随着数智化电商的快速发展,直播带货行业面临用户需求碎片化、市场竞争加剧以及营销效率不足等多重挑战。本研究基于用户行为数据,构建了扩展的RFM-QPC-V用户特征模型,并运用K-Means聚类算法生成用户画像,精准刻画用户的消费偏好与价值层级。在此基础上,采用梯度提升模型设计代金券动态投放策略,并通过SMOTE过采样技术与ENN欠采样方法优化模型性能。实验结果表明,该模型召回率达到0.7,AUC-ROC值达到0.88,在精准识别高价值用户和提升转化率方面表现出色,为直播带货提供了有效的技术支撑与决策参考。此外,本研究进一步提出了分层投放与地域投放的营销策略,并针对不同顾客群体设计了差异化的代金券方案,旨在为数智化电商产业的高效发展赋能。

With the rapid development of digital and intelligent e-commerce, the live streaming e-commerce industry is facing multiple challenges such as fragmented user demands, intensified market competition, and insufficient marketing efficiency. Based on user behavior data, this study constructs an extended RFM-QPC-V user characteristic model, and uses the K-Means clustering algorithm to generate user portraits, accurately depicting users' consumption preferences and value hierarchies. On this basis, a gradient boosting model is adopted to design a dynamic voucher delivery strategy, and the model performance is optimized through the SMOTE oversampling technique and the ENN under-sampling method. Experimental results show that the recall rate of this model reaches 0.7 and the AUC-ROC value reaches 0.88. It performs excellently in accurately identifying high value users and improving the conversion rate, providing effective technical support and decision - making reference for live streaming e-commerce. In addition, this study proposes a layered distribution and regional marketing strategy, designing differentiated coupon schemes for different customer groups, aiming to empower the efficient development of the smart digital e-commerce industry.