NOTE: reprinted with permission from “Do You Really Know What to Do with Your Customer Data?”, June 2003.
With the advent of customer relationship management (CRM) in the late 1990s, companies came to believe that by using technology to tailor their offerings to individual consumers’ needs, customer loyalty–and company profits–would skyrocket.
But in today’s crowded marketplace, customer loyalty is more elusive than ever.
A recent McKinsey study reveals that the annual churn in the wireless industry increased from 17 percent in 1995 to 32 percent in 2000. This trend holds true even in industries less susceptible to turnover. In core retail categories such as department stores, for instance, the top players’ market share declined more than 10 percent.
In a 2001 Bain & Co. survey of the 25 most popular management tools, CRM was ranked near the bottom. In a follow-up study, 20 percent of the 451 senior executives polled said that their companies’ CRM initiatives had failed to deliver profitable growth and had damaged long-term customer relationships.
Tempting as it may be to point the finger at your CRM technology, that won’t help you reverse these worrisome trends. It’s quite possible that the problem isn’t with your CRM technology at all but with the way you are collecting and using your data, experts say. Although getting your CRM program in order is an essential component of achieving customer loyalty, there’s much more that you need to do.
“Marketers need a good, thoughtful architecture to base their decisions on,” says Harvard Business School marketing professor Gerald Zaltman. A more strategic approach to data mining can provide the foundation for that decision-making architecture.
Below, advice on how to use information about the individual customer and the average customer in concert, and how to probe beneath customer preferences and behaviors to uncover the attitudes that provide a more solid understanding of customer loyalty.
One-to-one marketing, a term coined by Don Peppers and Martha Rogers in their influential 1993 book, The One to One Future (Currency/Doubleday), focuses on share of customer: Using the insights about what makes your most loyal customers different to maximize the value of those relationships. By the end of the decade, many marketers had come to believe that the combination of mass customization techniques, sophisticated database software, and the Internet would enable them to actually deliver on the promise of customized offerings to each individual customer.
But that hasn’t happened to the extent it should have, says Cleveland-based consultant James H. Gilmore, coauthor with B. Joseph Pine II of The Experience Economy (Harvard Business School Press, 1999), because “most practitioners have taken the concept of one-to-one marketing and bastardized it into CRM. They’re using CRM tools to design better processes for a nonexistent ‘average’ customer, instead of customizing for individual customers.”
He cites the example of a major hotel chain that asks guests to complete a multiple-question satisfaction survey via their room’s TV set during their stay. When one guest answered “extremely dissatisfied” to all the questions, he was not treated any differently when he checked out. Why? Because his answers went straight to a central repository where they were aggregated with other customers’ responses and used to measure overall market—not customer—satisfaction. A more effective approach would be to feed his answers directly to someone at the front desk who could respond immediately to his needs and create a better experience for him.
“A company’s goal should be to learn more about what each customer needs so that it can close the customer sacrifice gap, which is the difference between what individual customers settle for and what each wants exactly,” says Gilmore. Steve Cunningham, director of customer listening at Cisco, agrees that it’s vital to listen and respond to individual customer needs and preferences. But he believes you must also pay attention to the aggregate data—customer averages based on individual surveys.
“Let’s say that based on the customer survey averages, you realize that your hotel is taking too long to check guests out,” he says. “So you launch initiatives designed to reduce checkout time and prime your personnel to be sensitive to that issue. Despite these efforts, something goes wrong, and one morning the front desk manager sees a long line of guests queued up to check out. Because the survey averages have helped sensitize him to the importance of this issue, he knows he has to do something—for example, pull staff members off other jobs so they can help check people out, or offer free coffee to everyone who’s standing in line.”
Cisco relies on three layers of customer data to inform its efforts to improve customer satisfaction: The overall satisfaction survey that customers fill out annually; interviews with targeted customer segments, follow-on surveys, and sessions with corporate advisory boards that seek to identify an initiative that will address a problem hinted at in the overall relationship survey (“this is the ‘digging and understanding’ layer,” says Cunningham); and, at the most granular level, records of each individual transaction that the company’s technical support group has with a customer.
To illustrate how Cisco uses these three layers, Cunningham cites a hypothetical example. Assume that for a given year, the average score for product reliability has slipped a bit. Drilling down to the bottom two layers of data, Cisco discovers a problem with the power supply for its routers. It launches an initiative to solve this problem and identifies the number of spare power supply parts it sends out weekly as the measure it will use to track the progress. The transactional measure—the number of spare parts shipped weekly—may start to come down fairly soon after the initiative has been launched, but it may take a while before the change shows up on the annual relationship survey.
“You need both the aggregate and the transactional information,” says Cunningham. “The survey data tells you about the overall health of your relationships with customers; it tells you which way the wind is blowing. It also helps prevent you from running after individual problems that may not be significant in the aggregate. The transactional data gives the detail behind the relationship.” It helps you pinpoint specific issues that need to be addressed to boost overall customer satisfaction. Digging deeper
To boost customer satisfaction and, ultimately, customer loyalty, you have to do more than listen simultaneously to customer averages and to individual customers. You also have to look for what lies beneath the externals of customers’ behavior (what they buy, how they buy, and when they buy). “Without capturing what is going on inside customers’ minds and hearts, and integrating that information with the factual external experiences, the picture is incomplete,” says Doug Grisaffe, chief research methodologist for Indianapolis-based Walker Information.
“CRM tools enable you to collect a lot of rich data about a customer’s frequency and time of purchase, the size of her orders, and what she thinks of your company,” says Harvard’s Zaltman. That’s necessary but not sufficient data: It doesn’t tell you anything about “why customers do what they do, think what they think, and why they like or don’t like your products. Getting that level of insight requires more intensive interactions with customers than CRM tools permit.” It requires that you develop a “poetic insight into customers—a deep knowledge that enables you to intuit their answers to questions you haven’t even asked them.”
In one-on-one interviews with customers, Zaltman uses a process he describes as metaphor elicitation to get at the beliefs, emotions, intentions, and often unconscious attitudes that people have about a product or brand. As he explains in his recent book, How Customers Think: Essential Insights into the Mind of the Market (Harvard Business School Press, 2003), the information gleaned from these interviews as well as from surveys and observation is used to create a consensus map—an illustration of the particular bundles of constructs that customers have developed based on their experience and emotional connection with a product or brand.
A consensus map that Zaltman developed for General Motors reveals the richness of the metaphor elicitation approach. As expected, customers associated GM products with quality and competitive price. But there was more: Customers also linked GM with patriotic feelings. By buying GM cars, they saw themselves as not simply helping Americans keep their jobs, but as fulfilling a larger obligation that they felt toward their country.
Based on the consensus map Zaltman produced, GM’s domestic managers redesigned the customer experience at dealerships and added subtle cues in their advertising to make the idea of patriotism more salient. For GM’s overseas managers, the task was more difficult but no less valuable for that. Realizing that GM products also produced patriotic associations among foreign purchasers, the overseas managers “found cues that underscored patriotic associations with the local country without pressing the American button,” says Zaltman.
Reams of customer data are no guarantee that you’ll be able to increase your most profitable customers’ loyalty—you have to be sure that you’re collecting the most relevant information. Listening for the attitudes that inform customers’ behaviors and preferences, Zaltman maintains, gives you “a more solid basis on which to craft and implement strategies that will improve customer loyalty.”