Don’t Get Buried in Customer Data—Use It

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.

Not surprisingly, many executives’ faith in CRM has waned. 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.

Why You Need Both Individual And Aggregated Data

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.