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Research on Precision Marketing Strategy of Commercial Consumer Products Based on Big Data Mining of Customer Consumption

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Abstract

In the business market, precision marketing of consumer products is beneficial to improve corporate profits. Based on the big data mining of customer consumption, this paper first analyzed precision marketing and then applied the K-means algorithm to classify customers. An improved particle swarm optimization (PSO) algorithm was designed to optimize the K-means algorithm. Finally, the IPSO-k-means algorithm was used to classify the customer consumption data established by the recency, frequency, and monetary (RFM) model. The results demonstrated that the classification accuracy of the IPSO-k-means algorithm was higher than that of K-means and PSO-k-means algorithms, and the average value reached 81.79%. In classifying 3000 customer consumption data in an APP, three categories were obtained, and some suggestions of marketing strategies were provided according to the different characteristics of these three categories of customers. The experimental results prove the reliability of the IPSO-k-means algorithm in customer classification and application feasibility in actual commercial consumer products.

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  1. Shijiazhuang University of Applied Technology, Shijiazhuang, 050081, Hebei, China

    Lili Fan

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  1. Lili Fan
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Fan, L. Research on Precision Marketing Strategy of Commercial Consumer Products Based on Big Data Mining of Customer Consumption. J. Inst. Eng. India Ser. C 104, 163–168 (2023). https://doi.org/10.1007/s40032-022-00908-7

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