possible to identify these customers within days of their first overcall. With this information, the customer service center could contact at-risk customers and move them onto appropriate billing plans even before the first bill went out. This simple system was a big win for data mining, simply because having a data mining group-with the skills, hardware, software, and access-was the enabling factor for putting together this triggering system.

Take Action

Taking action is the purpose of the virtuous cycle of data mining. As already mentioned, action can take many forms. Data mining makes business decisions more informed. Over time, we expect that better-informed decisions lead to better results.

Actions are usually going to be in line with what the business is doing anyway:

Sending messages to customers and prospects via direct mail, email, telemarketing, and so on; with data mining, different messages may go to different people

Prioritizing customer service

Adjusting inventory levels

And so on

The results of data mining need to feed into business processes that touch customers and affect the customer relationship.

Measuring Results

The importance of measuring results has already been highlighted. Despite its importance, it is the stage in the virtuous cycle most likely to be overlooked. Even though the value of measurement and continuous improvement is widely acknowledged, it is usually given less attention than it deserves. How many business cases are implemented, with no one going back to see how well reality matched the plans? Individuals improve their own efforts by comparing and learning, by asking questions about why plans match or do not match what really happened, by being willing to learn that earlier assumptions were wrong. What works for individuals also works for organizations.

The time to start thinking about measurement is at the beginning when identifying the business problem. How can results be measured? A company that sends out coupons to encourage sales of their products will no doubt measure the coupon redemption rate. However, coupon-redeemers may have purchased the product anyway. Another appropriate measure is increased sales in

particular stores or regions, increases that can be tied to the particular marketing effort. Such measurements may be difficult to make, because they require more detailed sales information. However, if the goal is to increase sales, there needs to be a way to measure this directly. Otherwise, marketing efforts may be all sound and fury, signifying nothing.

Standard reports, which may arrive months after interventions have occurred, contain summaries. Marketing managers may not have the technical skills to glean important findings from such reports, even if the information is there. Understanding the impact on customer retention, means tracking old marketing efforts for even longer periods of time. Well-designed Online Analytic Processing (OLAP) applications, discussed in Chapter 15, can be a big help for marketing groups and marketing analysts. However, for some questions, the most detailed level is needed.

It is a good idea to think of every data mining effort as a small business case. Comparing expectations to actual results makes it possible to recognize promising opportunities to exploit on the next round of the virtuous cycle. We are often too busy tackling the next problem to devote energy to measuring the success of current efforts. This is a mistake. Every data mining effort, whether successful or not, has lessons that can be applied to future efforts. The question is what to measure and how to approach the measurement so it provides the best input for future use.

As an example, lets start with what to measure for a targeted acquisition campaign. The canonical measurement is the response rate: How many people targeted by the campaign actually responded? This leaves a lot of information lying on the table. For an acquisition effort, some examples of questions that have future value are:

Did this campaign reach and bring in profitable customers?

Were these customers retained as well as would be expected?

What are the characteristics of the most loyal customers reached by this campaign? Demographic profiles of known customers can be applied to future prospective customers. In some circumstances, such profiles should be limited to those characteristics that can be provided by an external source so the results from the data mining analysis can be applied purchased lists.

Do these customers purchase additional products? Can the different systems in an organization detect if one customer purchases multiple products?

Did some messages or offers work better than others?

Did customers reached by the campaign respond through alternate channels?

All of these measurements provide information for making more informed decisions in the future. Data mining is about connecting the past-through learning-to future actions.

One particular measurement is lifetime customer value. As its name implies, this is an estimate of the value of a customer during the entire course of his or her relationship. In some industries, quite complicated models have been developed to estimate lifetime customer value. Even without sophisticated models, shorter-term estimates, such as value after 1 month, 6 months, and 1 year, can prove to be quite useful. Customer value is discussed in more detail in Chapter 4.

Data Mining in the Context of the Virtuous Cycle

A typical large regional telephone company in the United States has millions of customers. It owns hundreds or thousands of switches located in central offices, which are typically in several states in multiple time zones. Each switch can handle thousands of calls simultaneously-including advanced features such as call waiting, conference calling, call-forwarding, voice mail, and digital services. Switches, among the most complex computing devices yet developed, are available from a handful of manufacturers. A typical telephone company has multiple versions of several switches from each of the vendors. Each of these switches provides volumes of data in its own format on every call and attempted call-volumes measured in tens of gigabytes each day. In addition, each state has its own regulations affecting the industry, not to mention federal laws and regulations that are subject to rather frequent changes. And, to add to the confusion, the company offers thousands of different billing plans to its customers, which range from occasional residential users to Fortune 100 corporations.

How does this company-or any similar large corporation-manage its billing process, the bread and butter of its business, responsible for the majority of its revenue? The answer is simple: Very carefully! Companies have developed detailed processes for handling standard operations; they have policies and procedures. These processes are robust. Bills go out to customers, even when the business reorganizes, even when database administrators are on vacation, even when computers are temporarily down, even as laws and regulations change, and switches are upgraded. If an organization can manage a process as complicated as getting accurate bills out every month to millions of residential, business, and government customers, surely incorporating data mining into decision processes should be fairly easy. Is this the case?

Large corporations have decades of experience developing and implementing mission-critical applications for running their business. Data mining is different from the typical operational system (see Table 2.1). The skills needed for running a successful operational system do not necessarily lead to successful data mining efforts.

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