Auto Sales Forecasting Model Based on Target Customer Satisfaction Theory

DOI

10.2991/978-94-6463-042-8_79How to use a DOI?

Keywords

Target customer satisfaction; Logistic regression; Vehicle sales forecasting; Customer mining model

Abstract

The development of new energy vehicles is highly supported by the country, but the sales strategy of new energy vehicles is not mature, and there are few studies on the relationship between vehicle performance and purchase intention in the existing sales strategy. This paper refers to the existing research on the vehicle performance of new energy vehicles and consumers' willingness to purchase, based on the research framework of target customer satisfaction, Pearson's correlation coefficient between various factors was obtained through the data correlation analysis, and three indicators that had a greater impact on the results were selected. This paper establishes a user mining model, selects the power comfort, safety and economy of the vehicle as indicators through factor analysis, and uses logistic regression for model prediction according to the linear hypothesis of each index, finally analyzes the sensitivity of the three indicators and puts forward a targeted new automotive product.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chen Pang
AU  - Zixuan  Li
AU  - Changying  Feng
AU  - Zexuan Li
PY  - 2022
DA  - 2022/12/29
TI  - Auto Sales Forecasting Model Based on Target Customer Satisfaction Theory
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 551
EP  - 558
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_79
DO  - 10.2991/978-94-6463-042-8_79
ID  - Pang2022
ER  -