Developing Decision Tree based Models in Combination with Filter Feature Selection Methods for Direct Marketing

Ruba Obiedat

doi:10.14569/ijacsa.2020.0110180

Abstract

Direct Marketing is a form of advertising strategies which aims to communicate directly with the most potential customers for a certain product using the most appropriate communication channel. Banks are spending a huge amount of money on their marketing campaigns, so they are increasingly interested in this topic in order to maximize the efficiency of their campaigns, especially with the existence of high competition in the market. All marketing campaigns are highly dependent on the huge amount of available data about customers. Thus special Data Mining techniques are needed in order to analyze these data, predict campaigns efficiency and give decision makers indications regarding the main marketing features affecting the marketing success. This paper focuses on four popular and common Decision Tree (DT) algorithms: SimpleCart, C4.5, RepTree and Random Tree. DT is chosen because the generated models are in the form of IF-THEN rules which are easy to understand by decision makers with poor technical background in banks and other financial institutions. Data was taken from a Portuguese bank direct marketing campaign. A filter-based Feature selection is applied in the study to improve the performance of the classification. Results show that SimpleCart has the best results in predicting the campaigns success. Another interesting finding that the five most significant features influencing the direct marketing campaign success to be focused on by decision makers are: Call duration, offered interest rate, number of employees making the contacts, customer confidence and changes in the prices levels.

Highlights

Direct marketing has become a trend topic for academics and researchers over the past few years due to high competition between companies, increasing marketing campaigns costs and the changing demands of customers which make it hard to predict [29] [22]
This study aims to use a simple and comprehensive data mining model which is easy to be understood by users with little or no technical background, especially that decision makers in this case are usually sales persons and managers who are responsible for the direct marketing decisions and it is hard for them to use, understand and interpret more complex models even if these models have more predictive power
The results showed that Support Vector Machines (SVM) has the highest prediction performance followed by Naıve Bayes (NB) and Decision Tree (DT) respectively

Summary

Introduction

Direct marketing has become a trend topic for academics and researchers over the past few years due to high competition between companies, increasing marketing campaigns costs and the changing demands of customers which make it hard to predict [29] [22]. All industries aim to increase their returns of marketing campaigns and their sales through using the right marketing channels and techniques directed to the right customers at the right time [15]. Mass marketing uses the traditional media for promotion such as television, radio, newspapers and broadcast messages to be distributed randomly without any customization [15], [12]. This type of marketing becomes less effective with time because of the great competition and the large number of available products these days along with its high cost. It is to be noted that, industries hope to increase this rate using direct marketing [13][29] [22]

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