Decoding Customer Behaviour: Relevance of Web and Purchasing Behaviour in Predictive Response Modeling

Abstract

In this paper, an approach is presented to improve the online direct marketing process regarding predictive customer response modeling. Namely, customer response models are usually faced with a class imbalance problem, due to a low conversion rate, in relation to the entire number of targeted offers. To avoid the bias towards the negative class in machine learning process, this paper proposes a combination of random undersampling and Support Vector Machine method for data pre-processing and increasing the predictive performances of subsequently used classifiers – Decision Tree and Random Forest. In addition, different attribute groups are tested in terms of their influence to the model performance, which enables marketing decisions makers to better understand which attributes have the highest impact in determining the customer’s response to a direct marketing campaign. The results showed that the proposed method successfully solved the class imbalance and significantly increased the accuracy of the response prediction, as well as that web behavior is the most significant when predicting the customer's response.