辽宁省糕点消费客户分级评价数据

采集销售记录表中购买糕点的数据，通过客户在2025年7月1日距离2025年8月30日间隔的距离最近一次消费时间天数R、客户在2025年7月1日至2025年8月30日之间消费件数F和客户在2025年7月1日至2025年8月30日之间消费金额M元， 采用 RFM 模型对客户进行价值评级，实现精准化运营，通过对购买糕点客户价值管理，满足不同价值客户的个性化需求。对A级客户，每个月进行一次回访维护，对B级客户，每个季度进行一次回访维护，对C级客户每半年进行一次回访维护，对D级客户每年进行一次回访维护。另外可以为本客户群体高度重叠企业提供不同价值类型的客户个性化服务的数据支持。对从销售记录表中采集到的数据进行脱敏、降噪、清洗、聚集、分析。2、数据加工：运用RFM模型结合客户在2025年7月1日距离2025年8月30日间隔的距离最近一次消费时间天数R天、客户在2025年7月1日至2025年8月30日之间消费件数F和客户在2025年7月1日至2025年8月30日之间消费金额M元的得分排名对客户进行一个综合排名，最终得出一个RFM总评分。a.提取出最近一次消费时间距离当前分析时间的天数R、客户在2025年7月1日距离2025年8月30日之间消费件数F和客户在2025年7月1日距离2025年8月30日之间消费金额M元进行分类，最近一次消费时间间隔最短的客户排在最上面。按照从1-5评分，前20%的客户获得5分，接下来的20%用户获得4分,再下来20%的客户为3分，再下来20% 的客户为2分，最后20% 的客户为1分。 b.根据客户在2025年7月1日距离2025年8月30日消费件数F从高到底依次对用户进行分类，前20%的客户在用户活动频率的分数为5，以此类推。 C, 根据客户在2025年7月1日距离2025年8月30日消费金额M元，前20%的客户在消费金额的分数为5，以此类推。消费金额最少的20%客户则分数为1。 RFM得分=0.3*（R得分）+0.3*（F得分）+0.4*（M得分） 评分大于等于4分的为A级客户，大于等于3小于4的为B级客户，大于等于2小于3的为C 级客户，低于2的为D级客户。Collect pastry purchase data from the sales record table. Specifically, extract three metrics for customers within the period from July 1, 2025 to August 30, 2025: R, the number of days since the customer's most recent purchase by August 30, 2025; F, the total number of purchased items; and M, the total consumption amount (in yuan). Then use the RFM model to conduct customer value grading to enable precise operational management. By managing the value of pastry-purchasing customers, we can meet the personalized needs of customers across different value tiers. Tier A customers will receive monthly follow-up visits and retention maintenance; Tier B customers quarterly; Tier C customers semi-annually; and Tier D customers annually. Additionally, this dataset can provide data support for enterprises with highly overlapping customer groups to deliver personalized services for customers of different value types. The data collected from the sales record table will undergo desensitization, denoising, cleaning, aggregation, and analysis. 2. Data Processing: Use the RFM model combined with the score rankings of R, F, and M to conduct a comprehensive customer ranking and calculate the final overall RFM score. a. Extract R (days from the most recent purchase to August 30, 2025), F (number of purchased items), and M (total consumption amount) within July 1 to August 30, 2025 for classification. Rank customers in ascending order of the interval since their most recent purchase (i.e., customers with the shortest interval are ranked highest). Assign scores from 1 to 5: the top 20% of customers get 5 points, the next 20% get 4 points, the subsequent 20% get 3 points, the next 20% get 2 points, and the last 20% get 1 point. b. Rank customers in descending order based on their F value (number of purchased items within the specified period). The top 20% receive a score of 5 for their activity frequency, and so on for the remaining groups. c. Rank customers in descending order based on their M value (total consumption amount within the specified period). The top 20% receive a score of 5 for their consumption amount, and so on. The 20% of customers with the lowest consumption amount get a score of 1. The overall RFM score is calculated as: RFM Score = 0.3 * (R Score) + 0.3 * (F Score) + 0.4 * (M Score). Customers are classified into tiers as follows: Tier A for scores ≥4, Tier B for 3 ≤ Score <4, Tier C for 2 ≤ Score <3, and Tier D for Score <2.

搜集汇总

背景与挑战

背景概述

该数据集是辽宁省糕点消费客户的分级评价数据，包含526条记录，基于RFM模型（结合最近消费时间、消费频率和消费金额）对客户进行价值评级，分为A、B、C、D四个等级。数据集主要用于企业精准运营，通过客户分级实现个性化回访和服务支持，适用于制造业中的销售分析和客户管理场景。