小程序用户消费分析数据

通过RFM模型，将客户数据分成标准的8类，然后根据每一类用户人数占比、金额贡献等不同的特征，进行人、货、场三重匹配的精细化运营。这有助于公司针对不同客户群体实施个性化的营销策略，进而提高不同类型的客户在产品中的活跃度、留存率和付费率。使用Data Mapping，将用户的姓名和ID映射为一个匿名的唯一标识符，以确保在数据分析时无法直接识别个人身份。利用Python处理数据，搭建RFM模型。根据客户最后一次购买时间，计算出R：顾客距今未回购天数。统计特定时间内客户购买的次数，这代表F：累计有效支付次数。次数越高，表明客户对企业的忠诚度越高。计算特定时间内客户的总付款金额。M：总付款金额/支付次数即为客户平均实付款金额，金额越高，表明客户的价值越大。统计顾客储值卡余额，根据有赞浏览用户，企微好友匹配，分析用户是否浏览过小程序，加入过企微。再根据将R,F,M是否大于均值，将用户分为8种类型，便于公司进行客户精细化运营，针对不同客户群体实施个性化的营销策略。

Using the RFM (Recency, Frequency, Monetary) model, customer data is divided into 8 standard categories. Refined three-dimensional matching operations of people, products, and scenarios are then conducted based on characteristics such as the proportion of users in each group and their revenue contribution. This enables enterprises to implement personalized marketing strategies for different customer segments, thereby enhancing the activity level, retention rate, and payment rate of various customer groups on their products.

Data Mapping is utilized to map users' names and IDs into anonymous unique identifiers, ensuring that individual identities cannot be directly identified during data analysis. Python is adopted for data processing and building the RFM model:
1. Calculate R (Recency): the number of days elapsed since the customer's last purchase up to the current date;
2. Count the number of purchases made by customers within a specific time period, which denotes F (Frequency): the cumulative number of valid payments. A higher frequency indicates greater customer loyalty to the enterprise;
3. Calculate the total payment amount of customers within a specific time period, then compute M (Monetary): the average actual payment amount per customer, calculated as total payment amount divided by the number of payments. A higher average payment amount signifies greater customer value.

Furthermore, the balance of customers' stored-value cards is collected. We match users who browsed the Youzan platform with their WeChat Work contacts to analyze whether a user has browsed the mini-program and joined WeChat Work. Finally, users are classified into 8 categories based on whether their R, F, and M values exceed the average, so as to facilitate refined customer operations and the implementation of personalized marketing strategies for different customer groups.

搜集汇总

数据集介绍

特点

该数据集提供了小程序用户的消费行为分析，包括用户ID、消费时间、消费次数、消费金额等关键字段，适用于通过RFM模型进行客户分类和精细化运营，以提升客户活跃度和留存率。