Why the difference between customers' circumstances and their behavior is important?

[shuijian-xu]

Most of the time in data warehousing, we have been analyzing behavior. The Fact table in a traditional dimensional schema usually contains information about a customer's interaction with our business. That is: the way they behave toward us. In the Wine Club example we have been using, the Fact table contained information about sales. This, as has been shown, is the normal approach toward the development of data warehouses.

[shuijian-xu]

The abrupt change in behavior is the effect of the change in circumstances.

[shuijian-xu]

The abrupt change in behavior is the effect of the change in circumstances.

[shuijian-xu]

Now let us look again at one of the most pressing business problems, that of customer loyalty and its direct consequence, that of customer churn. For the moment let us put ourselves in the place of a customer of a cellular phone company and think of some reasons why we, as a customer, may decide that we no longer wish to remain as a customer of this company:

1. Perhaps we have recently moved to a different area. Maybe the new area has a poor reception for this particular company.

2. We might have moved to a new employer and have been given a mobile phone as part of the deal, making the old one surplus to requirements.

3. We could have a child just starting out at college. The costs involved might require economies to be made elsewhere, and the mobile phone could be the luxury we can do without.

Each of the above situations could be the cause for us as customers to appear in next month's churn statistics for this cellular phone company. It would be really neat if the phone company could have predicted that we are a high-risk customer. The only way to do that is to analyze the information that we have gathered and apply some kind of predictive model to the data that yields a score between, say, 1 for a very low risk customer to 10 for a very high risk customer.

But what type of information is likely to give us the best indication of a customer's propensity to churn? Remember that, traditionally, data warehouses tend to be organized around behavioral systems. In a mobile telephone company, the most commonly used behavioral information is the call usage. Call usage provides information about:

Types of calls made (local, long distance, collect, etc.)

1. Durations of calls

2. Amount charged for the call

3. Time of day

4. Call destinations

5. Call distances

If we analyze the behavior of customers in these situations, what do you think we will find? I think we can safely predict that, just before the customer churned, they stopped making telephone calls! The abrupt change in behavior is the effect of the change in circumstances.

The cause-and-effect principles can be applied quite elegantly to the serious problem of customer churn and, therefore, customer loyalty. What we are seeing when we analyze behavior is the effect of some change in the customer's circumstances. The change in circumstances, either directly or indirectly, is the cause of their churning. If we analyze their behavior, it is simply going to tell us something that we already know and is blindingly obvious–the customer stopped using the phone. By this time it is usually far too late to do anything about it.

In view of the fact that most dimensional data warehouses measure behavior, it seems reasonable to conclude that such models may not be much help in predicting those customers that we are at risk of losing. We need to turn our attention to being very much more rigorous in our approach to tracking changes in circumstances, rather than behavior. Thus, the second-generation data warehouses that are being built as an aid to the development of CRM applications need to be able to model more than just behavior.