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Research on Segmentation of Car-buying Users Based on Cross-industry Data Integration: Tianjin Car Buyers as Case

In: Proceedings of the 2024 6th International Conference on Economic Management and Model Engineering (ICEMME 2024)

Author

Listed:
  • Xing Han

    (China Automotive Technology & Research Center Co. Ltd., Automotive Data of China Co. Ltd)

  • Ziran Dong

    (China Automotive Technology & Research Center Co. Ltd., Automotive Data of China Co. Ltd)

  • Qiuhao Li

    (China Automotive Technology & Research Center Co. Ltd., Automotive Data of China Co. Ltd)

  • Taige Hu

    (China Automotive Technology & Research Center Co. Ltd., Automotive Data of China Co. Ltd)

Abstract

Through the integration of two authoritative data resources and the construction of auto-user related big data, this study employes hierarchical cluster method to analyze Tianjin’s vehicles buyers from 2014 to 2022. The present study centered on family-associated labels and addressed challenges due to data asynchrony and collection anomalies by data preprocessing techniques and feature engineering. As a result, users are categorized into nine distinct groups, including five majority groups and four minority groups. The study finds that different groups are significantly different in car-purchasing behavior. Factors such as family structure and economic status affect car purchase decisions. This research offers a novel user research classification approach for the Chinese automotive market. This study also provides theoretical support and practical guidance for automotive enterprises based on big data user segmentation.

Suggested Citation

  • Xing Han & Ziran Dong & Qiuhao Li & Taige Hu, 2025. "Research on Segmentation of Car-buying Users Based on Cross-industry Data Integration: Tianjin Car Buyers as Case," Advances in Economics, Business and Management Research, in: Lina Zhong & Tang Yao & Chee Yoong Liew & Hongbo Li (ed.), Proceedings of the 2024 6th International Conference on Economic Management and Model Engineering (ICEMME 2024), pages 227-237, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-690-1_22
    DOI: 10.2991/978-94-6463-690-1_22
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