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May 13, 2003

Optimizing Customer Insight

When it comes to data, many multichannel retail businesses face an embarrassment of riches. What's missing is timely, actionable insight. Data mining is the centerpiece of an analytics strategy that can deliver business value.

by Usama Fayyad

Understanding customer behavior is important to adjusting business strategies, increasing revenues, and identifying new opportunities. The vital importance of such knowledge to these objectives isn't new; in fact, it's always been a fixture of business success. What's new is that many organizations now have at their disposal an impressive variety of data and information resources that promise to reveal far more about customer behavior than previously thought possible. This potential has unfortunately created an agonizing paradox, especially for large and diverse businesses: the more data resources available, the harder it is for the organization to understand its customers.

Today, detailed customer interaction data is abundant. We have data about browsing behavior, purchase behavior, returns, complaints, wishes, gifts, and more. Yet, how many businesses are truly using this data effectively? The reason for this paradox is that technology for generating, capturing, and storing data has far outpaced the human capacity to understand, analyze, and exploit it for maximum impact. Data mining technology, which focuses on identifying interesting patterns and developing predictive models from data, has the greatest potential for enabling businesses to leverage data resources for strategic business success.

The Retail Challenge

Nowhere is the paradox of rich data and poor utilization more apparent then in a multichannel retail environment. Initial data-mining efforts in the online domain focused primarily on site statistics and transaction logs, analyzing such factors as how many hits a site received, which pages visitors viewed, how long customers stayed at the site, what they purchased, and how much they paid. Such Web site analytics are fine as aggregate statistics, but they don't go nearly far enough to help retailers get the most value out of customer interactions.

To obtain a truly comprehensive view of how customers interact with your store, you also need to combine online data with data from other sources, such as demographic information and records of in-store and catalog purchases. You must also be able to segment customers according to a variety of criteria, and then analyze the specific behaviors of each segment. Finally, you need an efficient means of turning these insights into action — for example, creating promotions, campaigns, and "related items" recommendations that target particular groups of customers. With such a comprehensive set of capabilities, you can realize the ultimate benefit of data mining: gaining in-depth customer insights and acting on them to increase customers' purchases — and revenues for your business.

The first step I'll address in this article is to consider the different types of customer data that can be gathered and the various stages that retailers must go through to learn how to take better advantage of data-mining technology. Next, I'll describe how you can harness advanced analytics to optimize customer interactions and improve your bottom line. Finally, I'll discuss what's required to take full advantage of the insights gained from data mining.

What Data Do You Need?

For most retail environments today, three sources of customer data are most critical to data mining efforts toward better understanding of behavior.

Demographic data. Direct marketers have employed data about age, geographical location, and income for many years to target specific groups of customers. The goal has been to use this data to aim promotional campaigns at groups with particular interests.

Transaction data. This resource provides concrete data about what your customers are purchasing. Going beyond general demographic information, transaction data is essential in helping you predict future purchases and target promotional campaigns more effectively. In addition to the value of the transaction itself, this data also reveals key information about time, location, and other factors related to the transaction.

Online interaction data. The dominant form of data here is Internet clickstream data, although we must also include interactions that occur through wireless devices, cable television, and more. This resource can provide deeper information than transaction data; it provides a window on customers' decision processes and the navigational steps they took to find what they desired. Online interaction data records every page that the customer saw leading up to a decision. Therefore, you know not only what they purchased (or didn't purchase), but you also have strong evidence of how they arrived at that decision.

By better understanding the process leading up to the buy decision, you can more effectively influence future purchase decisions. For instance, suppose you feature a particular product on your home page. However, when customers click through to look at it, they end up buying a different product that they found through a link from the featured product's page. That tells you you're featuring the wrong product on your home page. Or, it might confirm that the featured product is indeed a good candidate for a "loss leader."