Construction of Personalized Marketing Model for E-commerce Services Based on Big Data

With the vigorous development of digital economy, e-commerce industry has ushered in a new round of opportunities for innovation and growth, and at the same time it is facing increasingly fierce market competition pressure. In order to improve the consumer experience and meet the diverse needs of users, e-commerce enterprises urgently need to complete the construction of personalized and accurate marketing model. However, at this stage, the marketing scheme setting of e-commerce enterprises still follows the traditional way, and generally faces the core problems such as the lag of theoretical methods, the simplification of algorithm models and the disconnection between marketing strategies and actual market demand. In this regard, based on the actual project application requirements, this paper will deeply explore the application feasibility of big data analysis methods in e-commerce marketing planning, and build a personalized marketing model of e-commerce services based on distributed computing framework and machine learning algorithm to improve the implementation effect of marketing programs and user satisfaction. Practice has proved that on the one hand, the model emphasizes the comprehensive collection and efficient integration of multi-source user data, and provides support for subsequent personalized recommendation through multi-dimensional user portraits; On the other hand, the model uses LightGBM algorithm to predict the future purchase behavior of users. Combined with the analysis results of the two parts, the corresponding marketing strategy is formulated, so as to provide users with products and services that are more in line with their individual needs.