Nowadays, new techniques are being developed; however, much work is also devoted to extending and improving existing techniques. All the techniques discussed in this book are available in commercial software products, although there is no single product that incorporates all of them.
How Data Mining Is Being Used Today
This whirlwind tour of a few interesting applications of data mining is intended to demonstrate the wide applicability of the data mining techniques discussed in this book. These vignettes are intended to convey something of the excitement of the field and possibly suggest ways that data mining could be profitably employed in your own work.
A Supermarket Becomes an Information Broker
Thanks to point-of-sale scanners that record every item purchased and loyalty card programs that link those purchases to individual customers, supermarkets are in a position to notice a lot about their customers these days.
Safeway was one of the first U.S. supermarket chains to take advantage of this technology to turn itself into an information broker. Safeway purchases address and demographic data directly from its customers by offering them discounts in return for using loyalty cards when they make purchases. In order
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to obtain the card, shoppers voluntarily divulge personal information of the sort that makes good input for actionable customer insight.
From then on, each time the shopper presents the discount card, his or her transaction history is updated in a data warehouse somewhere. With every trip to the store, shoppers teach the retailer a little more about themselves. The supermarket itself is probably more interested in aggregate patterns (what items sell well together, what should be shelved together) than in the behavior of individual customers. The information gathered on individuals is of great interest to the manufacturers of the products that line the stores’ aisles.
Of course, the store assures the customers that the information thus collected will be kept private and it is. Rather than selling Coca-Cola a list of frequent Pepsi buyers and vice versa, the chain sells access to customers who, based on their known buying habits and the data they have supplied, are likely prospects for a particular supplier’s product. Safeway charges several cents per name to suppliers who want their coupon or special promotional offer to reach just the right people. Since the coupon redemption also becomes an entry in the shopper’s transaction history file, the precise response rate of the targeted group is a matter of record. Furthermore, a particular customer’s response or lack thereof to the offer becomes input data for future predictive models.
American Express and other charge card suppliers do much the same thing, selling advertising space in and on their billing envelopes. The price they can charge for space in the envelope is directly tied to their ability to correctly identify people likely to respond to the ad. That is where data mining comes in.
A Recommendation-Based Business
Virgin Wines sells wine directly to consumers in the United Kingdom through its Web site, www.virginwines.com. New customers are invited to complete a survey, “the wine wizard,” when they first visit the site. The wine wizard asks each customer to rate various styles of wines. The ratings are used to create a profile of the customer’s tastes. During the course of building the profile, the wine wizard makes some trial recommendations, and the customer has a chance to agree or disagree with them in order to refine the profile. When the wine wizard has been completed, the site knows enough about the customer to start making recommendations.
Over time, the site keeps track of what each customer actually buys and uses this information to update his or her customer profile. Customers can update their profiles by redoing the wine wizard at any time. They can also browse through their own past purchases by clicking on the “my cellar” tab. Any wine a customer has ever purchased or rated on the site is in the cellar. Customers may rate or rerate their past purchases at any time, providing still more feedback to the recommendation system. With these recommendations, the web
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site can offer customers new wines that they should like, emulating the way that the stores like the Wine Cask have built loyal customer relationships.
Cross-Selling
USAA is an insurance company that markets to active duty and retired military personnel and their families. The company attributes information-based marketing, including data mining, with a doubling of the number of products held by the average customer. USAA keeps detailed records on its customers and uses data mining to predict where they are in their life cycles and what products they are likely to need.
Another company that has used data mining to improve its cross-selling ability is Fidelity Investments. Fidelity maintains a data warehouse filled with information on all of its retail customers. This information is used to build data mining models that predict what other Fidelity products are likely to interest each customer. When an existing customer calls Fidelity, the phone representative’s screen shows exactly where to lead the conversation.
In addition to improving the company’s ability to cross-sell, Fidelity’s retail marketing data warehouse has allowed the financial services powerhouse to build models of what makes a loyal customer and thereby increase customer retention. Once upon a time, these models caused Fidelity to retain a marginally profitable bill-paying service that would otherwise have been cut. It turned out that people who used the service were far less likely than the average customer to take their business to a competitor. Cutting the service would have encouraged a profitable group of loyal customers to shop around.
A central tenet of customer relationship management is that it is more profitable to focus on “wallet share” or “customer share,” the amount of business you can do with each customer, than on market share. From financial services to heavy manufacturing, innovative companies are using data mining to increase the value of each customer.
Holding on to Good Customers
Data mining is being used to promote customer retention in any industry where customers are free to change suppliers at little cost and competitors are eager to lure them away. Banks call it attrition. Wireless phone companies call it churn. By any name, it is a big problem. By gaining an understanding of who is likely to leave and why, a retention plan can be developed that addresses the right issues and targets the right customers.
In a mature market, bringing in a new customer tends to cost more than holding on to an existing one. However, the incentive offered to retain a customer is often quite expensive. Data mining is the key to figuring out which
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customers should get the incentive, which customers will stay without the incentive, and which customers should be allowed to walk.
Weeding out Bad Customers
In many industries, some customers cost more than they are worth. These might be people who consume a lot of customer support resources without buying much. Or, they might be those annoying folks who carry a credit card they rarely use, are sure to pay off the full balance when they do, but must still be mailed a statement every month. Even worse, they might be people who owe you a lot of money when they declare bankruptcy.
The same data mining techniques that are used to spot the most valuable customers can also be used to pick out those who should be turned down for a loan, those who should be allowed to wait on hold the longest time, and those who should always be assigned a middle seat near the engine (or is that just our paranoia showing?).
Revolutionizing an Industry
In 1988, the idea that a credit card issuer’s most valuable asset is the information it has about its customers was pretty revolutionary. It was an idea that Richard Fairbank and Nigel Morris shopped around to 25 banks before Signet Banking Corporation decided to give it a try.
Signet acquired behavioral data from many sources and used it to build predictive models. Using these models, it launched the highly successful balance transfer program that changed the way the credit card industry works. In 1994, Signet spun off the card operation as Capital One, which is now one of the top 10 credit card issuers. The same aggressive use of data mining technology that fueled such rapid growth is also responsible for keeping Capital One’s loan loss rates among the lowest in the industry. Data mining is now at the heart of the marketing strategy of all the major credit card issuers.
Credit card divisions may have led the charge of banks into data mining, but other divisions are not far behind. At Wachovia, a large North Carolina-based bank, data mining techniques are used to predict which customers are likely to be moving soon. For most people, moving to a new home in another town means closing the old bank account and opening a new one, often with a different company. Wachovia set out to improve retention by identifying customers who are about to move and making it easy for them to transfer their business to another Wachovia branch in the new location. Not only has retention improved markedly, but also a profitable relocation business has developed. In addition to setting up a bank account, Wachovia now arranges for gas, electricity, and other services at the new location.