期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：1
DOI：10.14569/IJACSA.2020.0110180
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Direct marketing; data mining; decision tree; simpleCart; C4.5; reptree; random tree; weka; confusion matrix; class-imbalance
