Using Data Mining Methods to Build Customer Profiles

P ersonalization—the ability to provide content and services tailored to individuals on the basis of knowledge about their preferences and behavior 1 —has become an important marketing tool (see the " Personalization " sidebar). Personalization applications range from per-sonalized Web content presentations to book, CD, and stock purchase recommendations. Among issues the personalization community must deal with, the following are of special importance: how to provide personal recommendations based on a comprehensive knowledge of who customers are, how they behave, and how similar they are to other customers; and how to extract this knowledge from the available data and store it in customer profiles. Various recommender systems address the recommendation problem. 2 Most use either the collabora-tive-filtering 3-5 or the content-based 6 approach (see sidebar). Some systems integrate the two methods. 2,6 To address the second issue, we have developed an approach that uses information learned from cus-tomers' transactional histories to construct accurate, comprehensive individual profiles. 7 One part of the profile contains facts about a customer, and the other part contains rules describing that customer's behavior. We use data mining methods to derive the behav-ioral rules from the data. We have also developed a method for validating customer profiles with the help of a human domain expert who uses validation operators to separate " good " rules from " bad. " We have implemented the profile construction and validation methods in a system called 1:1Pro. Our approach differs from other profiling methods in that we include personal behavioral rules in customer profiles. 7 We can judge the quality of rules stored in customer profiles in several ways. We might call rules " good " because they are statistically valid, acceptable to a human expert in a given application, or effective in that they result in specific benefits such as better decision making and recommendation capabilities. Here, we focus on the first two aspects: statistical validity and acceptability to an expert. As Figure 1 illustrates, the two main phases of the profile-building process are rule discovery and validation. Our method of building personalized customer profiles begins with collecting the data. Applications use various kinds of data about individual customers. Many applications classify the data into two basic types: factual—who the customer is— and transactional—what the customer does. For example, in a marketing application based on customers' purchasing histories, the factual data includes demographic information such as name, gender , birth …