A decision-making framework for precision marketing

Abstract

Precision marketing offers personalized customer service and is used to help enterprises increase their profits by means of high-efficiency marketing. This paper presents a novel decision-making framework for precision marking using data-mining techniques. First, this study presents a trend model to accurately predict monthly supply quantity; second, it uses a RFM (Recency, Frequency and Monetary) model to select attributes to cluster customers into different groups; third, it uses CHAID decision trees and Pareto values to identify important attribute values to distinguish different customer groups; and finally, it creates different supply strategies targeting each customer group. The objective of the proposed precision-making framework is to help managers identify the potential characteristics of different customer categories and put forward appropriate precision marketing strategies, which can greatly reduce inventory for every customer category. The real-world data from a company in China were collected and used in a case study to illustrate how to implement the proposed framework. This case study demonstrates that our proposed decision-making framework is efficient and capable of providing a very good precision marketing strategy for enterprises.