Evaluation of Classification and Ensemble Algorithms for Bank Customer Marketing Response Prediction

ABSTRACT This article attempts to improve the performance of classification algorithms used in the bank customer marketing response prediction of an unnamed Portuguese bank using the Random Forest ensemble. A thorough exploratory data analysis (EDA) was conducted on the data in order to ascertain the presence of anomalies such as outliers and extreme values. The EDA revealed that the bank data had 45, 211 instances and 17 features, with 11.7% positive responses. This was in addition to the detection of outliers and extreme values. Classification algorithms used for modelling the bank dataset include; Logistic Regression, Decision Tree, Naive Bayes and the Random Forest ensemble. These algorithms were applied to both the balanced and original bank data using Orange 3.2 data mining application following the Cross Industry Standard for Data Mining (CRISP-DM), and the ten-fold cross-validation method. Results from the experimental methods revealed that the performance of the Random Forest ensemble improved when the data was balanced. Results also showed that the features duration, poutcome, contact, month and housing were the most important features that contribute to the success of the bank customer marketing campaign for deposit subscription. The study also revealed that the duration of call to clients, response to past promotions, and the use of cell phone contribute positively to the success of the campaign. While the months of September, November, March and April recorded higher subscription rates. Those in management cadre and technicians were found to have responded more positively to the campaign than those in other job categories. Keywords: Area Under Curve, classifier, cross-validation, data mining, Decision Tree, direct marketing, ensemble, exploratory data analysis (EDA), Logistic Regression, Naive Bayes, and principal component analysis (PCA) INTRODUCTION It has become pertinent in recent years for corporate organizations to explore online and direct database marketing as part of the marketing strategy in order increase sales and keep a tab on customers. This is important in order to understand and respond to the immediate needs of the consumer. According to Raorane and Kulkarni (2011), the study of consumers helps firms and organizations to improve their marketing strategies by understanding issues such as the psychology, mindset, behavior and motivation of consumers. It is clear from the last statement that corporate organizations must hold data on customers, and develop capacity to analyze and make good use of such transactional data. One of such transaction that has been automated is customer relationship management (CRM). In this study relevant data mining techniques are used to determine which customer and what market segment would respond positively to promotions and marketing campaigns by a corporate organization. The study would rely on consumer data aggregated electronically through customer relationship management (CRM) processes. According to Tudor, Bara and Botha (2011, p. 266), a customer relationship management system is a bucket on information technology (IT) applications and procedures whose target is to identify the main expectations and preferences of clients, and to effectively use client information to improve relationships between the enterprise and its customers. Electronic CRM (eCRM), which entails wide application of information and communication technologies (ICTs) such as personalized email for instance could be categorized into four dimensions for direct marketing purposes; (i) customer identification, (ii) customer attraction, (iii) customer retention, and (iv) customer development (Kracklauer, Mills & Seifert, 2004). The motivation behind the study was to improve the level of confidence in the ability to accurately predict the customers and market segment that would respond positively to the marketing campaigns of an unnamed Portuguese bank. …