The problem of orchestrating all interactions and weaving them together into a unified customer experience is harder than it first appears. The reason lies in the fact that many companies now interact with each customer through multiple channels. This creates at least two problems.
The first is a technological one. Complex businesses with multiple product lines often do not use a single database or IT infrastructure. For instance, when a firm interacts with a customer through both traditional brick-and-mortar retailing and online channels (omni-channel retailing), it is quite problematic to track that customer across all her interactions with the firm’s various touch points.
This leads us to the second problem, an organizational one. The reason for the multiple IT systems is often historical. Different business units develop their own processes and systems, a problem that is exacerbated when divisions are added through mergers and acquisitions. Moreover, these units often fight for internal resources or compete for status and career slots. So, when a customer who was well advised by an employee in a retail store ends up buying a product from the online branch of the same retailer, the store manager might view him as a customer who was lost to another unit. Similarly, consider Disney. To generate the amazing guest experiences that we described previously, Disney had to overcome exactly these challenges:
The data related to a given customer was scattered among (for example) the Disney video games the customer had on her PlayStation, the retail store at which she purchased the last piece of Disney apparel, the Disney movie she saw on Netflix, the Disney theme park she visited last year, and the Disney Hotel she stayed in. Integrating this into a single customer relationship is not easy, but without integration, how could Bill, who was acting as Captain Jack Sparrow in Disney’s Anaheim park, remember that little Sydney had seen Bill’s colleague, François, in Disney’s Paris park last year?
Even though they are part of the same company, theme parks have to be profitable, and so do feature films. To move from a product-line-(channel)-based view of the world to one that puts the customer in the center of all transactions requires strong vision and leadership support from above. Traditionally, it was the customer who had to navigate Disney’s organizational chart in order to stitch together a seamless experience. As part of the MagicBand implementation, organizational changes had to be executed as well.
But Disney did it, and you already know the results from chapter 1. Not only did the guest experience improve, but in many cases, the costs also dropped. Where it was once necessary to manually weave together transactions across channels and time when handling special customer requests or complaints, now a seamless customer experience can be delivered with high efficiency.
Improve Customization Based on Past Interactions: Strengthen “Request”
While the first level of customization is all about keeping track of and getting to know the customer across individual transactions with the firm, the second level is about turning this information into actionable knowledge. The firm needs to use the information about a customer’s needs to translate it into a specific request for an appropriate product or service. To understand which product or service is most appropriate, the firm needs to understand which willingness-to-pay drivers are particularly important for a given customer.
In the last chapter, we introduced the concept of the customer journey (see figure 4-1). At each step of this customer journey are a number of possible willingness-to-pay drivers. Understanding these drivers is essential to customizing the customer’s experience. (We will guide you through this process in the next workshop chapter.) Most importantly, what the customer journey highlights is that your customer’s willingness-to-pay is driven not only by the product or service itself (the “what”) but also by how a customer interacts with you and how the customer can access your products.
For instance, convenience of access has become an ever more important element of customization. Again, the world of education provides an illustration. In the old world of brick-and-mortar education, physical campuses and class schedules created a rigid delivery system. In today’s world, “anywhere and anytime” has become the mantra of online education, especially in the market of educating busy professionals. Thus, physical campuses and fixed class schedules are inconveniences that negatively impact the customer willingness-to-pay.
Customization goes beyond the ability to access content anytime the learner wants. While it sounds great to have access to tens of thousands of educational videos around the clock, the options can be overwhelming. Maybe a learner wants to become a web developer. Coursera, EdX, and even YouTube have plenty of video material that the learner would benefit from. But where to start? As mentioned earlier, Lynda.com bundles videos so that they collectively correspond to a career track. It takes the expressed need of the learner (“I want to become a web developer”) and turns it into a solution (“Take JavaScript first, then take a course on interface design,” etc.). This is the idea of curation leading to customization. The smartbook at McGraw-Hill takes customization one step further still. Beyond reacting to explicitly expressed user needs, it also infers customer needs based on past interactions. Past reading and test-taking behaviors are analyzed and used for future curation.
This is the skill that Amazon has mastered so well. By observing our past browsing and buying behavior, Amazon is able to infer our needs. Moreover, it creates a virtuous cycle. The more a firm engages in business with somebody, the more it learns about the customer and the better it is able to customize future offerings. The better the firm customizes its offerings, the more delighted the customer becomes, bringing the customer back again and again, creating even more information for the firm. At some point, the customization becomes so good that customers get locked in and stop taking their business to competitors. Recent data shows that Amazon has more than 40 percent market share of online retail. This feedback loop is visualized in figure 5-2. Given one customer, a firm learns more and more about what that one customer needs. This creates a positive feedback loop: recognize, request, respond, and repeat, then recognize, request, and respond even better, and so on.
FIGURE 5-2
Learning at the level of the individual customer
Learn at the Population Level to Enhance Product Offerings: Strengthen “Respond”
We recently worked with a telecommunications executive who shared the following story. He was at the checkout counter at a large home-improvement store. The cashier asked for his zip code, to which he responded, “I will tell you my zip code if you give me a 5 percent discount. In fact, if you give me 10 percent off, I will tell you the street I live on.” The clerk called the manager, who took the deal!
It is said that, in this connected world, customers pay not just with their wallets but also with their data. We will discuss this theme further in chapter 8. For now, let us simply observe that knowing your zip code and your street address does not just allow the store to serve you better; the store can also transfer this knowledge to better serve other customers like you. Conversely, it can use the data on customers like you to help in predicting what you might need. This is the first advantage that comes from population-level learning. The firm can move beyond using an individual’s data to help that person by using aggregate data to make customized suggestions or decisions for each of its customers.
Population-level data makes even more powerful learning possible. By learning about its customer population, the firm can create a better product or service offering. After all, what good is it to have a deep understanding of your customers’ needs if you don’t have the products or services available to satisfy those needs? True customization requires not only understanding the customer deeply but also having the right product and services available. Thus, level 3 of customization is fundamentally about strengthening your ability to respond.
First, consider examples from the world of education. Learning analytics is emerging as a hot new field. If we can predict which students are likely to str
uggle in a course, we can take corrective action before problems occur. Teachers can learn where individual students or entire classes are likely to struggle, allowing them to proactively alter what they offer in their courses. The same can be true for authors like us. If, for example, we knew that learners love the worksheets in chapter 3 but rarely use the ones in chapter 10, we could improve this book. In fact, we hope to achieve exactly this through our website, connected-strategy.com.
A parallel trend is playing out in medicine. Under the label of personalized medicine or precision medicine, health care companies mine genomics data in the hope of finding predictive patterns for who will develop Alzheimer’s or cancer, among a range of illnesses. For example, the genetic testing firm 23andMe is establishing itself as a valuable partner to biotech companies as it amasses the genetic profiles of millions of people.
As a firm learns more about its customers, it can also broaden the set of customer experiences that it creates. Consider Square, a financial services provider founded in 2009. Square started out by providing small businesses with a lower-cost option for accepting payments via credit cards. Through its Square readers (small electronic devices for swiping cards), Square helps its clients to improve their respond-to-desire strategies. Over time, as Square learned more about the needs of its clients, it created curated offerings that included new features such as tailored dashboards providing information about the end customers and new services such as payroll systems. The information contained in the Square system also allows small businesses to provide more curated offerings to their customers—for instance, via targeted advertising. Lastly, Square has started to offer an automatic execution experience by automatically issuing lines of credit in real time based on the merchant’s cash flow.
As these examples illustrate, population-level learning allows firms to refine their product portfolio in two different ways. First, learning about demand allows a firm to better choose which products it should carry. The second type of portfolio adjustment is more radical. As a firm learns more about its customers, it might get deeper insights into them than any of its suppliers have. These insights might then allow the firm to backward integrate and produce (or direct suppliers to produce) brand-new products. Consider Zalando, one of the largest German online fashion retailers. Zalando started as a copycat of Zappos, the largest online retailer of footwear in the United States. Zalando initially focused on providing a respond-to-desire customer experience. Over time, as Zalando learned more about its customers, and customers were willing to share personal information and fashion preferences, Zalando was able to add curated offering activities, matching individual customers with selected items that are presented to them through the company’s website. Eventually, Zalando was also able to use the data it gathered to start a private-label brand. From searches on its website, Zalando had customer data for which price points and product categories customers rarely used a “brand” filter. Zalando realized that for these products, its customers didn’t care much about the brand name. Therefore, Zalando started to offer its own products in these categories. (See the sidebar for another example of repeat in action.)
Again, we can observe a virtuous cycle, a positive feedback loop. The larger the set of customers a firm serves, the more information it can gather to fine-tune its existing product portfolio through better assortment or the creation of new products. The better its product portfolio, the more likely it is that it can find a good match between the needs of a customer and its product offering. This good match, in turn, leads to customer satisfaction and expands the customer pool, again creating more data.
ASTHMA INHALERS IN THE REPEAT LOOP
Nonadherence to medication is a key cost driver in health care systems around the world. In the United States alone, $100 billion to $300 billion of avoidable health care cost has been attributed to nonadherence. It’s particularly problematic for long-term, chronic diseases where patients are not always symptomatic. For instance, the World Health Organization has estimated that almost half of all prescribed medication for asthma is not being used. This can lead to costly emergency room visits, hospitalizations, and emotional trauma, as many parents can attest if they have endured late-night hospital visits for a child with an acute asthma attack. No wonder many firms are trying to reduce these costs, while providing more value to patients, via connected strategies. Consider the SmartInhaler developed by New Zealand–based Adherium. The SmartInhaler is a Bluetooth-enabled sensor that wraps around the patient’s existing inhaler. The device sends information via an app to the patient, or parents, and health care professionals to track medication adherence. After learning the average usage pattern of a patient, the app also sends the patient reminders or alerts if a dosage is missed. Over time, the app learns more about a patient and can start predicting when asthma may strike, allowing the patient to preempt attacks. The device contains a range of sensors that allow feedback to the user not only about whether the medication has been taken but also about whether the inhaler has been primed correctly and pointed accurately inside the mouth to deliver the full dosage to the respiratory system. Given its information on its population of users, Adherium has been able to provide valuable feedback to AstraZeneca, the maker of the inhaler, to help it redesign the inhaler so that it is more likely to be used correctly. In this case, we can see the repeat dimension in full play. Over time, the app learns more and more about a particular patient, allowing it to improve its coach behavior. Likewise, learning at the population level allows the app to improve its analytics with respect to predicting asthma attacks, thus improving the device over time.
FIGURE 5-3
Population-level learning
Figure 5-3 illustrates this positive feedback loop. Unlike figure 5-2, which was all about finding what is best for one particular customer in order to provide a better curation, metadata enables learning about many customers.
Become a Trusted Partner to the Customer: Recognizing Deeper Needs
As a firm learns more about its customers, it also has the opportunity to move from addressing one or more narrow needs to focusing on more fundamental ones. A narrow need is to learn about compound interest rates. A more fundamental need is to be able to ascertain the value of an investment. More fundamental still is the desire to become an investment adviser. When a learner entrusts her career dreams to Lynda.com, customization can be done at a whole new level because Lynda.com can take a more active role in the connected relationship. Beyond curation, the firm might nudge the learner to keep on top of her homework (coach behavior) or even automatically sign her up for an important job fair.
The distinction between narrow needs and more fundamental ones is also relevant in the field of health care. If a patient feels some heart palpitations, the narrow need is to talk to a cardiologist. More broadly, what this patient wants is to have the health care provider deal with her cardiac problems. Actually, what the patient really wants is for her health care team to provide the right health care when needed. Most fundamentally, what the person wants is for her health care team to keep her healthy. Thus, we can identify a hierarchy of needs in which the current request is an expression of a higher-level, more general need. The promise of connected strategy is that through repeated interaction, a firm is able to move up this hierarchy of needs and embed each user experience in a deeper relationship between the firm and the customer. In doing so, firms can address more fundamental drivers of customer value, increasing a firm’s value proposition.
A helpful approach to discover such deeper relationships by addressing more fundamental needs is the why-how ladder. Figure 5-4 provides an illustration for our cardiology example. Each of the boxes in the why-how ladder corresponds to a specific problem definition. Problem definitions at the bottom of the ladder are more focused, addressing how a need could be fulfilled. We climb up the ladder by asking why. Why is that problem relevant in the first place? Why would it be good to fulfill this customer need?
Going up the why-how
ladder accomplishes two goals. First, it aligns the search for solutions with what the customer really cares about. Again, patients don’t really care that much about their cardiologist; they just want to make sure that their heart is in good shape and, even more broadly, that they are healthy. That’s the most relevant problem for the patient, and whoever provides the solution to this problem is likely to win the competition to serve this patient.
Second, this understanding opens up alternative solution approaches: solving the problem of providing easy access to a busy cardiologist is hard. At this level in the why-how ladder, our solution space is limited to finding more cardiologists and making them work faster or longer hours. But as we go up to the problem of keeping a patient’s heart healthy, there exist many alternative solutions, ranging from changing exercise routines and nutrition to reinforcing medication adherence. For every dollar that we spend, we might be able to improve patient cardiac health by a lot more if we invest in methods to reinforce medication adherence or lifestyle management. This improves efficiency.
FIGURE 5-4
The why-how ladder for cardiology problems
In the eyes of the customer, the purpose of the relationship with our firm is to …
Researchers at the University of Pennsylvania conducted clinical studies on cardiac health and found some interesting results. Prior studies had shown that many of those discharged from the hospital after being treated for major cardiac problems were not willing or able to stay on their medication for longer than six months. Using pill bottles connected to the internet, the Penn research team could quickly detect when patients forgot to take their medications. By automatically hovering over the patient this way, deviations can be detected early and patients can be engaged and trained to form healthy behaviors. The researchers used small financial incentives and peer pressure built through social media to coach patient behavior, nudging them to stay on their medications and lead a healthier lifestyle.
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