Promotion Recommendation Method and System Based on Random Forest

Living in a sharply competitive telecom market, customers are provided with a great variety of promotions of telecom offers which are excessive and complex in recent years. Not only customers have no idea how to choose the suitable promotions but also front-line sales cannot recommend suitable promotions depending on merely the traditional recommendation method, namely, their personal intuition and experiences. In this way, promotion recommendation lacks mobile data usage pattern and consumption level of customers so that telecom operators hardly provide precision marketing. Conclusively, it may be possibly an inappropriate telecom offer, resulting in low customer satisfaction and loyalty, high churn rate, or in the worst case, erosion of average revenue per user (ARPU). Therefore, this study proposes a promotion recommendation method and system based on random forest to analyze the customer profiles and historical mobile data usage. Then the marketing information can be obtained for front-line sales to help make precise marketing strategies and recommending promotions in accordance with these customer features. Eventually, customer satisfaction, customer loyalty and the income of telecom operators can be increasing. In experimental results, more than 500 thousand mobile data usage records of customers in Chunghwa Telecom from January to March in 2017 were collected and analyzed for the evaluation of the proposed method. The accuracy of the proposed method is 93.36% which is higher than the traditional method. It also gains an advantage over other three popular classification algorithms for recommendation.