Abstract:

With the rapid development of electric vehicles (EVs) in recent years, it is essential to understand the varied EV users for EV sector business innovation. Therefore, identifying different types of EV users and implementing differentiated marketing strategies can help charging service businesses increase profitability and user loyalty. Recency, frequency and monetary (RFM) model is an important data mining method that has important practical applications in customer relationship management and direct marketing. This study proposes an integrated approach incorporating an extended RFM model, a two-stage clustering method, and the Entropy Weight Method to classify EV users. Analysis results demonstrate that 7426 EV users are divided into six groups, namely “high-value users,” “key users to maintain,” “key users to develop,” “potential users,” “new users,” and “lost users”. To estimate the performances of the proposed approach, the traditional cluster algorithm and fuzzy c-means method are compared with the improved entropy-cluster algorithm by using the intraclass method. The results indicate that the proposed approach is more robust than other methods. Finally, the authors develop related marketing strategies for each group of EV users to assist charging service enterprises in improving their marketing effectiveness and financial performance.