A Comparison of Logistic Regression, k-Nearest Neighbor, and Decision Tree Induction for Campaign Management

Extensive research has been performed to develop appropriate machine learning techniques for different data mining problems. However, previous work has shown that no learner is generally better than another learner. Comparing machine learning methods depends very much on the characteristics of a particular data set and the requirements of the respective business domain. Direct marketing is an important task in marketing departments, and one where machine learning techniques have been used repetedly. A systematic comparison of classifier performance can achieve considerable gains in marketing effectiveness. This case study provides an assessment of the predictive performance of different classification methods for campaign management. The evaluation of data mining methods for marketing campaigns has special requirements. Whereas, typically the overall performance is an important selection criteria, for campaign management it is more important to select the technique which performs best on the first few quantiles. This study selects candidate techniques and relevant evaluation criteria for campaign management and provides a guideline for similar comparison studies.