杭州地区博洋服饰零售会员活跃度分类数据

基于博洋服饰在杭州地区的门店会员数据，为会员的分类运营提供必要的会员活跃度分类数据支持。此数据主要服务于服装零售场景中的会员分类运营，为精准营销提供明确的会员活跃度分类数据。通过对会员行为数据的不同维度分析、数据聚类、数据排序等方法，将所有会员按照其活跃度分划分为“活跃、潜力、惰性、休眠、其他”等5类。算法及规则说明： 会员分类的算法规则采用统计学模型排序、聚类的方法，通过对会员的消费频次和消费时间、互动频次和互动时间的排序、聚类，对会员进行分类。 包括以下： 1.数据来源：会员管理系统(会员基础信息表)，DRP销售系统(销售记录表)，会员系统(会员登录数据表) 。 2.数据整理：数据仓库（会员分类表)。 3.模型选择：采用了排序、聚类等模型。通过对会员的消费频次聚类、消费金额聚类、互动频次聚类、最近一次消费记录排序等方法获得多维度的会员活跃度分类数据。 4.模型参数及优化：通过对参数调整，调优会员的分类结果。以会员分类运营所需的5类群体“活跃、潜力、惰性、休眠、其它”为最终分类目标，基于消费频次、消费金额等不同维度获得的聚类分组成果，对聚类分组数量和分组阀值、以及维度权重进行人为干预，使会员分类趋于合理。 5.数据成果：根据算法模型得出会员“活跃度”分类数据。

This dataset is derived from the membership data of Boyang Fashion's stores in Hangzhou, providing necessary support for classified membership operations through membership activity classification data. It primarily serves the classified membership operation scenario in clothing retail, offering clear membership activity classification data for precise marketing. Through multi-dimensional analysis, data clustering, data ranking and other methods of member behavior data, all members are categorized into 5 groups based on their activity levels: "Active", "Potential", "Inactive", "Dormant", and "Other". Algorithm and Rule Description: The classification algorithm adopts statistical model-based ranking and clustering methods, which classify members by ranking and clustering their consumption frequency, consumption time, interaction frequency and interaction time. Specific contents are as follows: 1. Data Sources: Membership Management System (membership basic information table), DRP Sales System (sales record table), and Membership System (member login data table). 2. Data Organization: Data Warehouse (member classification table). 3. Model Selection: Ranking and clustering models are employed. Multi-dimensional membership activity classification data are acquired through methods including clustering of member consumption frequency, clustering of consumption amount, clustering of interaction frequency, and ranking of the latest consumption record. 4. Model Parameters and Optimization: The member classification results are optimized by adjusting parameters. Taking the 5 target groups required for classified membership operation ("Active", "Potential", "Inactive", "Dormant", "Other") as the final classification goal, manual intervention is performed on the number of clustering groups, grouping thresholds and dimension weights based on the clustering grouping results obtained from different dimensions such as consumption frequency and consumption amount, so as to make the member classification more reasonable. 5. Data Results: Membership "activity" classification data are generated based on the algorithm model.