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

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.

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