serial number and phone type to a central switching office. Computers at the switching office figure out which cell site the phone should be talking to at the moment and send a message back to the phone telling it which cell it is using and what frequency to tune to.

When the subscriber enters a phone number and presses the send button, the number is relayed to the central switch, which in turn sets up the call over regular land lines or relays it to the cell closest to another wireless subscriber. Every switch generates a call detail record that includes the subscriber ID, the originating number, the number called, the originating cell, the call duration, the call termination reason, and so on. These call detail records were used to generate a behavioral profile of each customer, including such things as the number of distinct numbers called and the proportion of calls by time of day and day of week.

The pilot project used 6 months of data for around 50,000 subscribers some of whom canceled their accounts and some of whom did not. Our original intention was to merge the two data sources so that a given subscribers data from the marketing database (billing plan, tenure, type of phone, total minutes of use, home town, and so on) would be linked to the detail records for each of his or her calls. That way, a single model could be built based on independent variables from both sources. For technical reasons, this proved difficult, so due to time and budgetary constraints we ended up building two separate models, one based on the marketing data and one based on call detail data.

The marketing data was already summarized at the customer level and stored in an easily accessible database system. Getting the call detail data into a usable form was more challenging. Each switch had its own collection of reel-to-reel tapes like the ones used to represent computers in 1960s movies. These tapes were continuously recycled so that a 90-day moving window was always current with the tapes from 90 days earlier being used to record the current days calls. Since eight tapes were written every day, we found ourselves looking at over 700 tape reels, each of which had to be loaded individually by hand into a borrowed 9-track tape drive. Once loaded, the call detail data, which was written in an arcane format unique to the switching equipment, needed extensive preprocessing in order to be made ready for analysis. The 70 million call detail records were reduced to 10 million by filtering out records that did not relate to calls to or from the churn model population of around.

Even before predictive modeling began, simple profiling of the call detail data suggested many possible avenues for increasing profitability. Once call detail was available in a queryable form, it became possible to answer questions such as:

Are subscribers who make many short calls more or less loyal than those who make fewer, longer calls?

Do dropped calls lead to calls to customer service?

Team-Fly®

What is the size of a subscribers calling circle for both mobile-to-mobile and mobile-to-fixed-line calling?

How does a subscribers usage vary from hour to hour, month to month, and weekday to weekend?

Does the subscriber call any radio station call-in lines?

How often does a subscriber call voice mail?

How often does a subscriber call customer service?

The answers to these and many other questions suggested a number of marketing initiatives to stimulate cellular phone use at particular times and in particular ways. Furthermore, as we had hoped, variables built around measures constructed from the call detail, such as size of calling circle, proved to be highly predictive of churn.

The Findings

Data mining isolated several customer segments at high risk for churn. Some of these were more actionable than others. For example, it turned out that subscribers who, judging by where their calls entered the network, commuted to New York were much more likely to churn than subscribers who commuted to Philadelphia. This was a coverage issue. Customers who lived in the Comcast coverage area and commuted to New York, found themselves roaming (making use of another companys network) for most of every work day. The billing plans in effect at that time made roaming very expensive. Commuters to Philadelphia remained within the Comcast coverage area for their entire commute and work day and so incurred no roaming charges. This problem was not very actionable because neither changing the coverage area nor changing the rules governing rate plans was within the power of the sponsors of the study, although the information could be used by other parts of the business.

A potentially more actionable finding was that customers whose calling patterns did not match their rate plan were at high risk for churn. There are two ways that a customers calling behavior may be inappropriate for his or her rate plan. One segment of customers pays for more minutes than they actually use. Arguably, a wireless company might be able to increase the lifetime value of these customers by moving them to a lower rate plan. They would be worth less each month, but might last longer. The only way to find out for sure would be with a marketing test. After all, customers might accept the offer to pay less each month, but still churn at the same rate. Or, the rate of churn might be lowered, but not enough to make up for the loss in near-term revenue.

The other type of mismatch between calling behavior and rate plan occurs when subscribers sign up for a low-cost rate plan that does not include many minutes of use and find themselves frequently using more minutes than the

plan allows. Since the extra minutes are charged at a high rate, these customers end up paying higher bills than they would on a more expensive rate plan with more included minutes. Moving these customers to a higher-rate plan would save them some money, while also increasing the amount of revenue from the fixed portion of their monthly bill.

The Proof of the Pudding

Comcast was able to make a direct cost/benefit analysis of the combined data mining and telemarketing action plan. Armed with this data, Comcast was able to make an informed decision to invest in future data mining efforts. Of course, the story does not really end there; it never does.

The company was faced with a whole new set of questions based on the data that comes back from the initial study. New hypotheses were formed and tested. The response data from the telemarketing effort became fodder for a new round of knowledge discovery. New product ideas and service plans were tried out. Each round of data mining started from a higher base because the company knew its customers better. That is the virtuous cycle of data mining.

Lessons Learned

In a business context, the successful introduction of data mining requires using data mining techniques to address a real business challenge. For companies that are just getting started with analytical customer relationship management, integrating data mining can be a daunting task. A proof-of-concept project is a good way to get started. The proof of concept should create a solid business case for further integration of data mining into the companys marketing, sales, and customer-support operations. This means that the project should be in an area where it is easy to link improved understanding gained through data mining with improved profitability.

The most successful proof-of-concept projects start with a well-defined business problem, and use data related to that problem to create a plan of action. The action is then carried out in a controlled manner and the results carefully analyzed to evaluate the effectiveness of the action taken. In other words, the proof of concept should involve one full trip around the virtuous cycle of data mining. If this initial project is successful, it will be the first of many. The primary lesson from this chapter is also an important lesson of the book as a whole: data mining techniques only become useful when applied to meaningful problems. Data mining is a technical activity that requires technical expertise, but its success is measured by its effect on the business.