Промышленный лизинг Промышленный лизинг  Методички 

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common themes emerge. At each company there was someone responsible for the data mining project who truly believed in the power and potential of analytic customer relationship management, often because he or she had seen it in action in other companies. This leader was not usually a technical expert, and frequently did not do any of the actual technical work. He or she functioned as an evangelist to build the data mining team and secure sponsorship for a data mining pilot.

The successful efforts crossed corporate boundaries to involve people from both marketing and information technology. The teams were usually quite small-often consisting of only 4or5 people-yet included people who understood the data, people who understood the data mining techniques, people who understood the business problem to be addressed, and at least one person with experience applying data mining to business problems. Sometimes several of these roles were combined in one person.

In all cases, the initial data mining pilot project addressed a problem of real importance to the organization-one where the value of success would be recognized. Some of the best pilot projects were designed to measure the usefulness of data mining by looking at the results of the actions suggested by the data mining effort.

One of the companies, a wireless service provider, agreed to let us describe its data mining pilot project.

A Controlled Experiment in Retention

In 1996, Comcast Cellular was a wireless phone service provider in a market of 7.5 million people in a three-state area centered around Philadelphia. In 1999, Comcast Cellular was absorbed by SBC and is now part of Cingular, but at the time this pilot study took place, it was a regional service provider facing tough competition from fast-growing national networks. Increasing competition meant that subscribers were faced with many competing offers, and each month a significant proportion of the customer base switched to a competing service. This churn, as it is called in the industry, was very disturbing because even though new subscribers easily outnumbered the defectors, the acquisition cost for a new customer was often in the $500 to $600 range. There is a detailed discussion of churn in Chapter 4.

With even more competitors, poised to enter its market, Comcast Cellular wanted to reach out to existing subscribers with a proactive effort to ensure their continued happiness. The difficulty was knowing which customers were at risk and for what reasons. For any retention campaign, it is important to understand which customers are at risk because a retention offer costs the company money. It doesnt make sense to offer an inducement to customers who are likely to remain anyway. It is equally important to understand what motivates different customer segments to leave, since different retention offers



are appropriate for different segments. An offer of free night and weekend minutes may be very attractive to customers who use their phones primarily to keep in touch with friends, but of little interest to business users.

The pilot project was a three-way partnership between Comcast, a group of data mining consultants (including the authors), and a telemarketing service bureau.

Comcast supplied data and expertise on its own business practices and procedures.

The data mining consultants developed profiles of likely defectors based on usage patterns in call detail data.

The telemarketing service bureau worked with Comcast to use the profiles to develop retention offers for an outbound telemarketing campaign.

This description focuses on the data mining aspect of the combined effort. The goal of the data mining effort was to identify groups of subscribers with an unusually high likelihood to cancel their subscriptions in the next 60 days. The data mining tool employed used a rule induction algorithm similar to decision trees to create segments of high-risk customers described by simple rules. The plan was to include these high-risk customers in telemarketing campaigns aimed at retaining them. The retention offers were to be tailored to different customer segments discovered through data mining. The experimental design allowed for the comparison of three groups:

Group A consists of customers judged by the model to be high risk for whom no intervention was performed.

Group B consists of customers judged by the model to be high risk for whom some intervention was performed.

Group C is representative of the general population of customers.

The study design is illustrated in Figure 18.2. Our hope, of course, was that group A would suffer high attrition compared to groups B and C, proving that both the model and the intervention were effective.

Here the project ran into a little trouble. The first difficulty was that although the project included a budget for outbound telemarketing calls to the people identified as likely to cancel, there was neither budget nor authorization to actually offer anything to the people being called. Another problem was a technical problem in the call center. It was not possible to transfer a dissatisfied customer directly over to the customer service group at the phone company to resolve particular problems outside the scope of the retention effort (such as mistakes on bills). Yet another problem was that although the customer database included a home phone number for each customer, only about 75 percent of them turned out to be correct.



Study Population


Figure 18.2 Study design for the analytic customer relationship marketing test.

In the end, the outbound telemarketing company simply called people from the test and control groups and asked them a series of questions designed to elicit their level of satisfaction and volunteered to refer any problems reported to customer service. Despite this rather lame intervention, 60-day retention was significantly better for the test group than for the control group. Apparently, just showing that the company cared enough to call was enough to decrease churn.

The Data

In the course of several interviews with the client, we identified two sources of data for use in the pilot. The first source was a customer profile database that had already been set up by a database marketing company. This database contained summary information for each subscriber including the billing plan, type of phone, local minutes of use by month, roaming minutes of use by month, number of calls to and from each identified cellular market in the United States, and dozens of other similar fields.

The second source was call detail data collected from the wireless switches. Each time a mobile phone is switched on, it begins a two-way conversation with nearby cell sites. The cell sites relay data from the telephone such as the



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