英文摘要 |
In studying customer relationship management, how to find the high-value customers and retain them is a very important topic: this issue is also the key for a company for survive. High-value customers have been considered to be a company's precious property, Therefore, forecasting the high-value customers' revenue contribution will help forecasting the company's property change. In this study, we combine the RFM model with Bayesian stochastic model and ARIMA, respectively, to construct two models for forecasting customer state. The forecasting accuracy of these two models is tested by the 1,928 transactions records from the 157 business customers of a chemicals trading company. The results indicate that the RFM model with ARIMA performs better than the RFM model with Bayesian stochastic model. The present study also find the deficiency of traditional customer state definition. |