An objective and data-based market segmentation is a precondition for
efficient targeting in direct marketing campaigns. The role of customer
segments classification in direct marketing is to predict the segment of
most valuable customers who is likely to respond to a campaign based on
previous purchasing behavior. A good-performing predictive model can
significantly increase revenue, but also, reduce unnecessary marketing
campaign costs. As this segment of customers is generally the smallest, most
classification methods lead to misclassification of the minor class. To
overcome this problem, this paper proposes a class balancing approach based
on Support Vector Machine-Rule Extraction (SVM-RE) and ensemble learning.
Additionally, this approach allows for rule extraction, which can describe
and explain different customer segments. Using a customer base from a
company?s direct marketing campaigns, the proposed approach is compared to
other data balancing methods in terms of overall prediction accuracy, recall
and precision for the minor class, as well as profitability of the campaign.
It was found that the method performs better than other compared class
balancing methods in terms of all mentioned criteria. Finally, the results
confirm the superiority of the ensemble SVM method as a preprocessor, which
effectively balances data in the process of customer segments classification
Class balancing in customer segments classification using support vector machine rule extraction and ensemble learning
