The Research on Dynamics in Phone Sales Marketing Campaign Based on Machine Learning

Abstract

Phone market campaign is one of the selling methods that banks use to attract new term deposits. Identifying attributes of customers is essential to increase the successful rate of a phone marketing campaign. This paper uses a dataset from a Portuguese bank where various features and attributes of customers are summarized. Four methods are used to analyze this issue, firstly decision tree method, then random forest method, K nearest neighbor and lastly logistic regression. After evaluating and reviewing their confusion matrices and generated scores, the decision has been made to choose the model of random forest as it possesses the highest mean of all metrics of classification. In conclusion, the duration of contact, age, how many days have passed since last contact, the month of the contact, and the number of contacts on one customer are deemed as more important than other attributes under a random forest classifier. The findings of this paper implicates that the Portuguese bank needs to focus more of these attributes to obtain a sustainable development.