Electric vehicle user classification and value discovery based on charging big data

• Published in: Energy (Oxford) 2022-06, Vol.249, p.123698, Article 123698
• Main Authors: Hu, Dingding, Zhou, Kaile, Li, Fangyi, Ma, Dawei
• Format: Article
• Language: English
• Subjects: Algorithms; Big Data; Charging; Clustering; Customer relationship management; Customer satisfaction; Data mining; Economics; Electric vehicle; Electric vehicles; Entropy; Entropy (statistics); Entropy weight method; Extended RFM model; Market strategy; Marketing; Profitability; User charging behavior; Weighting methods
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2022.123698

With the rapid development of electric vehicles (EVs) in recent years, it is important to understand the varied EV users for EV sector business innovation. Therefore, identifying different types of EV users and implementing differentiated marketing strategies can assist charging service enterprises in improving 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 fields. To classify EV users, an integrated approach incorporating an extended RFM model, a two-stage clustering method, and the Entropy Weight Method is proposed in this study. 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, we develop related marketing strategies for each group of EV users to assist charging service enterprises in improving their marketing effectiveness and financial performance. •It expands the traditional RFM model to the RFMN model.•A two-stage clustering method which combines the DBSCAN and K-means is proposed.•Different EV user groups are identified based on EV charging big data.•Targeted marketing strategies are proposed from EV charging service enterprises.