Customer Profiling (guest author: Paul Cook)

Profiling is a data mining technique used to find patterns and trends in customer data. In today's post we will explore the application of this technique.

As we will demonstrate in this article, profiling is both simple and powerful. Its great strength is its simplicity. It is ideal for communicating large amounts of information in a user-friendly way. Profiles are clear, comprehensive, and easy to read, which makes them ideal for communicating with a business or non-technical audience.

Profiling describes a group of people by summarizing information about them. Profiles are typically used to answer questions like:
  • What do my customers look like?
  • Which prospects are most likely to buy?
  • What drives customer churn?

Often, a profile is all you need to answer these questions. Other times, you may choose to use a profile for exploratory data analysis before multivariate modelling.

Profiling is often used to find hot prospects for marketing campaigns. By comparing past purchasers and non-purchasers, you can see what's different about the purchasers. These differences can be combined into a statistical model, and the effectiveness of the model measured using gain and lift charts. When building a model, a profile is used to show the effect of the variables included in the analysis.

Today's tutorial was created using a profiling add-on for IBM SPSS Statistics, though you can also build profiles in Excel if you prefer. In today's tutorial, we will start with the most basic type of profile, and build up to the more sophisticated applications.

The simplest form of profile describes one group of customers, like the example below.

This is, of course, a greatly simplified example. Real profiles may contain dozens or even hundreds of variables, and thousands to millions of customers. Nonetheless, from this example, it is immediately apparent that this business's base is skewed towards older, affluent, male customers.

The above example contains just counts of customers. Often, you would want to examine other statistics too, like counts, averages, maximums, standard deviations, and so on. The next example presents the same customers, but this time showing both sample sizes (N) and average profit per customer (MEAN).

We saw before that this customer group tends to be older, affluent, and male. But when we look at the profitability figures, see how the most profitable customers tend to be less affluent and female. This raises questions for the business's marketing strategy. Could the business increase profitability by targeting women and the less affluent market sectors?

A common use of profiling is to compare and contrast one customer group against another—often comparing a customer segment to the entire customer base, to see what's different and special about the segment.

In the profile above, the customer base has been split into three equal-sized groups of 10,000 each, and a customer segment is compared using an index, which is also graphed as a bar chart. This shows that the segment being investigated is skewed towards new customers who have purchased recently and made multiple purchases.

Comparisons like these are perfect for answering ad hoc questions like "what's different about high-value customers?" or "how do recent recruits differ from existing customers?" They are also perfect when using clustering to create new segmentation schemes.

Marketers are always searching for market segments that are highly responsive to their offers and promotions. To find these responsive segments, profiles of response rates are used, as shown below.

In this example, z-scores have been calculated to highlight particularly high or low response rates. The profile above shows that the number of cars a person owns is strongly associated with response: the more cars a person owns, the more likely they are to respond.

Using exactly the same technique, you can profile response rates, cross-sell rates, attrition rates, and so on.

As we have seen, profiling is both beautifully simple and extremely powerful. And best of all, it can now be done in seconds using automated profiling tools.


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