Detecting the change of customer behavior based on decision tree analysis

Understanding and adapting to changes in customer behavior is an important aspect for survival in a continuously changing environment. This paper develops a methodology based on decision tree analysis to detect the change in classified customer segments automatically between two data sets collected over time. We first define three types of changes as the emerging pattern, the unexpected change and the added/perished rule. Then, similarity and difference measures are developed for rule matching to detect all types of change. Finally, the degree of change is developed to evaluate the amount of change. Our suggested methodology based on decision tree analysis in the change detection problem can be used in more structured situations in which the manager has a specific research question and it also detects the change of classification criteria in a dynamically changing environment. A Korean Internet shopping mall case is evaluated to represent the performance of our suggested methodology, and practical business implications for this methodology are also provided. We believe that the change detection problem and the suggested methodology will become increasingly important as more data mining applications are implemented.