Data Mining throughout the Customer Life Cycle

The purpose of data mining is to help businesses realize value from their most important asset: customers. Earlier chapters have talked about the algorithms and methodology for making data mining successful. This chapter turns from the specific technology to customers. The next three chapters continue this theme, stepping away from technical algorithms to talk about the data and the systems environment needed to exploit data mining.

For almost any business, customers are the critical asset. Yet, they are elusive, because of the wide variety of different relationships that change over time. Different industries have different definitions of customers. Within an industry, different competitors have different approaches to managing these relationships. Some focus on quality of service, some on convenience, some on price, and some on other aspects of the relationship. No two businesses have exactly the same definition of a customer, nor treat customers the same way throughout the relationship.

The purpose of data mining is to complement other customer service initiatives, not to replace them. Customer interactions take place through many channels-through direct mail pieces, through call centers, face-to-face, via advertising. Now that the click and mortar way of doing business is becoming standard, most businesses provide an online interface to their customers. The Web, with its new capabilities for interacting with customers, has the potential to provide a wealth of customer behavior data that can be turned into a new window on the customer relationship. It is ironic that a technology that

has largely replaced human-to-human interactions is allowing companies to treat their customers more personally.

This brings us back to the customer and to the customer life cycle. This chapter strives to put data mining into focus with the customer at the center. It starts with an overview of different types of customer relationships, then goes into the details of the customer life cycle as it relates to data mining. The chapter provides examples of how customers are defined in various industries and some of the issues in deciding when the customer relationship begins and when it ends. The focal point is the customer and the ongoing relationship that customers have with companies.

Levels of the Customer Relationship

One of the major goals of data mining is to understand customers and the relationships that customers have with an organization. A good place to start understanding them better is by using the different levels of customer relationships and what customers are telling us through their behavior.

Customers generate a wealth of behavioral information. Every payment made, every call to customer service, every click on the Web, every transaction provides information about what each customer does, and when, and which interventions are working and which are not. The Web is a particularly rich source of information. CNN does not know who is viewing or paying attention to their cable news program. The New York Times does not know which parts of the paper each subscriber reads. On the Web, though, cnn.com and nytimes.com have a much better indication of readers interests. Connecting this source of information back to individuals over time is challenging (not to mention the challenge of connecting readers interests to advertising over time).

Customers are not all created equal. Nor should all customers be treated equally, since some are clearly more valuable than others. Figure 14.1 shows a continuum of customer relationships, from the perspective of the amount of investment worthy of each relationship. Some customers merit very deep and intimate relationships centered around people. Other customers are too numerous and, individually, not valuable enough to maintain individual relationships. For this group, we need technology to help make the relationship more intimate. The third group is perhaps the most challenging, because they are in between those who merit real intimacy and those who merit feigned intimacy. This group often includes small businesses as well as indirect relationships. The sidebar No Customer Relationship talks about another situation, companies that do not know about their end users and do not need to.

Consumers Very small Small and medium Large businesses

(low intimacy) businesses businesses (deep intimacy)

Many customers Few customers

Each small contribution to profit Each large contribution to profit

Very important in aggregate Intimacy Important individual and in aggregate

Technologies: Technologies:

Mass intimacy Sales force automation

Customer relationship management Account management support

Figure 14.1 Intimacy in customer relationships generally increases as the size of the account increases.

Deep Intimacy

Customers who are worth a deep intimate relationship are usually large organizations-business customers. These customers are big enough to devote dedicated resources, in the form of account managers and account teams. The relationship is usually some sort of business-to-business relationship. One-off products and services characterize these relationships, making it difficult to compare different customers, because each customer has a set of unique products.

An example is the branding triumvirate of McDonalds, Coca-Cola, and Disney. McDonalds is the largest retailer of Coke products worldwide. When Disney has special promotions in fast food restaurants for childrens movies, McDonalds gets first dibs at distributing the toys inside their Happy Meals. And when Disney characters (at least the good guys!) drink soda or open the refrigerator-Coke products are likely to be there. Coke also has exclusive arrangements with Disney, so Disney serves Coke products at its theme parks, in its hotels, and on its cruises. There are hundreds of people working together to make this branding triumvirate work. Data mining, with even the most advanced algorithms on even the fastest computers, is not going to replace these people-nor will this process be automated in the conceivable future.

On the other hand, even large account teams and individual managers can benefit from analysis, particularly around sales force automation tools. Data mining analysis can help such groups work better, by providing an understanding of what is really going on. Data can still help find some useful answers: which McDonalds are particularly good at selling which soft drinks? Where are product placements resulting in higher sales? What is the relationship between weather and drink consumption at theme parks versus hotels? And so on.