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A Precision Marketing Strategy of e-Commerce Platform Based on Consumer Behavior Analysis in the Era of Big Data

Author

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  • Di Zhang
  • Minghao Huang
  • Wenlong Hang

Abstract

In order to develop a more efficient and accurate marketing strategy for consumers’ purchase behavior, this paper establishes a user value model by modeling and learning the user historical data of e-commerce enterprises. The improved K-means algorithm is used to cluster the purchase behavior of users, and the customer value matrix is constructed from two dimensions of consumption frequency and average consumption amount. Finally, e-commerce users are classified into four categories by marking points. The test results show that the improved K-means algorithm is stable and efficient, and the analysis of user clustering characteristics is helpful to develop more accurate marketing strategies.

Suggested Citation

  • Di Zhang & Minghao Huang & Wenlong Hang, 2022. "A Precision Marketing Strategy of e-Commerce Platform Based on Consumer Behavior Analysis in the Era of Big Data," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, August.
    • Handle: RePEc:hin:jnlmpe:8580561
    DOI: 10.1155/2022/8580561
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    Cited by:

    1. Oyama, Yuki & Fukuda, Daisuke & Imura, Naoto & Nishinari, Katsuhiro, 2024. "Do people really want fast and precisely scheduled delivery? E-commerce customers' valuations of home delivery timing," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).

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