COMPARISON OF DATA MINING TECHNIQUES FOR DIRECT MARKETING CAMPAINGS

Abstract

The intensive increase in the competition of marketing campaigns over time reduced the impact of them on customer base. Economic pressures, intense competition in the industry, changing lifestyles of people and developing technology have caused marketing managers to adopt the concept of direct marketing by entering into new pursuits. The campaigns prepared in accordance with this understanding might be improved using a variety of data mining techniques. This study compares the performances of artifical neural networks, logistic regression and decision tree data mining techniques on a direct marketing campaign. The purpose of the study is to determine the best target group involved in the campaign by comparing estimation powers of the methods used for determining target groups. Based on the results of this study, it is revealed that artificial neural networks method is more reliable than decision tree and logistic regression analysis about estimating the likely responders in the campaign. This model can improve the efficiency of campaigns by determining of the main features that affect the success of the campaign, identifying the best target group and managing of resources.