In recent years, with the development of machine learning and big data technology, user data has become an important element in the production process of enterprises. For today’s e-commerce platforms, the deep mining of user’s purchase behavior is helpful to understand user’s purchase preferences and accurately recommend products that meet user expectations, which can not only improve user satisfaction but also reduce platform marketing cost. To accurately identify the user value of online purchasing on an e-commerce platform, this paper uses an improved RFM model to extract user features and uses the -means++ clustering algorithm to realize user classification. The indicators of the traditional RFM model characterize user features from three angles: recent purchase time (), purchase frequency (), and total consumption amount (). The user group and scenarios studied in this paper are different from the previous literature: (1) the user group is relatively fixed, (2) the consumer goods are relatively single, and (3) the characteristics of repeated purchase are obvious. Therefore, based on the existing literature, this paper extracts the user characteristics studied and improves and models the traditional indicators. Based on the real purchasing data from September to December 2018, it calculates the indicators that improved RFM, empowers the weight to indicators, and finally classifies the value of users by using the -means++ algorithm. The experimental results show that the user classification based on the improved RFM model is more accurate than the user classification based on the traditional RFM model, and the improved RFM model can identify the user value more accurately, which provides a strong support for the e-commerce platform to realize the accurate marketing strategy based on big data.

中文翻译：

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基于改进的RFM模型和-Means ++算法的用户价值识别用于复杂数据分析

近年来，随着机器学习和大数据技术的发展，用户数据已成为企业生产过程中的重要元素。对于当今的电子商务平台，深入挖掘用户的购买行为有助于了解用户的购买偏好并准确推荐符合用户期望的产品，不仅可以提高用户满意度，还可以降低平台营销成本。要准确地确定一个电子商务平台上的网上购物的用户价值，本文采用了改进的RFM模型，以提取用户的功能和使用-手段++聚类算法，实现用户分类。传统RFM模型的指标从三个角度表征用户功能：近期购买时间（），购买频率（）和总消费金额（）。本文研究的用户群和情景与以前的文献不同：（1）用户群相对固定；（2）消费品相对单一；（3）重复购买的特征很明显。因此，本文在现有文献的基础上，提取了所研究的用户特征，并对传统指标进行了改进和建模。根据2018年9月至2018年12月的实际购买数据，该工具计算可改善RFM的指标，赋予指标权重，最后使用-表示++算法。实验结果表明，基于改进RFM模型的用户分类比基于传统RFM模型的用户分类更加准确，改进后的RFM模型可以更准确地识别用户价值，为电子商务提供了有力的支持。电子商务平台，以实现基于大数据的准确营销策略。