Research on the Purchase Intention of Electric Vehicles Based on Customer Evaluation and Personal Information

With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers’ purchase intentions. According to the data of customers’ vehicle experience evaluation and personal information, this paper develops the analysis models of i...

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Bibliographic Details
Published in:World electric vehicle journal 2024-01, Vol.15 (1), p.9
Main Authors: Chen, Jian, Zhang, Zhenshuo, Zhao, Chenyu, Zhang, Shuai, Guo, Wenfei, Lu, Cunhao, Sun, Xiaoguang
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
ANOVA algorithm
Consumer behavior
Consumers
Customers
Electric vehicles
Environmental impact
Government subsidies
Influence
Kruskal–Wallis algorithm
Optimization
Optimization models
Perceptions
Personal information
Prediction models
Questionnaires
random forest method
Sales
sales plan optimization
Social networks
Statistical methods
Structural equation modeling
Time series
Variance analysis
vehicle purchase forecast
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Summary:With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers’ purchase intentions. According to the data of customers’ vehicle experience evaluation and personal information, this paper develops the analysis models of influencing factors using the analysis of variance algorithm (ANOVA) and Kruskal–Wallis algorithm. Then, the purchase intention model for EVs is proposed using the random forest method. Finally, the optimization model for the EV sales plan was built. The results show that the main factors influencing customers’ purchases are different for different vehicle brands. However, the customer’s evaluation of the vehicle experience has a greater influence on the customer’s purchase. Compared to other prediction models, the random forest model has the highest accuracy. For 3 EV brands, the prediction accuracies are 97.8%, 98.9%, and 97.6%. In addition, this paper predicts the purchase intentions of 15 customers. By optimizing the sales plans for 3 EV brands, the predicted purchase rate of 15 customers increased from 40% to 53%. The research work contributes to the sales of electric vehicles, the accurate positioning of customers, and the identification of more potential customers.
ISSN:2032-6653
2032-6653
DOI:10.3390/wevj15010009