructed ROI model based on an ROI Value Matrix built solidly from your customer’s perspective is a tremendous tool for training your sales team, understanding your market, and qualifying the value you deliver to your customers. Creating why buy statements is the first step in building an ROI Value Matrix.
Why buy statements are phrases used to describe the emotional reasons people or companies buy products and services like yours. The why buy statement is a personalized expression that you craft strictly from your customer’s or client’s perspective. Your objective in writing these statements is to capture every reason someone would buy a product or service like yours and then gather these reasons into a coherent list. These statements fill the first column of the ROI Value Matrix and are the foundation on which the matrix data is built. When we conduct ROI workshops, many participants discover that this phase of the process helps them pull together information on their product and assess its potential values in ways they never had before. And creating effective why buy statements has value beyond building the value matrix itself. By carefully crafting why buy statements for the value matrix, you will:
Each of these answers became the basis for the why buy statements Oracle used in its ROI Value Matrix. In this post, you learn how to create effective why buy statements that will capture all of the emotional reasons a prospective client might have for purchasing your products or services. You also learn how to use these statements as the foundation for the remainder of your ROI Value Matrix and as important tools for understanding your market and the many ways your products and services can meet the demands of that marketplace. Key Concepts and GuidelinesWhy buy statements are a critical component of the ROI Value Matrix and an important tool in understanding your company’s products, services, and customer base. Keep these key concepts and guidelines in mind as you craft your own why buy statements:
“Why would I buy your product or service?”
Understanding How to Create Powerful Why Buy StatementsIt is important to understand the people and companies that make up the market in which you’re selling. When listing all the reasons your customers buy products or services like yours, be sure to capture the issues your buyers face every day whether you have a solution for them or not. Always try to use easy-to-understand language when entering data into the Why Buy? column of the ROI Value Matrix table. For example, in Figure 2.1 you see sample data from a Why Buy workshop on a sales training program similar to Solution Selling. Let’s take a look at how each of these why buy statements was created and how well it fits with the fundamental concepts of and guidelines for creating these statements.Including Measurable GoalsThe first why buy statement in Figure 2.1, “I want to reduce our cost of sale,” is more than just an emotional reason for buying a product or service; the desired outcome is actually defined in the statement. “Cost of sale” is both measurable and quantifiable. Assuming reasonably good recordkeeping, you can evaluate the cost of sale for your fiscal or other reporting periods (e.g., this year versus last year). The difference between the two points in time provides hard data you can measure and evaluate for success.
The first why buy statement in Figure 2.1 is well written because it is direct and simple and contains a measurable outcome, but it is not necessarily typical of all why buy statements. Not all why buy statements you gather at the first stages of this process are going to include measurable and quantifiable goals, but that’s OK. As we noted earlier, at this point in the process you are concentrating on why people buy the type of products and services you sell, so don’t worry whether the statement includes a measurable outcome. Say it, capture the statement, and move on! As the process unfolds, each statement becomes measurable or will be removed from the matrix.
Limiting Each Statement to a Single Goal or IdeaThe next sample why buy statement in Figure 2.1, “I want to increase revenue per closed lead and reduce our cost of generating leads,” breaks one of our rules for building why buy statements by incorporating two reasons or goals (increase revenue and reduce cost) into a single statement. To craft the most effective why buy statements, you must break down your thought so that each statement contains just one, single reason or concept. Although the two goals in this example are related (and might be used together later in a Needs Analysis Questionnaire), it is important at this stage to deal with just one thought at a time. If you combine or mix goals or ideas within a single why buy statement, it becomes difficult later on to calculate specific costs and gains associated with a particular statement.Writing Clear, Concise, and Personalized StatementsThe last why buy sample statement in Figure 2.1, “Reduction in amount of time spent doing account debriefs,” is stated incorrectly. It is missing a strong personalization and defined audience for the goal. It might be better stated as, “I want to reduce the amount of time spent conducting account debriefs with my sales team,” which presents the statement much more directly from the stakeholder’s personal point of view. To be successful at building high-quality and objective ROI models, it is necessary for you to be the customer. You need to feel your customers’ pain and live their everyday experiences and frustrations. When you are using phrases like “I want . . .” and “I need . . .,” you are forcing the creation of objective and credible why buy statements that can be felt as an issue, problem, or goal by your prospect’s stakeholders.With the changes we made to the statements shown in Figure 2.1, our value matrix table now looks like the one shown in Figure 2.2. Improving Sample Why Buy StatementsLet’s look at another example of a why buy value matrix table, this time for talent acquisition and/or recruiting software. Try to correct the way the why buy statements are phrased in the examples shown in Figure 2.3.
The first why buy statement in Figure 2.3, “We want to improve the talent acquisition process for selecting candidates so it costs less to hire them,” is wordy and runs right into the perceived value or outcome the customer is seeking. There is no need, when creating why buy statements, to extend the phrase to include what the prospect expects as a return. Better stated, this example would read, “We want to reduce the cost of selecting and hiring candidates.”
The second example in Figure 2.3, “Eliminate the need to manage multiple job boards,” is an easy-to-measure why buy statement that only needs to be personalized. Simply add the phrase “We want to . . .” at the beginning of the sentence, making it “We want to eliminate the need to manage multiple job boards.”
The third example in Figure 2.3, “Web site is not current,” is stated in a way that is not personalized, doesn’t express a goal, and is too general because it does not refer to a particular section of the prospect’s Web site. Because this particular example is intended to refer to the career section of the prospect’s Web site, the why buy statement should make this intent clear. It is important that you understand a prospect’s issue, pain, or goal when creating why buy statements. By changing this statement to read “We need to keep the employment opportunity data on our Web site current,” you are stating a clear goal for the organization. In addition to referencing a goal, this why buy statement now expresses an implicit requirement for definition and measurement of what the word current actually means. The time frame expressed by the word current must be defined to be measurable.
As you build your value matrix and define the other elements of the ROI equation, many of the outcomes arising from your why buy statements are driven by time saving. It will become critical to define the time period for the assessment to prove its value. For example, if a job opening is filled but the career section of your Web site is not updated, the person managing the résumés will waste time (human capital cost) sorting through them to weed out those sent to apply for the filled position. The old adage, “Time is money,” is true when it comes to dealing with value estimation.
Finally, the last why buy statement in Figure 2.3, “We want access to more candidates,” needs to be a little more specific—clarity is critical when creating why buy statements. By making this statement more specific, it will be easier to define the goal’s starting and ending points for measurement. Also, this why buy statement as phrased could be hiding multiple needs. If we added a phrase to the statement—for example, “. . . [more candidates] from our help-wanted advertising program,” or “. . . [more candidates] from our college recruiting talent acquisition pool” we are then able to help the prospect measure the results from each program.Crafting the statement with this level of detail should help you better understand your prospect’s needs in each area of recruiting. Therefore, we suggested that our client create a separate line item in the value matrix for each of these why buy statements.
Figure 2.4 displays the corrected why buy statements from Figure 2.3.
Figures 2.5 and 2.6 illustrate additional why buy statement samples from other industries. You may want to reference these examples as you begin building your own why buy statements. Figure 2.5 includes why buy statements designed for advertising programs.
Figure 2.6 is from one of our many Rockwell Automation workshops; in this example, Rockwell’s sales force was creating why buy statements for the sale of extended warranties and ongoing maintenance contracts to their manufacturing customers.
via Blogger http://ift.tt/2fog6Tx November 13, 2016 at 07:59PM
Web analytics with social media
Currently, social media such as Facebook, Twitter and blogs are attracting attention in the web marketing arena. http://ift.tt/2fdU2tcTypes of Social MediaSocial media is bidirectional; analyzing PVs, the number of comments and the number of unique users is important as the information may spark new ad campaigns that were not possible before.Social media sites can be classified by the type of information they focus. A flow-type social medium provides real-time information and a stock-type medium provides stock of information and allows users to browse past data at any time. For example, Twitter belongs to the flow type and YouTube and summary sites belong to the stock type. Social media can also be classified by their communication methods. One method is based on human relationships and is called the social graph. Another method is based on interests and is called the interest graph. Social media can further be classified by the scope of exposure it gets, for example, open media types such as Twitter and closed media types such as Facebook and mixi where exchanges are kept between friends or chosen interests. Improving and renovating the brand imageOne of the affects you can expect from using social media is the improvement of your brand image. The brand image can be improved by communicating directly with customers regarding a current product or service, interacting with customers, and sharing the views of the company. You can also try to introduce a character or catchy tune or phrase that will get new customers interested in your product.Enhancing user loyaltySocial media can be used to enhance user loyalty for the brand or company.By asking each existing user for information on how they are utilizing products and by explaining how to use things to inexperienced users, existing customers satisfaction rate can be improved. Also, a secondary sales effect can be expected by making existing customers introduce the product to prospective customers. Ways of enhancing loyalty depends on the company, the service and the people in charge. There are various ways to enhance loyalty. For example, by talking about the product or service quality and details, or by making the people who own or work at the company more approachable with photos, comments or backstories. No matter which way is used, social media must lead to an action enhancing engagement with the user. To achieve this goal, bidirectional communication with users is necessary. SNS, Social Media WebsitesSocial media venues are excellent sources that companies can use to promote their company, their products and services, and interact with current and prospective customers. Utilizing social media is a trend that is increasing over time and, therefore, performing access Analysis on said social media websites is essential to success. Social media is very different than traditional media. One of the biggest differences is the way social media sites disseminate information. The idea can be visualized on social graphs: Social media is based upon the idea that a single user can be a source of information and that their network of connections are all included when they post the info they want to share. That single source can be a customer or a company and they have the ability to communicate with each other through posts. This second social graph shows the differences between traditional media sources that share information and the way that social media works. Why is it called social media? Well, because communication between entities is the reason for its existence. Social media allows friends to stay in touch, encourages collaboration with followers, and allows for business/customer engagement. Followers, engaged with properly, will become fans, fans will, in turn tell their friends. If you have engaged and active followers, you will also make sales. Be aware that social media can be a double-edged sword. On one hand, you can gain huge profits from followers and fans, on the other, the same followers might dislike something you do. If they dislike a product, service or even post an image of something that went wrong, you may lose a huge amount of money and loyalty. Social media analysisIn order to understand how successful your company is, analyze data you can get from social media websites and implement any necessary improvements. There are three main ways that companies use social media to interact with customers:
Some things to note: Actions posted to LINE are measured visually. When trying to count the valid number of friends you have on LINE: You will need to subtract the number of users you are blocking from the number of friends you have. Instagram does not provide an official analysis tool. Negative feedbackIf the number of followers decreases unexpectedly, try to figure out why so you can fix the problem quickly. Is it because of negative feedback?Engagement indexIs the content you are posting engaging followers? If not, modify your approach.The engagement rateThe engagement rate is calculated by dividing follower actions on a post by the total number of followers your profile has.Engagement Rate = Actions attached to posts (likes, comments, shares) / Followers The same equation can be used to find the Reach Engagement Rate. Data from Social MediaRecently, social media is being used by an ever increasing number of people. Just measuring inflow from social media sites is not sufficient, you need to also understand user behavior on social media sites in order to track how they ended up on your site. You should be careful however, because managing and measuring social media sites takes labor and time when posting and checking customer posts.Quantitative analysisQuantitative analysis measures values or figures. You can record the number of “Likes or retweets (RTs), the number of members, the number of subscribers, the number of followers and the number of footprints to measure effects.Note that when posting an article including a link, add a parameter to the URL. This allows you to capture which post worked. Qualitative analysisQualitative analysis measures the quality data which cannot be represented by figures. You can see the quality of responses to an article or post from the comments to it. Even if you see increases in the number of shares or comments in the quantitative analysis, it is not known whether you can get your desired responses. In some cases, responses may be completely negative. These cannot be measured as figures, but knowing the quality of responses to each posting is indispensable to enhance user interest. Some social media analysis programs automatically judge user comments to be positive or negative using text mining and other techniques. Using Social MediaListen closely: Social listeningSocial listening allows a company to gain extra feedback. This approach picks up tweets and posts that include product names or address particular needs and then reflects them on sales promotions and product development.Possible indices include: the number of postings including observed keywords, the number of postings including product names of the client company and competitors, as well as the negative or positive connotations of the words posted. Sending messages: Viral marketingViral marketing is a method used to gain sympathy from users via reviews. Note that information like sales promotions cannot gain interest from the users. Clarify the basic position you are taking and share that information clearly and concisely.Share information beneficial to users in order to increase sales and enhance feelings of trust. Some corporations use a “mascot account” (sometimes imaginary) to send information and enhance familiarity. Possible indices include the number of users who expressed the desire to know details of the company’s information (e.g., number of followers or friends), the number of views, responses (e.g., “Like!”), shares and retweets of posts and the number of users and views of shared and retweeted posts. Communication: Active supportThis method tries to actively talk with users who posted positive or negative comments even if the company is not required to answer to enhance feelings of trust. This is important in order to improve user satisfaction. Be careful that transmitting a unilateral opinion of the company or an adverse opinion against the misunderstandings of the users may cause flaming on the user side.Possible indices include the number of users communicated with, the number of communications, changes from negative to positive postures (whether or not it is due to conversations) and the types and numbers of questions from users per category. Social media advertisingAdvertising on social media sites is a good supplement to your other advertising efforts. You will be able to take advantage of users’ demographic information, target users with specific interests, and share promotional information with your followers. Budget accordingly, as most social media advertising is just as expensive as other types of advertising.via Blogger http://ift.tt/2fdTm77 November 02, 2016 at 07:21PM
Site traffic analytics
Referrers are one of the primary methods a website uses to track inflow. Referrer data shows you the main domains and specific pages that traffic to your website originates from. http://ift.tt/2fcAeGuInformation on referrers can help show the efficiency of your advertising strategy, how newsletters affect return traffic, and how you can improve your content with SEO. Senior Web Analytics Consultants build upon the knowledge they have and learn how to utilize referrers and advertisements to the benefit of their client companies. Reference source structuresWhen a user visits your site, the referrer information is recorded in a referrer log. Referrers include ads, search engines, social media, emails, newsletters, affiliate links, etc. Knowing which types of referrers are driving the most traffic to your website will help you identify the content that users find most interesting. You can also look at how you optimized the content with SEO to discover the keywords that are most effective. As one of your most important goals is attracting customers, you need to make sure that inflow is constantly increasing. One way to do this is to understand where your inflow is coming from and use that knowledge to improve your marketing strategies. Types of ReferrersAccess logs store referrer data; that is, information about a page that a visitor was viewing before accessing your website. There are many types of referrers including visit referrers, original referrers, search referrers, internal referrers and external referrers.We are going to be focusing on external referrers. External referrers are third-party sources through which a visitor is directed to your website. These sources include banners, portals, bookmarks, shops, emails, links and search engines. External referrers are roughly classified into the following five categories: Search enginesA search engine referral log includes data on the search phrase or directory used to generate a search.Social mediaA social media referral log includes data from sites like Facebook and Twitter.WebsitesA website traffic analysis referral log includes data on the page URL that sent traffic to your website.Direct traffic i.e. direct navigation or “no referrer” referralsDirect traffic is inflow to your website from URL’s that visitors have saved in their bookmarks or typed directly into their browser’s address bar.The relationship between advertisements and the referring sourceEach referring source has an advertisement that users follow to a landing page. Ads contained in newsletters and emails are considered non-referrer traffic, keyword ads and product listing ads are considered search traffic, and ads published on Facebook or other social media sites are social media traffic.The referring source can vary depending on ad placement and delivery method. Access analytics and ad effectiveness measurement tools can be used to measure the effectiveness of each ad published. Please note that the results from different tools and methods can vary, so it is a good idea to perform multiple Analysis. The User Processes FlowUnderstanding how a user accesses a site is one of the methods that can help a consultant improve their client’s advertising campaigns.There are three stages before a user visits a website: impression, engagement, and visiting. ImpressionAn impression is created when a user has visited a referrer, seen an ad, but has not yet followed the ad to the landing page linked to the ad. The number of impressions shows how many users are interested in the advertised products or services.Google Search Console can be used to gather an approximation of the number of impressions and the number of times a particular search phrase has been displayed. EngagementEngagement is often noticed on social media sites when users Like or Share posts or profiles. If users are engaged, their activity will increase and the interest in the business will increase, therefore leading to an improved chance of gaining conversions.Search phrases can also be used to measure engagement. If the number of times a business name or a brand word is searched for is high, you can assume that there are many users interested in the product or service being advertised. If users visit the website from a no-referrer source, you will need to analyze the pages that they visit after they arrive on the site and their user attributes. If they engage in the website, they are more likely to become return users. VisitingVisiting the website comes after impression and engagement.You need to make sure that the content of the user’s landing page matches the advertisement or link that they followed or they will bounce before looking at the content you are offering. Access analytics tools can record the activities of users after they arrive on a website. Attracting more customersIn this textbook, we will not be looking at any concrete methods of attracting customers. We do however suggest that you look at the number of impressions your advertisements are creating, improve the levels of engagement, and enhance your website’s content if you are not receiving the interest you were expecting.Query stringsThe query string i.e. UTM parameter is the text tag that is added to the end of a URL. For example, when looking at this address, http://xxx.com/?s=xyz, /?s=xyz is the query string.Request data can indicate the path a user used to reach a file or landing page you have stored on your web server. If you have a site where dynamic content is being generated, it also includes additional information on the content delivery path. Query strings are often used in association with page tags. Identification of the inflow from a query stringWhen your site receives a referral from Page A, the URL for Page A is the referrer URL. If you add a query string to the end of the referrer URL, you can identify the source and the URL that sent traffic to your website. Note that the first question mark is used as a separator and is not part of the query string. The following is an example, but there are others that have their own query string rules from various analysis tools.http://ift.tt/2eTiBNm Example 1 http://ift.tt/2fcAfKy Example 2, including a plurality of values Google Analytics allows you to read the entire URL and track the referrers. The URL builder allows you to add your campaign name, target media, promotional slogans, keywords and messages. Improving Traffic for Each Referral TypeThere are many methods you can use to increase inflow to your website. One of those methods includes analyzing referrer data.Press ReleasesNew information about a product or service is released as a “press release,” which may be introduced on media such as news websites, magazines or on TV to attract customers. There are press release distribution websites and services or you can hire a publicist. You can deliver press releases for free or at a low cost through mass media through these services. While a press release can attract customers at a low cost, you cannot control if it is actually published (that is determined by the media company).Note that the company cannot control how the contents of the press release are introduced to the public. Social BookmarkingA social bookmark is an online service, different from usual bookmarks registered to a user’s browser. A representative social bookmark in Japan is “Hatena Bookmark.” A social bookmark shows just how many users have bookmarked certain pages. A page bookmarked by many users means they have been attracting attention with highly popular content.Do not Forget About Offline Referrers!Posters, business cards and billboards when planning your marketing strategy. Offline promotions are just as important as promotions done online. For example, you can use free services to attract customers. These include banners, game applications and a program called “widget” that can be attached to blogs. It is also possible to form a contract with a medium website or portal site to publish articles or introduce the company’s products or services to the public.You can also register to a category on Yahoo! for a fee. Note that cost and labor are also required for means other than ads. You will need to use web analytics KPI dashboard to measure the ad effects and optimize them. Referrer sourcesReferrer sources give you a deeper look into how users are accessing your website. Analyzing referrer sources allows you to figure out which places inflow is generating from and can help you develop your marketing strategies.Mutual linking & Reciprocal LinkingMutual linking is where the content of each link has a mutual benefit for both sites. If your website sells lamps, you might have links to other sites that sell light bulbs or extension cords. You can even link to electricians. This information is beneficial to your visitors and to the other websites.Mutual links can improve SEO and attract customers, but you must be aware that hosting mutual links can negatively affect you position on search engines and lower your inflow rate. If the negative affects become obvious, advise your client to stop hosting irrelevant links and focus on their own website. On the other hand, reciprocal linking, or links your trade with other sites in order to build link popularity, tend to affect search engine rankings in a more positive manner. Most search engines still tend to count links as links regardless of the subject matter of the originating site. Just be aware that link relevance does affect your ranking and you may be penalized if a link you host is part of a linking scheme, hiding links or text, or cloaking. SNS and BBS sitesSocial media sites, SNS sites and BBS sites often act as referrers. Posts to these websites can be seen by followers. If you notice a sudden increase or decrease in inflow, check social media sites to see how your company is being portrayed. If the posts are positive, that is great, but if the posts are negative or malicious, you will need to take steps to refute the issue.Referrer spamReferrer spam i.e. log spam and referrer bombing contaminate Analysis. Spammers create a fake referrer URL and then begin making requests to the site they want to advertise. While the spam does not negatively affect the infected websites, it will taint their analysis statistics. Once referrer spam has been logged, it leaches data as a referrer source and links your website back to the spammer’s website. The spam links are also indexed by search engine spiders as they peruse infected access logs. The act of spamming servers and search engines is known as spamdexing.You can exclude malicious referrers from your Analysis by filtering them out. If you include them in your Analysis, they can influence your user rates and user activity results. Make sure to add a notation to your report if you choose to include them in your analysis. Situations when there is no referrerDirect traffic i.e. direct navigation or “no referrer” referralsEmailsWhen a user clicks a link in an email, they are taken directly to the website and no referrer data is stored in the logfiles as there is no previous URL data for your website to look at. This problem is becoming less common as more and more users are switching from email programs to viewing and storing emails and newsletters on the cloud.In this case the cloud refers to web mail servers like Gmail and Yahoo! Mail. As these websites have URL’s, any links clicked from an email viewed will show their URL as the referrer. Note that if an email client is used to receive the e-mail, the access is logged as direct traffic. BookmarksIf a user directly accesses a website from a stored bookmark, the access is logged as direct traffic.Address barsIf a user directly accesses a website by typing or copy/pasting a domain name into the browser’s address bar, the access is logged as direct traffic.More examplesIf a user is on their mobile phone, accesses a site from an organic search, transitions between SSL encrypted and non-encrypted pages, accesses a website from links within applications like Word, Excel or a PDF file, clicks a link from an RSS feed, or uses links embedded within Flash or JavaScript, the access may be logged as direct traffic.*In Google Analytics, when a user visits a campaign website once and then accesses a bookmarked website, the access is not regarded as direct traffic but as “inflow from the campaign.” It is shown as “(direct)/(none)” in Google analytics and “no link source” in analysis tools like Visionalist. Issues Found from Direct Traffic AccessWhen a newsletter works, direct traffic accesses increase. Since you cannot tell where the visitors came from when analyzing direct traffic access, you need to figure out how to get this information.General methods include attaching a parameter to the URL link in the newsletter and preparing a separate landing page. If you funnel users through this new page, you will be able to gather the data you need. Be aware that direct traffic access ratios are high and may cause issues. When this happens, ratios of accesses from search engines and referrers are naturally low. This means that accesses from channels which provide new visitors (prospects) with ways to access the website are limited. That is, the current customer attraction strategy is not sufficient and inflow from search engines and referrers (e.g., blogs) are insufficient. If you have a substantial amount of direct traffic accesses, thoroughly test your hypothesis with various possibilities and outcomes. NewsletterYou can also attract customers by newsletters. The newsletter is a method to distribute an e-mail to many subscribers at a time. A newsletter has two purposes:Acquiring prospective customersThere are a variety of methods you can employ to acquire prospective customers. Social media is a good way to let users know about targeted information and limited time campaigns. You can also use campaign sites to ask users to register their email addresses or fill out application forms.Supporting existing users and customersWhile acquiring new customers is important, keeping your current customers happy is vital. Return customers are valuable sources of income and brand loyalty. If your customers are happy they will tell their friends and that can create an influx of new customers as well.You can do many things to interact with current customers. Delivering newsletters, coupons and emails can keep your customers interested and communicating directly with them over social media sites can be a wonderful source of information. Points to note about newslettersNewsletters are a common way that business communicate with customers that have purchased products and services from them in the past.Transmission timeIf you are going to send out newsletters, analyze your site demographics and try to narrow down the best time of day to interact with your customers. Sending out a newsletter in the middle of the night or during work hours will not result in an influx of customers. Also make sure that the links you provide are mobile-friendly.Newsletter frequencyIf you send too many email or newsletters to your customers, they may decide you are spamming them and either unsubscribe or just delete your messages without reading them. Finding that balance is essential.Delivery settingsMake sure that your settings are all correct before sending out any messages. Always check to make sure that your distribution list is working properly and that your newsletters have been checked for content, spelling and grammar errors.Add query strings to any links in your newsletterAs newsletter links are logged as having no referrer, add a query string to the end of the URL link you send out.Using query strings to identify referrers in no referrer logsUsually, the referrer log shows you the referrer URL, but sometimes no referrer data is available. If this is the case, the user may have reached your site from a bookmark, their address bar (if they typed your URL in by hand), may be using privacy protection software, or the referring site blocked the referrer information from being sent.If you receive a high number of no-referrer users, user inflow from organic searches, or many internal referrals, you will need to figure out ways you can use query strings to supplement your request data. Knowing where your referrals are coming from is very important to PR and keeping track of social media reactions. Search Queries and ResultsOrganic search results are listings on a search engine results page that are shown to a user because of their relevance to the keywords input into the search bar. They are not the result of advertisements. In the following section, you will learn how to analyze an organic search. Access analysis and organic searchesGoogle Analytics allows you to analyze keywords, but there are very few access analysis tools that can analyze organic searches. Many of the main search engines are SSL, and access analysis tools have changed to reflect that fact. SSL sites do not allow analysis tools to access phrase searches and keywords.SimilarWeb is one of the only tools that can acquire information on phrase searches. Another is the Google Search Console, but that can only analyze phrase searches from websites visited prior to the user visiting Google. Search queries can help you discover frequently searched for keywords and any hidden intentions a user may have. It is commonly accepted that there are three different types of search queries:
What Is a Navigational Search Query?A navigational query is a search query entered into a search bar with the intent of finding a particular website or webpage. It is also called a search word. For example, a user might enter “Gmail” into their search bar to find the Gmail site instead of entering the URL into the navigation bar our using a bookmark. YouTube and Facebook are the most popular navigational queries to date.What Is an Informational Search Query?This type of query covers a broad topic, like recipes or weather, for which there may be hundreds of thousands of results. This type of query is made by a user looking for answers or directions.What Is a Transactional Search Query?A transactional search query is a query that indicates an intent to complete a transaction, such as making a purchase or requesting information. It is also called a phrase search. Transactional search queries may include product or brand names (like “Microsoft Surface Pro”) or be generic (like “warm winter gloves.”) They may also include terms like “order,” “purchase,” or “buy.” This type of search allows a user to search for an exact phrase or sentence rather than a keyword or set of keywords in random order.Differences in language processing in the search engine and access analysis toolWhile this problem does not happen when using English, you should be aware that other languages sometimes interact with access analysis tools in unexpected ways. When using languages that use characters instead of letters, the search engine will search for results by combining the intent of the word as well as its spelling. Access analysis tools process the characters as they are entered.For example, when searching for “Web” and “???” (read as web in Japanese characters), the search engine will output the processed results as if they are the same words; access analysis tools normally treat them as separate words. Google Analytics calls a search phrase a search query. You can view a report for search keywords with this program, however, many of the keywords used in organic searches are shown as “(not provided).” This is because Google adopted SSL and changed the specifications so that access analysis programs cannot acquire search keywords entered by users. There is not program available currently that can acquire this data. The occasional web master program can gather some rough data regarding accesses from organic searches and CTR, but it’s only a rough estimate. What is SEO?Search Engine Optimization, or SEO is the process of affecting how visible a website is in an organic search. The higher your site is ranked, the more visitors it will receive. SEO works with a search engine to optimize keywords, images, links, etc. that will allow the search engine spider to index their pages easily.There are both onsite optimization and off-site optimization that can be taken when working with SEO. Both help SEO increase user inflow from organic searches. It should be noted that access analysis is helpful in determining Keyword selection. Overview of onsite optimizationIf you want the target user to look at your website’s contents (On-page factor), you need to tell a search engine about your content, structure, and the intentions of each of your web pages. When you are coding your website, remember to:
Typical breadcrumbs look like this: Home page > Section page > Subsection page
Overview of off-site optimization
External measures encourage websites and social media sites to link to your website or post articles and content relating to your business (inbound link). They can be extremely useful, but beware of allowing the links to increase without monitoring them.
You can also use the Google Search Console Help: http://ift.tt/1Ew2Dhf to improve your SEO. If there is anything wrong with your website, the issues will be displayed for you to see. You will then be able to go in and modify anything that may be causing problems.
Search marketing and paid advertising
Search marketing i.e. search engine marketing is the process of using paid and unpaid advertisements to gain visibility and traffic on a search engine. Paying for advertisements can increase the exposure and effectiveness of your website.
Black hat, white hat, and grey hat SEO techniques
Black hat SEO is the method in which a webmaster attempts to improve their ranking in illegal or deceptive ways. They might use hidden text, invisible devs, or cloaking. Search engines that find a site using black hat methods may penalize them by reducing their ranking or by eliminating their listings altogether. Black hat methods can be discovered during manual reviews and by search engine algorithms.
White hat methods are considered to be safe. They comply with search engine guidelines and are generally regarded as sites that write content for users and spiders rather than attempting to trick the algorithm.
Grey hat SEO employs methods that are focused on improving search engine rankings but do not really produce great content for users or use methods that will result in a penalty.
SEO and organic searches
If your site has a high ranking on a search engine, it will have a higher inflow than lower ranked sites.
In order to make your site more attractive to users, make sure that your meta descriptions are both attractive and represent your website correctly.
Recently, Google performed two major updates to their algorithm, and implemented measures that ended up affecting websites in higher positions negatively. The Penguin update negated meaningless links ( link farm) while the Panda update penalized websites without useful content or with copied content.
There are companies that do not care about the penalties they may receive and end up spamdexing or utilizing black hat SEO.
If you want to keep up to date with SEO measures, you can check Google’s official blog. They update regularly.
Google Search ConsoleDifferences between Google Search Console and Google Analytics
Google Search Console is a tool you use to monitor and maintain your site’s presence in Google’s search results. It is a free service that can also help you understand how Google views your site and help with SEO.
Google Analytics is a tool used to analyze how users who visit a website behave.
As a web analytics consultant, you will need to be intimately familiar with these two programs as they will be cornerstones to your success.
Overview of Google Search Console
Here are some of the things you can do with Google Search Console:
Find out if there is a risk of the site getting penalties
A warning will be displayed if the registered site is facing any problems. This could include them discovering that you have used black hat SEO, your website has been displaying a number of 404 errors, a lack of content, your site is spamdexing, or any number of other issues. If you receive a warning, fix the problem as soon as possible.
Find out your content keywords
Under “Search Analytics” in Google Search Console, you can receive a report on the most frequently used and important keywords on your website. Since the search phrase information is excluded from the referrer, you cannot determine inflow keywords from search engines or from Google Analytics. Furthermore, it is also possible to show results by filtering users accessing from mobile devices.
Fetch as Google
Fetch as Google allows you to simulate how Google renders your website. Google crawls with a spider and then displays the page they indexed as a browser would. You can use this feature to detect differences between screen sizes and how various browsers render the page through Google.
Sitemaps
You can create and edit your XML sitemap (by specifying a list of the webpage of the site, an XML file which can tell the configuration of the site content in Google and other search engines). You will be able to see the differences between the number of submitted and indexed pages and you will be able to view any sitemap errors.
Cooperate with Google Analytics
Google Search Console can work with Google Analytics to check organic search keywords, rankings and frequency.
You can confirm the multi-device support and global support status of the website
If there are problems when the website is viewed through devices like smartphones, Google Search Console can tell you so that you can address the issue.
There are many other useful functions, so please add Google Search Console to your arsenal of web statistics analysis tools.
SEO measures using Google Search ConsoleXML Site-maps
Sitemaps are useful tools for correctly indexing your website. They also help search engines during the crawling process. A sitemap helps search engines understand your site structure, so make sure that you upload an XML site map to your website.
HTML Sitemaps
HTML sitemaps are text versions of the site menu or navigation panel. They allow users to directly link to pages attached to the anchor text displayed in a bulleted outline format. The sitemap can be used to locate content that is difficult or impossible to find on the site.
Confirmation of the search phrase word and target selection
Use search analytics to figure out display frequency and CTR display rankings.
The display frequency is also called impression, and it refers to the number of times users have searched for a search phrase on a search engine. CTR refers to the click through rate, the ratio of clients visiting owned media by clicking on displayed search phrases.
Let’s look at a few examples:
Perform a Mobile-Friendly test
Google has recently performed an update to their Mobile-Friendly Test. The test analyzes a URL to check to see if it has a mobile-friendly design. Having a mobile-friendly website will help your ranking. Note that a mobile-friendly site is not the same thing as a mobile site.
Keyword tools
There are a number of different free keyword tools available for you to utilize. These tools analyze phases and keyword trends, determine product posting times, and allow you to find keywords that will differentiate you from your competitors.
For example, Keyword Planner, a part of Google AdWords, can tell you the competitiveness and search volume of a keyword, as well as keyword candidates, just by entering a keyword and a URL.
If you enter the desired bids, you can find out click numbers, display numbers, costs, and the average display ranking as predicted over the course of a set period. If you set conversion tracking, you can also find out the estimated total conversion rate.
Note that while you need an account registered with Google AdWords, the Keyword Planner is available for free.
Google AdWords Keyword Planner
https://adwords.google .com/KeywordPlanner)
While Google AdWords is great for English websites, there are other keyword analysis programs for websites in other languages. Google JAPAN and Yahoo! JAPAN use a program called goodkeyword. It allows you to search keywords on search engines, Amazon, and Rakuten. Note that since goodkeyword is a free tool that uses external APIs provided by each website, it can be affected by specification modifications.
Goodkeyword:
http://goodkeyword.net/
Take advantage of tools that track trends
There are a number of different tools available that can help web analytics consultants track user trends online. Google Trends, found at http://ift.tt/n5mClp, is one of the most popular examples of a trend-tracking tool. These tools can analyze text written in a blog, examine the frequency of particular phrases, download trends, keyword trends, help you figure out how search trends change over time in specified geographical locations, etc.
It is important to remember that the results of an analysis with one of these tools is going to be presented in relative values. For example, the following figure shows a comparison between content marketing, net marketing, and web marketing trend data. The reader can see that while there has been a constant rise in content marketing over recent years, net marketing and web marketing actually decreased in popularity for a number of years before beginning to make a comeback.
Another type of trend tracking tool, this one used exclusively in Japan, is the Promotional Calendar created by Asahi Orikomi. It can be found at http://ift.tt/2eTgUzE. This promotional calendar can show weekly and monthly event trends, seasonal event trends, and unique user event trends.
This type of tool is useful because it organizes and tracks events in a manner that allows a company to understand how their events and promotions are received by consumers. Understanding trends enables a marketing department to quickly and efficiently improve their promotions, an IT department to enhance content on a website and for SEO, and web analytics consultants to discover what is web analytics and what users want so they can add that information into their marketing strategies.
Remember that in addition to presenting the data in relative values, these tools are also available to competitors and the data they can show you is not unique.
Off-page and Onpage Search Engine Optimization
Millions of searches are conducted each day on search engines like Google, Bing, and Yahoo. Being well placed on these search engines can increase your site inflow exponentially. Search rankings can be improved through off-page and onpage SEO efforts.
Off-page SEO can include: link building, social media profiles and posts, public relations work, etc.
Onpage SEO is useful for establishing site architecture and making a spider’s job easier. A well-organized website will allow for a user-friendly navigation system, help establish an information hierarchy, and establish a crawlable link structure.
Support tools
SEO support tools and ranking tools can report on both your website and your competitor’s websites. If you use them in conjunction with access analysis you will get a clear picture of your company’s presence on the web and how to increase visibility. Understanding how your site works and how it is ranked will allow you to improve your website and increase profit.
Analysis of Keyword and PLA Advertisements
Online paid search advertising is a marketing resource used by most advertisers. Knowing which keywords to bid on and the terms your prospective customers are searching for will allow you to create a budget, improve results, understand how everything works together to increase profit, and build new marketing campaigns that will reach larger audiences for the same amount of money.
Look at the following categories in Google Analytics when you are analyzing ads:Publishing
Keywords
Audience
User Behavior
Remember to set a max amount to your CPA and use the analysis results to improve your marketing campaign.
Traffic via PPC Ads
As with an organic search, having a higher ranking product listing ad leads to more impressions and clicks. If you want to attract more users from your PLAs, you need to create a strategy that aims to rank the ad higher.
The difference here from an organic search is that raising the cost per click to bid makes it more likely to rank the ad higher. It can be said that the PLA results can be seen sooner than with SEO.
Since a search ad, display ad, remarketing ad, etc. are all charged (per click), cost-effectiveness is important. Indices for cost-effectiveness include CPA and CPC (mentioned later).
The PLA addresses quality. The keyword quality score is generally said to be affected by the click rate. The higher the click rate, the higher the quality. To raise the click rate, review the title and description of the ad.
Quality can also be improved by reviewing the grouping of ads. Managing companies pride themselves on creating attractive titles and knowing the most effective ways of grouping ads.
Manage ads by focusing on quality and cost (CPA and CPC) in order to get conversions.
Pay Per Click Ads
This text uses the term product listing ad as a general term for the various ads listed below. In general, multiple types of ads are combined and published. Based on the characteristics of each type, choose an ad which will give you the best business results.
A Search AdAd display: the results of a keyword search
In search engines like Yahoo! or Google, ads related to the search keywords can be displayed.
For example, if an online shop selling soy sauce publishes a product listing ad with the keywords “buy soy sauce,” users who search for websites with those keywords will see an ad for the aforementioned shop in their results.
Even when raising the ranking is difficult with SEO, search ads allow you to display your website at the top of the search results page. This type of ad is also called a search network ad.
A Display AdAd display location: In an ad frame on a partner website
Unlike the product listing ad, a display ad can use images in addition to text to appeal to users visually. By specifying conditions, you can narrow down the target users to publish the ad to. You can specify keywords, topic, website category (e.g., FAQ website, fool portal website), age, and gender, among others, to narrow down the target users in order to avoid fruitless clicks.
A Search Targeting AdAd display location: In an ad frame on a partner website
A search targeting ad can be displayed based on the search keywords used. By combining these with product listing ads, you can also appeal to the search engine users visually.
A Remarketing AdAd display location: In an ad frame on a partner website
A remarketing ad can be displayed in an ad frame, on a partner website, for a certain period of time, to users who have visited your website before. It pushes users to revisit the website, tends to produce a higher click to conversion rate and is better for cost-efficiency.
However, since this ad is only shown to those who have previously visited the website, it will take time before it shows any significant results.
Ad PartnersPlace where ads are displayed
Determine where to publish the ad depending on the target users. To publish an ad linked with the Yahoo! search engine, you need to apply to Yahoo! Promotion Ads. To publish an ad linked with the Google search engine, you need to apply to Google AdWords.
Main ad partners:
< Google AdWords: Settings>
Since a product listing ad is displayed when search engines are used, it will be shown more frequently on Google or Yahoo! Choose where to publish the ad depending on your marketing strategy (e.g., if you want to expose the ad as much as possible, you want to start with a website with a lower share rate due to a limited budget).
Ad Formats
Two ad formats are available. However, only text ad can be used for the search-linked ad.
Text ad
Ads are displayed in text form. The representative two search engines (Yahoo! and Google) have different limits on the number of characters that can be used in an ad. Words in the ad text that match search keywords are shown in bold.
Ads are examined in advance and those using forbidden expressions may not be published.
Yahoo!: Title: Up to 15 charactersDetails: Up to 19 characters + up to 19 characters
Google: Title: Up to 12 charactersDetails: Up to 17 characters + up to 17 characters
Image ad
In display, search targeting and re-marketing ads, images can be displayed in addition to the ad text.
Note that the limit on the image size available for ads differs between the Yahoo! and Google display ad networks. Check the limit in advance. Using more images may raise the display frequency of the ad, but you will need a larger budget and time to create the banner.
As with a text-based ad, images are examined in advance.
Video ad
You have probably seen an ad played on YouTube before a video starts. This type of ad is a product listing ad.
Charging SystemPay-per-click
As the name implies, you are charged every time your ad is clicked. The fee varies from less than ¢10 per click to several dozen dollars per click depending on the word. You can set the maximum unit cost when publishing the ad and it ad will be displayed only when the charge does not exceed the unit cost.
However, even if you set the maximum unit cost per click to $1, a click does not always cost $1. The click unit cost is determined by the quality of the ad and existence of competitors etc. Therefore, the fee per click in this case may be lower than $1.
Pay-per-impression
This method can be used on some ad display networks including Google. The ad is charged by how many times it is displayed. You can set the maximum impression unit price (the maximum price for displaying the ad 1,000 times).
Use the pay-per-impression system when your ad focuses on how many times it is displayed, rather how many times than it is clicked.
Setting the budget for one day
For a product listing ad, you can set the budget for one day in addition to the click unit cost. If the click unit cost for the specified keyword is $1, the ad fee will be $10 if it is clicked ten times. But if the maximum budget for one day is set to $5, the ad will not be displayed after it has been displayed five times.
100% of the budget for one day is not always spent, but the percentage spent varies between 80% and 120%.
If the maximum budget for one day is set to $10 and the ad is clicked twelve times on the first day (costing $12), it will not be displayed when it has been displayed eight times on the second day (costing $8), so that the average budget spent per day will be adjusted to $10 automatically.
Narrowing DistributionNarrowing by time period
You can narrow when the ad is published by the day of the week and time period.
Narrowing by area
You can publish an ad in a specific area.
It is recommended that you segment areas of distribution by prefecture, city or town (e.g., in Osaka city only).
You can also specify the distance from a particular point (e.g., within a 10-kilometer radius from Shin-Osaka station). However, since the area is identified by not only IP addresses, but also search phrases, Google profiles and GPS data, narrowing the area too much may spoil accuracy, causing the ad to be displayed less frequently.
Narrowing by device
Distribution target devices can roughly be divided into PC, smart phone and tablet. By narrowing distribution by device, you can display the ad on PC and tablet only. (Some services allow you to set then to display the ad on smart phones only.)
Also, you can set the maximum click unit cost to different amounts depending on the device type. For example, you can set a higher price for the click unit cost on smart phone than PC and tablet because the conversion rate is higher on smart phones.
When Opening an AccountAccount
Usually, a company uses only one account. However, if a company creates a different website for each of their brands and a different person is in charge of each, multiple accounts may be used. The user privileges, campaign settings, charging method and other conditions are managed separately for each account.
Campaigns
You can set the budget for one day and the distribution time, area and device for each campaign. Since budgets are managed per campaign, many companies create multiple campaigns in accordance with the purposes of the ads.
It is recommended that you use the most suitable distribution method (search-linked ad, display network ad or re-marketing ad) for each campaign. You can stop publishing the ad for each campaign whenever you like.
Ad groups
You can set the ad text and keyword for each ad group.
The ad text consists of a title, description and a linked target as mentioned above. You can set multiple links or display multiple phone numbers on a single add. You can stop publishing the ad for each ad group whenever you like.
Keywords
Choose your main keyword based on the search phrase you believe users will use to find the website. You can specify conditions for search results and ad displays using a partial match, phrase match or a perfect match setting option. These will be discussed later.
You can stop publishing the ad for each keyword whenever you like.
The Quality of Pay Per Click AdsThe Quality of Product Listing Ads
The quality is the score an ad receives. For a product listing ad, improving the ad quality leads to an increase in customers without increasing the cost.
Quality is expressed by different terms in Google and Yahoo!. It is called the quality score in Google AdWords ads and the quality index in Yahoo! promotion ads. Quality is determined mainly by the following factors:
The click rate of the ad, in particular, is important.
The Importance of Quality
Quality is extremely important when creating your product listing ad. The display order of product listing ads is determined by the following formula:
Ad rank = quality x price
Since the ranking of product listing ads depends on the quality and price, you must raise the amount of money allocated to your ad campaign if the quality is poor. If the quality is high, the ad will be ranked higher with a lower cost and CPA can be reduced.
How to Improve Quality
In order to improve the quality of a product listing ad, it is important to raise the click rate. You can do it through several technical methods.
Include the keyword in the text of the ad
Users enter a keyword they are interested in into the search field. When the search results are shown, they follow the entered keyword with their eyes. Therefore, it is very effective to include the keyword within the ad text.
Include a point that will differentiate you from your competitors
If your ad is similar to that of your competitors, the ad cannot break into the lead. Study the ads that your competitors are publishing and make yours stand out in comparison.
Include action keywords such as sales, purchase or estimate
Action keywords are those entered by urgent users who are ready to take action. These keywords tend to produce higher click rates. They include sales, purchase and estimate.
Run an A/B test on the ad text
Review and improve the ad text to maintain high quality. The A/B test is run for this purpose. In this test, prepare two patterns (A and B) for the ad text and choose the one with the higher click rate. Then compare the chosen ad with a new ad and run the A/B test again to double check your results.
Classify the ad group in detail
It is important to classify the ad group in detail. Since ad text is created for each ad group, the click rate will be reduced if the ad text and necessary keywords vary within the same group. Create your ad groups with your customer in mind.
Improving your product listing ad not only raises the ranking of the ad, but also reduces CPC and CPA and optimizes your account.
Managing Pay-per-click AdsEffective Deployment MethodsKeyword selection
To achieve success with a product listing ad you need to carefully determine which keyword the ad should display on the search results page. Ask yourself “what intent is hidden within this keyword?” or “what keyword is used for this purpose?” before you even start putting a design together.
Let’s consider the intent of a user searching for a website using the keyword qualification. They’re thinking “I want to get qualified for something,” “I want to see what qualifications are available in a specific field,” “I am considering a school because it has a qualification course I want to take,” “I am not satisfied with my salary,” etc.
You need to be aware that a single keyword can encompass a huge number of hidden intentions and appeal to as many of those intentions as possible.
Big keywords and complex keywords
Big keywords are keywords that users frequently input into a search bar. “Qualification” is an example of a big keyword.
Complex keywords are composed of a combination of a big keyword and one or more other keywords users frequently look up. For example, “qualification programs.”
Small keywords are less-frequently-searched-for complex keywords.
Refer to the table below for the characteristics of each type of keyword. Note that these are just trends and you should verify them for yourself before you begin work on a project.
How to do keyword research
What is the best way to choose a keyword?
Keyword research tools provided by Yahoo! Promotion Ads and the Keyword Planner provided by Google AdWords are two methods you can utilize. When you enter a representative keyword into these tools, you will see the search volume on the Internet and the estimated click unit cost of the keyword. Note, that as these programs are free, your competitors are most likely making use of them as well. You should use them as support tools, but try to come up with a unique key word if you can. Look towards the TV and magazines for inspiration if necessary.
Matching types (perfect match, phrase match, partial match, partial filter match)
By specifying the matching type, you can specify with what search query the ad should be displayed. Choose the most suitable matching type for the purpose.
Perfect match
A hit occurs only when the search query matches the keyword perfectly. While highly cost-efficient customer attraction is possible, this ad is less frequently displayed and fewer conversions are expected.
Example: qualification program
Phrase match
A hit occurs when the search query is entered in the same word order of the specified keyword. This does not narrow down the target less than perfect match and ensures highly cost-efficient deployment.
Example: qualification program, recommended qualification program, popular qualification program
Partial match
A hit occurs when the search query partially matches the keyword, covering similar words, synonyms, and typos. The ad will be more frequently displayed and more conversions are expected. However, cost-efficiency can be lowered.
Example: qualification program, online qualification program, schools with qualification programs
Partial filter match
A hit occurs when the search query includes the specified keyword without regard to the order. Variations of expressions are not covered but synonyms are covered.
ex: web certifications, web, recommendations, certifications.
Keyword exclusions
You can prevent your ad from being displayed, and avoid fruitless clicks, when users preform a search by setting your ad sharing program to exclude specific keyword searches.
Exclusion of placement (ad distribution target)
You can prevent your ad from being displayed on specified websites. These websites can be chosen due to their low effect on the display network or because they are not compatible with your brand image.
Excluded websites are black listed while the opposite, websites you want your ads to be shown on, are white listed.
Enhancing the correlation between a search query and a title/description
It is extremely important to include the search query in the title of your website. For example, if a enters “online qualification program” and sees the title “Our School’s Online Qualification Program,” they are more likely to click the ad.
If the search query is included in the title, the title will be shown in bold. Be aware of ways to enhance the correlation between the search query and title/description. As for the ad text shown below the title, be conscious of its correlation with the search query as well. Make sure to provide any information not covered by the title.
Writing a figure which is as concrete as possible in the description attracts the user. For example, writing “**** dollar(s) off!” is better than just writing “Sale!”
Also, be sure to consider user’s state of mind when writing your title and description.
Narrowing down to local distribution
For both Yahoo! Promotion Ads and Google AdWords, you can specify areas where the ad is and is not distributed. The ad can be distributed throughout the country or to a particular region (e.g., Kanto area), prefecture or city.
If an ad for a local service is distributed throughout the country, they will have to pay for accesses that do not lead to conversions, resulting in low cost-efficiency. It is best to narrow the distribution area of the ad to the marketing area. Even if the service is provided throughout the country, it is a good idea to start with a limited distribution area so you can evaluate the ad’s effect with a smaller cost to your company.
Setting the distribution time period
You can set the time period and the day of the week in which the ad is shown. For example, a B2B product page is considered to be mainly viewed during daytime. Also, visits during the weekend are unlikely to lead to conversions. In this case, you can stop showing the ad during the nighttime and weekend. On the other hand, you can raise the amount of times you set the ad to be shown during the daytime so that it is ranked higher.
Conversion tracking
Conversions and profit are your main goals. The data you gather from tracking conversions is indispensable. It can help you decide if you should modify your budget, alter your approach, or change your keyword. Conversion tracking can tell you if they purchased a product, signed up for a newsletter, requested information, etc. This data can also reveal which keywords are encouraging users to convert and which are not and how changes to the landing page affect conversions.
You must measure the conversion rate after you choose a new keyword or launch a new ad. You can do this by setting the dedicated measurement tag provided by Yahoo! Promotion Ads as well as Google AdWords at the conversion point. If you are an ecommerce website, set it to the thank you page shown after completing an order or on the registration completion page for users requesting information.
Optimize the website by setting parameters
A parameter in this context refers to a mark such as the “?xxx” appended to the end of a URL. Since the browser ignores data after the “?,” you can use this parameter to identify which keyword in which campaign led to the results.
By adding referrer and medium information as well as the keyword from the successful campaign to the parameter, you can distribute the ad more efficiently. The following example shows the URL appended with a parameter that indicates that the referrer of the campaign ad is Google AdWords, the medium is the banner ad, and the keyword is “webAnalytics” for a class in December:
http://ift.tt/2fcC0XU
Re-marketing linked with access analysis
Re-marketing is a product listing ad function provided by Yahoo! Promotion Ads as and Google AdWords. It displays a specific ad to users who have accessed a website with a special tag embedded. To these users, the ad is displayed only when they are viewing the website of the corresponding network such as the Google or Yahoo! display network. Re-marketing works more effectively when used with access analysis.
Let’s look at an example: A user added a product to their shopping cart but did not buy it. Since the user added the product to their cart once, you can assume that they are a good candidate for re-marketing. Also, assume that access analysis indicated that the average period of time for consideration before purchase is five days. In this case, you can set the re-marketing period to one week rather than 30 days, which is programing. Use access analysis to figure out the user’s state of mind and use the data to re-market products.
The Listing Deployment Logic Tree
The following introduces a logic tree you can use as a guide while working on listing deployment.
For example, if you want to improve CPA, you can use one of the two representative strategies. One method is to improve CVR by setting the exclusion keyword and/or improving LP. The other method is to optimize the click unit cost by fine-tuning the matching type, adjusting the ranking and score of the ad text. You will see that improvement of CVR and adjustment of the click unit cost lead to CPA improvement. Similarly, if you want to maximize CVs, you need to adjust the click unit cost or increase clicks.
In this way, recognize how you want to display ads, and then reflect it in detailed marketing strategies that will keep the PDCA cycle producing results from listing deployment.
When you start working with clients, be aware of the type of person you are going to be consulting with on a regular basis and tailor your approach towards them. Also be aware that working with people in various positions will have you looking at different types of information.
If you are going to be working with a person in management, you might be requested to improve CPA with the current CVs maintained. If you talk with a web manager, they may request that you increase CVs with the current budget. You should choose whether to start your proposal with improvement of the CPA or an increase in CVs, depending on with whom you will talk.
via Blogger http://ift.tt/2eT9iwM November 02, 2016 at 11:06AM
Deep diving into website traffic analysis
When performing a web analysis, you may by analyzing the overall trends from your website. These trends include PVs and sessions from the entire website. You have to make sure that you understand the data you are gathering when conducting web Analysis. There have been issues in the past when people outlining a city’s PVs ended up making a mistake in the number of search engine results they had received. http://ift.tt/2eY0inUWhen performing web analysis, do not pay attention to detailed data like the referrer source or the analysis of each separate page at first. You might end up reaching an incorrect conclusion. Make sure to focus on user characteristics and inclinations. You can avoid making quick, biased judgements if you look at pure data. You also want to make sure that you understand the definitions of the number of sessions and unique users as the averages available in web analytics tools is often useless in the overall scheme of things. What Senior Web Analytics Consultants learn about trend analysisAs an Associate Web Analytics Consultant, you focused on learning about basic trends. As a Senior Web Analytics Consultant, you will learn reference values and how they interact with trends. Reference values are metrics you need to be aware of if there is a problem with your website and can help you fix it accordingly. You also need to be able to match trends with your client’s KPI.How Logfiles Are StructuredThe basic building block of web analytics is the access log. They are automatically recorded files that show the access status to the web server, the access history of a web browser, the content (web pages and image files, CSS files, JavaScript files, HTML files, etc.) and any requests made to the web server. When you are working with DMP and IoT, you need to be able to utilize and understand access logs right from the start. They are generally stored on a web server, but if the file becomes too large for the server, it will need to be compressed into a zip file with respect to the past log. Aggregated Logfile DataBefore logfiles are processed by analysis tools, the stored data is referred to as raw data or raw logs. The information in these files is written in the CSS (Cascade Style Sheet) language used by programmers to set styles for webpages.
Hits and the number of hits to a webpageUser agents, including web browsers and search engine spiders, request information from websites. These requests are called hits. Each time a webpage is viewed, a record of the hits i.e. logfile or web server log is automatically created and saved to the web server. Web analytics consultants use these logfiles in their Analysis.While hits are important, they are often misinterpreted as a metric that represents a website’s success. However, the number of hits rarely shows the actual number of users accessing a site or the number of webpages viewed. The reason for this discrepancy is found in the way a webpage is constructed. Webpages are made up of many individual files that are each requested by the webserver when a user accesses a page. Each file request increases the hit-count for a website. For example, if a homepage is comprised of one HTML file, one JavaScript file, one layout file, one text file, five images, one Flash file, and one video file, then 11 hits will be added to the hit-count each time that page is viewed and the access will be recorded to the server as a logfile. The number of rows of text within each logfile represent how many hits a webpage receives. The lines that show access to HTML files represent PVs. It is also important to note that repeat users generate fewer hits as browsers often store webpage files locally i.e. caching. Once files have been cached, they will not be requested from the server when a webpage is viewed. When using access analysis tools, remember that they display file numbers, not hit numbers and that in older tools, PVs are occasionally called hit numbers. Make sure that you are analyzing the correct category of data before you begin your analysis. What You Can See from HitsWhen a huge number of hits are requested, while the bandwidth of the communication line used by the web server is low, the requested web page lags i.e. takes a long time to load. Having a webpage that lags is a major problem as visitors will exit your website if it does not work properly.Hits were originally utilized when access analysis tools were used for server and network management. Hits (or files) may still be shown (e.g., for a rental server), though this value now has almost no influence on attracting customers to a website. Understanding Raw DataThe access log file recorded on the web server is often saved with a “.log” file extension or as a compressed file with a “.zip” file extension. They can be opened in a text editor like Notepad just like normal text files. Since the access log file at this stage is not yet processed by analysis tools, it is often called raw data or raw logs. Log formatsWhen a web server receives a request for its contents, it records the request as a file name. There are four types of files names commonly used by servers. They include the common log format, the combined log format, the Microsoft IIS log file format and the W3C expanded log file format.1) Common log formatCommon Log Format is the log format used by the most popular web server “Apache.” It records the following data items:A “-” is shown for an item that was not collected by the server. Note that this log format is not used as often as other formats. 2) Combined log format
This format adds the user agent (OS and browser types) and referrer information to the common log format.
3) Microsoft IIS log file format
This is only one of the many log file formats used by the Microsoft web server “Internet Information Server (IIS).” It records a user’s IP address, user name, date, time, service and instance names, computer names, server IP address, time taken, received number of bytes, transferred number of bytes, Windows 2000 status code, request type, and operation target all separated by commas.
192.168.114.201, -, 03/20/98, 7:50:20, W3SVC2, SALES1,
172.21.13.45, 4502, 163, 3223, 200, 0, GET, DeptLogo.gif 4) W3C extended log file format
As with the format used by Microsoft, this format also uses commas to separate collected data. Unlike with other log formats, you can select fields that you want data from. The example below shows lines written to the log file when the checkboxes for [Time], [Client IP Address], [Method], [URI Stem], [Protocol Status], and [Protocol Version] are enabled.
17:42:15.16.255.255 GET /fefault.htm 200 HTTP/1.0 Defining MetricsReferrers
A referrer is a referencing source or a link source which shows the URL of the web page that was open in the browser immediately before the current web page. By investigating the referrer to the landing page, you can see the search website or search keywords used by the user or the external website that links to your web page.
Transfer Capacity
The data quantity (number of bytes) that the web server distributes is recorded when the status code reads 200 and is 0 bytes.
IP Address and Hostname
The IP address is an identifier given to the user when they connected to the Internet. The host name is the web address used by users when they are trying to reach a page. Rather than typing in a string of numbers they can type something like “web-mining.jp” which is more readable than an IP address. The host name is assigned by a server called the DNS (Domain Name Server).
Analysts can use the IP address and host name to find out how a user is accessing a website.
Status Code
This three-digit status code represents “the meaning of the response” returned by the web server for the request. The commonly used status codes are shown below.
User agents
A user agent is a string of characters that a communication device can transmit to a web server. The user agent contains information about the properties of the device, such as its OS, the browser it is using and the device’s hardware.
There are a wide variety of user agent formats in existence. For example, Apple uses “AppleWebKit/536.26 (KHTML, like Gecko) Version/6.0 Mobile/10B329 Safari/8536.25” to identify the iPhone. Microsoft uses “Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0)” to identify different versions of IE on a PC. Google uses “Mozilla/5.0 (compatible; Googlebot/2.1; +http://ift.tt/eSXNch)” to identify the Googlebot search engine spider. Goo identifies their Ichiro 4.0 spider with the string “ichiro/4.0 (http://ift.tt/2f6zzII. html).” Note that the user agent can be disguised by the user and is not always correct. How to Utilize the User Agent
You can obtain the following information from the user agent.
This information can be useful in many ways. For example, determining the type of browsers accessing the site so that your business can optimize it accordingly.
The user agent is the information defined for the browser used by the end user. This information includes the browser type and version as well as the OS type and version. The format depends on the browser and OS types. Combining the Windows OS and Internet Explorer
The most important data gathered by user agents are Version Tokens and Platform Tokens. A version token shows the specific browser name, while a platform token identifies the OS.
For Firefox, Safari and Google ChromeFirefox, Safari and Google Chrome follow the user agent format officially defined by Mozilla.Checking the User Agent
To display the user agent of the browser used, enter the following in the browser’s address bar.
JavaScript:alert (navigator.userAgent) Session Duration
The session duration indicates how long a user viewed the measurement target (web page or website). Page session duration and website session duration data is available.
Page session duration indicates how long a user stayed on the page.
The website session duration indicates how long a user stayed on the website. It is also called session duration per visit.
Website session duration is the sum of the session duration of pages from the start until the end of a single session. Note that you cannot acquire the page session duration for the last page the user viewed before leaving the website because the page next to it exists outside of the company’s website. Also, keep in mind when looking at website session duration data that “the page session duration for the last page is not included.” In the following example, the website session duration is eight minutes because the page session duration for the “product page” viewed last is not included. Sessions
Average session duration is the term used to describe the average time users spend on a website each session. In many cases, this number is divided by the sum of the duration of each session by the total number of sessions. Note that this number includes sessions that ended in immediate bounces or exits.
The total duration of all sessions (in seconds) / number of sessions
For example:
The total session duration for a website is 2000 minutes or 120,000 seconds, with 200 total sessions, so: The average session duration is 2000/200 = 10 minutes or 600 seconds Individual session durations are calculated differently depending on whether there are engagement hits on the last page of a session. If there are no engagement hits on the last page, then the duration is calculated as follows: The time of the first hit on the last page - the first hit on the first page For example: Page A’s first hit was at 11:20 PM Page B’s first hit was at 11:25 PM Page C’s first hit was at 11:30 PM 11:20 - 11:30 = A session duration of 10 minutes or 600 seconds. If there are engagement hits on the last page, then the duration is calculated as follows: The time of the last engagement hit on the last page - the first hit on the first page
For example:
Page A’s first hit was at 11:20 PM Page B’s first hit was at 11:25 PM Page C’s first hit was at 11:30 PM; last engagement hit: 11:35 PM 11:35 - 11:20 = A session duration of 15 minutes or 900 seconds. When the session interval is shorter than 30 minutes
In the figure to the left, the user exited the website at 18:10 and visited the same website again at 18:35. In this case, the session interval of 25 minutes is also included in the website session duration and is totaled out at 35 minutes.
Some analysis tools connect two successive sessions of which the interval is shorter than 30 minutes even if the browser was closed during the interval. Other tools check the referrers and separate sessions for inflows from an external website. Check with the vendor of each analysis tool for details. Google Analytics measures the average session duration in the same way as above, but it can also use any events that have been tracked to gather last engagement hit data if you set it to do so. Analyzing MetricsMetrics such as page views are the basis of web statistics analysis. They can help businesses gather data on their progress and can help them determine areas in which they must improve. Metrics gathered by analysis tools are useful tools, but you must be aware that they are occasionally incorrect representations of reality. Successful web analytics consultants must be able to use analysis tools to take advantage of the wealth of data at their fingertips. Knowing the types of metrics available and how best to use them is an invaluable skill. Pageviews
Calculating the Average Number of PVs Shows an Index of Interest for your Website.
In general, the more PVs you are receiving the better your website is doing. For example, Mr. A found apparel shop “ABC” through a search engine, visited the website, but left just after viewing a single page. Can you say that Mr. A was interested in this shop? He may have found the information he needed on the first page of this website. However, a general EC website requires a user to have visited at least five pages before conversion is registered as shown below. So, satisfaction from a user who viewed just one page and left the website should be considered less important than that of a user who converted. [EC Website Case Study]
“Product information page” => “Cart page” => “Personal information entry page” => “Confirmation page” => “Thank you page”
In addition to these pages, many visitors will choose to visit the company profile page as well. They often want to learn about the company and how reliable it is. A general rule you can follow is that the more a user wants to buy a product, the more pages they will view. Situations exist when the average PV count is not as important as it would be usually. Blogs in particular tend record a less than average number of PVs. This is because many blog readers just browse new articles then leave. Cases where you find sites with less than two average PVs are not rare. Remember that, depending on the purpose of the website, your client company may reach its goal even if the average PVs are low. Analyze PVs by Comparing Data with Other Periods
When analyzing PVs, it is difficult to find issues or points to improve just by viewing the figures. It is important to compare index data with data from other periods not limited to PVs. For example, compare the current data with the data from the previous month or the same period last year. You can visualize an increase or decrease in values accurately by comparing data from different periods. When you find a rapid change in PVs try to figure out the cause.
Consider Changes in PVs per Referrer
If an apparel company spends $100,000 in advertising expenses in November and $50,000 in December, PVs might decrease in December in proportion to the decrease in advertising. PVs can also decrease due to a drop in your search engine ranking. If a link to the company page is posted to a popular blog, PVs may increase.
It is important to confirm PVs per referrer, per keyword and per search engine in your analysis. Compare the values with other periods as mentioned above to analyze the cause of changes in PVs. Estimating the Size of a Website through Data from an Index.
Portal sites like Amazon and Rakuten use PVs as an index to indicate the size of their website. These types of massive sites can have PV counts into the tens of billions monthly.
Since PVs are proportional to the volume of products being offered, portal sites with more content naturally have many PVs. In contrast, specialized websites (e.g., an information website related to access analysis or movement) have less content. When publishing an ad, PVs can be used as an index to indicate the ability of a medium to attract customers (e.g., web page or website where the ad will be published). Sessions
The more sessions you can get, the better situation you are in. When this index value is high you know that your website is popular. For an EC website, it can be said that the number of sessions is equal to the number of prospective customers.
To increase sessions, you need to analyze inflow. Inflow to a website includes links from search engines, referrer websites (e.g., the website was introduced in a blog and users visited via the link in the blog article), ads, newsletters and bookmarks. To increase sessions, you need to check how many users visit the website from each type of inflow. If sessions are smaller than you were expecting or not likely to increase, analyzing the inflow and search phrases and conducting SEO or publishing a keyword ad are effective measures you can take to counter the problem. Analyzing Unique UsersUnique users, page views and the number of sessions
If the number of unique users increases, that means that the number of new visitors to the website has increased. While this is a good thing, you will need to compare the number of unique users to the number of sessions and page views to see the true scope of the situation.
If the number of sessions has increased in conjunction with the increase in unique users, then you know you are getting a higher influx of visitors to your site, but if the number of PVs does not increase, you can assume that the content of the website is not holding the user’s attention and you need to change or improve your content. Visit frequency and the number of visits from unique users
Visit frequency tells us how many days have passed since the last time a user visited a website. It can also tell us how many times the user has visited. Using the data in conjunction with the number of unique users can shed light on inflow to your website and if there are any areas that can be improved in order to increase conversions.
How to take advantage of the new unique users rate
The new unique user rate shows how many new users your website is attracting. If attracting new users is your goal, you can use these metrics to show how successful your marketing strategy is over the course of a set period.
Note that having a high new unique user rate is ideal, however if you are an EC website, you also want to increase your rate of returning users.
Google Analytics can show you the number of new users, returning visitors, sessions, repeats, PVs, and the interval of repeats.
Demographic information
Programs like Google Analytics can show you demographic information on users. It collects data from various sources, including cookies and surveys, to show the analyst ages, genders, locations and user interests. The data can help you predict user behavior, trends and ways to target specific users.
Using Conversions for web analyticsThe Basics of Improving your Conversion Rate.
There are three basic things you should do if you realize that you are not getting as many conversions as you desire.
Review the customer attraction method to increase inflows
The fewer customers there are, the less conversions will happen. Consider how to increase inflows to the website. Increasing Internet ads, newsletters, SEO, and SEM are effective ways you can attract customers.
Improve areas where users tend to exit from the website
If you notice a trend where users are exiting from the website before conversion, try to pinpoint the areas where they are leaving the most and work to improve them. There may be issues with content, slow loading times or even broken links. You can check the exit and bounce rates for each page individually and also check for various search phrases and referrers to figure out what is keeping your customers from taking that final step. If the exit rate is high for a specific referrer, you might want to reevaluate just how you can attract and keep their interest.
Improve the process customers experience as they are converting
The final act of conversion is always preceded by a specific page or pages on a website that shows the customer exactly what they are going to be receiving after they make their purchase or request. The shopping cart of an EC website is a good example of this kind of page. If there is a problem with the link to leading to the shopping cart, application etc., do your best to resolve the issue or improve the page so that the user will convert.
E-Commerce
When a user converts they supply the company with product names, sales revenue, member IDs etc. You can use the collected data to track the path a user followed to get them to the conversion page, inflow and outflow.
Google Analytics provides a method called E-Commerce tracking to analyze e-commerce. In particular, data on the order completion page is fed to Google Analytics, and the daily sales amount and sales amount per product can be determined. Average
The following explains attribution analysis.
When you conduct an attribution analysis, focus on the contribution to conversion For this type of analysis, focus your attention on data from the web server. You will want to take a close look at inflow and inflow referrers and how they relate to sessions that, hopefully, progress into conversions. These relations and where referrals are coming from are counted as contributions to conversion. For example, a user visited your client’s website for the first time after following a referral link in Ad A. They liked what they saw and ended up completing their visit with a conversion. The contribution inflow from this visit is due, 100%, to Ad A. A second user followed the same link in Ad A but chose not to convert that session. The second user visited the website again from Ad B on another day finally leading them to convert. In this case, Ad A and Ad B had a 50/50 contribution rate.
*Distribution ratio of the conversion value varies depending on several factors, for example, the order of visits and classification of inflows (charged or free).
Measure an ad’s “indirect effects” on customers
The concept of the indirect effect of ads has been studied in the past. Looking at the way ads can affect memory to create post-impressions and lead to post-clicks is important to knowing the full effect of your marketing campaign.
The indirect effects seen by post-clicking and post-impressions are also called click-through conversions and view-through conversions respectively. To analyze view-through conversion, distribution data on the ad server is mainly used. You can find out the number of assists, in addition to conversions, when you use Google AdWords ads. They are called assist conversions and measuring them is useful so that you know the medium or ad that indirectly contributed to a conversion. Session DurationNotes on Analyzing Session Duration
When a session is terminated during a stay (i.e., no request has occurred for 30 or more minutes), a new session is established and the session duration is counted as having ended.
Some analysis tools define the session duration for a page (page session duration) from which the visitor bounced to one minute.
When a visitor bounced to another website and returned to the website before the session is disconnected, a longer session duration than the actual duration may be recorded. Example of User Session Duration
Assume that a user browsed the website in the following sequence:
0:00:00 Home => 0:00:30 Top of category => 0:01:15 Product page A => 0:02:00 Product page B => 0:02:30 Follows link to another website
The session duration in this case is two minutes; lasting from 0:00:00 when the user accessed the home page until 0:02:00 when the user started browsing product page B. Google Analytics uses the term average session duration on visit. Notes on Analyzing Page Session Duration
Is a Longer Session Duration Better?The best session durations depend on the structure or purpose of the website and page.For example, for a website or page that directs visitors depending on which search engine they come from or what they want, it is better for the users to be able to access the required information as soon as possible, therefore a shorter session duration is better. On the other hand, a website or page that is dedicated to providing information generally assumes their visitors are reading the contents of their website thoroughly. In that case a session duration with some length is preferred. What is “Session duration 00:00:00” in Google Analytics? As mentioned above, the session duration cannot be calculated for the exit page, thus it is shown as “00:00:00.” Relationships with Other Indices
Observe the session duration per page from the viewpoint of the visitors. For a page that is providing information, Wikipedia for example, longer session durations are preferred. They like it when the information is being accepted by visitors and therefore they are being visited frequently and for fairly long durations.
A page with a long session duration and many PVs can be understood as being browsed by many people for a long time. However, if the session duration is long but the PV count is lower than expected, it can be said that “the contents are interesting but many users have not noticed this page or it does not seem to be able to hold user attention.” Analyzing Multiple DevicesYou can see what device users use for access from the user agent data recorded in the logfiles. By comparing inflow or conversions per device type, you can propose a website design suitable for each device type. In Google Analytics, you can use the custom dimension to obtain user attributes on the form confirmation screen or completion screen and use this data for analysis.*Segmentation by Google EstimatesGoogle Analytics shows the age, sex, and interest categories as estimated by Google. Note that this attribute is an estimate made by Google. Take the data provided with a grain of salt.Behavioral analytics for smartphones and tabletsIn recent years, with the rising popularity of smartphones and tablets, many businesses are prioritizing the improvement of mobile-friendly websites rather than their regular websites. A web analytics consultant must be aware of this trend and act in a manner that will benefit their client.You will need to understand how mobile devices interact with PC’s and how mobile sites compare to websites. Being able to identify session durations, PVs, etc. on smartphones is important if you are going to make sure that they are fulfilling user needs and resulting in conversions. Google has standardized smartphone support functions and other services are following in their footsteps. Other tools help analysts understand user behavior on mobile sites and how you can make sure that your site is displaying correctly on small screens. You will also need to make sure that your site responds to the now-standard motions that control touch-screens. TapA tap of the finger behaves like a click on a mouse. Double tapping is equivalent to double clicking, while pressing and holding brings up a menu or more options.FlickThis is a left-right motion that allows you to transition between pages. You can go back to the previous page, load images in a playlist, etc.Pinch in / outThis action allows a user to make text or an image larger or smaller as it zooms the page in or out.SwipeThis motion, a finger slide, allows the screen to scroll up or down.You can analyze these actions in order to improve the various elements on a mobile site. Notes on Smart Phone AnalyticsSmart phones can accept cookies. This allows analytics software to gather data and make comparisons as it does with PCs. In the example from Google Analytics below, a “Yes” for “Mobile (including tablets)” means there is inflow from smart phones and Tablet PCs.If there is a large inflow from the mobile category, it may be necessary to setup a mobile website. If there are differences in the bounce rate or average PVs, you may need to improve said website. If the numbers you are getting when you analyze traffic from mobile devices are low, there may be issues such as the page display lagging, characters and images being too small or tapping on links and buttons being difficult. Consider improving these issues. Many users use smart phones with one hand and you need to consider ways to make the experience user friendly. Clicking a button can be hard if it is in a bad location or if it’s too small. If you are creating an application, make use of operation methods specific to smart phones, such as flicking, for better usability. Note that there are differences in web Analysis between smart phones and PCs. The following discusses these differences. ReferrerSince accesses from smart phones come via browsers, you can obtain the referrers name.Cookies and JavaScriptNote that some smart phones accept cookies from the first-parties only and do not accept those from third-parties by default.Model name, OS and carrier nameThe model name as well as the user agent can be obtained from smart phones, but there are cases where determining the carrier is difficult. If a model is released only for a specific carrier, the carrier can be determined. However, for models supplied by multiple carriers (e.g., the iPhone), you cannot determine the carrier.Unique user identificationFor some smartphone OS types, you may be able to identify unique users from the terminal ID number and the number of the SIM card. Currently, browsers cookies are mainly used for unique user identification, similarly to PCs.Identification of unique users by line connectionSmart phones access the Internet using the cellular network. For connection via LTE, 3G or 4G accesses are made via gateways and the identification of unique users from IP addresses is difficult. If a user is utilizing a specific Wi-Fi connection it is possible and the ISP or organization name may be identified in this case, as with PCs.Notes on Smart Phone Analytics
Smart phone users often get referrals to websites from apps or non-official websites. With apps like Yelp, a user can search for non-specific places and then they are able to read reviews and follow a link to the main webpage. These apps often show search results based on location rather than popularity. They also allow for PPC advertising through companies like Admob, immobi, and Admaker. There are also affiliate ads called “reward ads” which reward visitors for clicking or watching a video.
Web Analytics for the Smart Phone
Refer to the user analysis mentioned above when considering the user friendliness of your mobile website. Since the users mainly browse websites from smart phones while in transit, you can narrow their purposes for browsing. They will use computers to collect more in-depth information at a later time.
Notes on DeviationsTotal Amount of Unique Users and SessionsThe sum of daily (weekly) unique users is not necessarily the number of weekly (monthly) unique users
When aggregating unique users, note that the sum of daily (weekly) unique users is not necessarily the number of weekly (monthly) unique users. This is because the number of unique users is not the total number of visitors and multiple visits by the same user are counted as one unique user. If the same user visits a website on different days, they are counted as one unique user on each day. They are also counted as just one unique user for the entire week or entire month.
Similarly, when aggregating sessions, the sum of daily (weekly) sessions is not necessarily the number of weekly (monthly) sessions. For a session across two days, it can be seen as a single session for each day, but also as a single session for a couple of days. *Some tools separate the session across two days at 00:00 and count it as two sessions. Errors Due to the Number of Days in a MonthNotes on comparing PVs and other data with other monthsWhen you compare PVs or sessions of one month with those of another month, pay attention to the number of days as well as the number of weekdays in each month.Since these numbers differ for each month, simply comparing the monthly data will produce a difference larger than the actual situation. By using the average value obtained by dividing the number of days, accurate analysis will be ensured. For example, there is a difference of three days between January and February. If the daily PVs are ten for both months, the monthly total PVs are 310 for January and 280 for February, resulting in the illusion that PVs decreased in February. When comparing data of a month with that of another month, be sure not to simply compare monthly data. Also, months with fewer weekdays (e.g., the end of the year and months with many holidays) tend to show decreased PVs. Therefore, be sure to consider the number of weekdays. When comparing data with other months, you should compare data not only with the previous month but also with the same month of the previous year. By comparing with the same month of the previous year, comparison eliminating seasonal factors (e.g., summer vacation and Christmas holidays) can be done. If the types of visitors include both B2B and B2C, analyzing data by separating time periods into “daytime on weekdays” and “nighttime and holidays” may produce more accurate analysis result. In particular, B2B users access more during daytime while B2C users access more during nighttime and on holidays. Separating the time period in this way allows you to understand the trends of each user group more easily performing a web analytics KPI dashboard analysis, you may by analyzing the overall trends from your website. These trends include PVs and sessions from the entire website. You have to make sure that you understand the data you are gathering when conducting web Analysis. There have been issues in the past when people outlining a city’s PVs ended up making a mistake in the number of search engine results they had received. Screen resolution, browsers, and operating systemsWhat You Can find out from Screen Resolution DataYou can obtain screen resolution data from access analysis data. The screen resolution is the resolution of the display monitor (e.g., PC monitor) and is generally shown as a number like “1280 x 1024.”You may think that obtaining screen resolution data is not useful for improving a website, but it is. From the screen resolution data, you can see what screen size the majority of visitors view the website with, if they view it in landscape or portrait and reflect this information on the design of the website with the goal of improving the visitor experience. It is not necessary to check screen resolution data every day but it is important to know what settings are the most popular so you can cater to the majority of your viewer base and build a website that will load quickly and be user friendly. What You Can Discover From OS and Browser DataYou can obtain the types and versions of the user’s OS and browser from the access log.The OS is the operating system running on a PC, such as “Windows 8” or “Mac OS.” A browser is the software program the user uses to access the Internet, such as “Internet Explorer” or “Firefox.” Information regarding the types and versions of a user’s OS and browser can be used as a reference when building a website. For example, since the displayed design differs a little depending on the type and versions of the OS and browser, you can determine which OS and browser should be used to check if your design layout appears consistent over all systems. Mobile versions of websites should be checked as well as they will differ from layouts that work on PC’s or tablets. Browsers and OS’sAs it is almost impossible to build a website that conforms to all of the screen sizes, OS’s, and browsers available; check with your client to see which types of browsers they want to work with. This will save you and your clients both time and money.Characteristics and Deviation in Web Analytics DataPower Distribution and AverageLong Tail Data
Since web analytics data largely fluctuates, long tail data is common.
Long tail data means that when data values are arranged in the descending order, the foot seems like the long tail of a dinosaur. For example, if you arrange search phrases used in search engines in the descending order of PVs, there will be enormous number of search phrases each of which has only one PV, forming a long tail.
One characteristic of such data is that mean and variance do not make sense.
While many of the data items used by web analytics tools show means, you should be careful to see if the mean really represents the population. In particular, be careful with the session duration. An average session duration of visitors does not likely link to actual user movement or lead to a result. Mean and Median An index “mean” is frequently used to analyze various data. A mean is the value calculated by dividing a total value by the number of data values. When any of the values is extremely different from others, the mean may be a value far from the actual condition. For example, if 19 visitors browsed a page for 20 seconds and one visitor browsed the same page for 300 seconds, the session duration for this user is extremely different from the others. The mean for the session duration in this case is (19*20+300*1)/20 = 34 seconds. This is far from the actual condition. For such a situation, you can use the “median.” The median is the value at the center when data values are arranged in descending order. If there are 21 values, the 11th value is the median. If there are 20 values, the 10.5th value is the median. Since the “10.5th value” does not exist, it is actually the mean of the 10th and 11th values. For the example above, the median is “20 seconds” and is close to the actual condition. Reasons of Deviation
Data output from a tool differs depending on the purpose.
A report of an ad effect measurement tool is intended to show the result brought by an ad, and that of an access analysis tool is intended to show the result brought by website traffic analysis. They use different result indices and there are differences in the data. For example, conversion values are different between a listing ad and access analysis data. While a listing ad considers a conversion a result even if it is made by a user several days after he/she clicked the ad, access analysis does not consider a conversion as a result unless it is made during the session from inflow from the ad in many cases. Difference Due to Cached Data
When measuring PVs, there are differences in values depending on the tool used. This is caused by how the tool handles cached data.
If the tool uses the server log method, PV is counted each time a file is accessed. When cached data is read, no file access occurs and PV is not counted. If the tool uses the web beacon method, PV is counted each time the beacon tag is read. Even when cached data is read, PV is counted as far as the beacon tag is read. Difference Due to Data Acquisition Timing
Access analysis tools show different values depending on how log data is acquired. While a tool using the web beacon method counts PV when a page is displayed in the browser, a tool using the server log method counts PV when a request for the page is made to the server. Therefore, if the user requests a page to the server and then closes the browser before the page is displayed, PV is counted by a tool using the server log method and not counted by a tool using the web beacon method. This results in more PVs with tools using the server log method than those using the web beacon method.
Difference Due to Tag Location and Specifications (of Analysis Tools and User Environment)
With the same access analysis or ad effect measurement tool, different data can be obtained depending on the tag location and/or specifications. If you place tags near the top of the HTML file and near its end, different data is returned. This is because if the user closes the browser before a web page is displayed completely, tags near the top are recognized while tags near the end are not.
In addition, different analysis tools connect sessions and judge that a session has been terminated differently. Generally, a session is terminated in 30 minutes with no operation. But some tools allow the user to change this period. Some tools use referrers or requests to judge if a session is continued. Also, data obtained depends on the settings of the browser and/or security software. If a browser is set to accept cookies from a first party but not from a third party, only access analysis tools which use first-party cookies can obtain data. Addressing Differences in Data
Cases where values of a single index (e.g., PVs) can be different depending on tools have been mentioned so far.
If this difference is large, you need to check how to set the tools and their specifications to find out the cause of difference. If you consider that data from one tool conforms to the actual condition more than other tools, clarify the basis of your judgment in a report or explanation. If you cannot judge which data is most realistic, show all of the data to the participants and think about a solution together. If you cannot find the cause of the difference, choose data which seems to be more accurate, and regularly check the other data. If the difference is not large, choose the one by yourself or by discussing it with the participants, and clarify the reason why you chose it. What is web analytics? Web analytics is an excellent method to analyze user behaviors in real time, however, there are differences due to which tools are used. Troubleshooting the cause of differences too much will not lead to results, an often turn out to be just a waste of time. Do not forget that you are conducting web analytics in order to bring business results, and you should focus on the comments and activities which lead to the results. via Blogger http://ift.tt/2f6A0Th November 01, 2016 at 10:29PM |