Consumer Behavior Prediction and Enterprise Precision Marketing Strategy Based on Deep Learning

被引：0

作者：

Yuan, Zhe ^{[1
]}

机构：

[1] Faculty of Culture and Tourism, Jiyuan Vocational and Technical College, Henan, Jiyuan,459000, China

来源：

Informatica (Slovenia) | 2024年 / 48卷 / 15期

关键词：

This paper explores consumer behavior prediction and enterprise precision marketing strategy utilizing deep learning. The study introduces consumer behavior prediction research and highlights the advantages of the Long Short-Term Memory (LSTM) network in processing time series data. The methodology includes comprehensive data collection and teleprocessing; followed by the construction of an LSTM model. The model's predictive accuracy is validated through metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE); demonstrating low error rates within 1%. Key results include improved browsing conversion rates; purchase rates; and repurchase rates. The study also analyzes the impact of various marketing strategies; including personalized recommendations; which increased purchase rates by 4.8% and user satisfaction by 4.0%; as well as limited-time offers; member rewards; optimized checkout processes; and social media engagement. The findings suggest that deep learning technology significantly enhances consumer behavior prediction accuracy; offering a robust foundation for precision marketing strategies that elevate market competitiveness and customer satisfaction. © 2024 Slovene Society Informatika. All rights reserved;

D O I：

10.31449/inf.v48i15.6260

中图分类号：

学科分类号：

摘要：

引用

页码：65 / 76

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