铸件购买客户分级评价数据
收藏资源简介：
采集销售记录表中购买铸件的数据，通过客户在2024年1月1日距离2025年6月30日间隔的距离最近一次消费时间天数R天、客户在2024年1月1日至2025年6月30日之间消费频次F和客户在2024年1月1日至2025年6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，通过对购买铸件客户价值管理，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。1、数据收集：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2024年1月1日距离2025年6月30日间隔的距离最近一次消费时间天数R天、客户在2024年1月1日至2025年6月30日之间消费频次F和客户在2024年1月1日至2025年6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天、客户在2024年1月1日距离2025年6月30日之间消费频次F和客户在2024年1月1日距离2025年6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2024年1月1日距离2025年6月30日消费频次F从高到底依次对用户进行分类，前20%的客户在客户活动频率的分数为5，以此类推。 C, 根据客户在2024年1月1日距离2025年6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。
This dataset collects purchase data of castings from sales record tables. We employ the RFM model to perform customer value grading for precise operational management, based on three core metrics: R (the number of days since the customer's most recent purchase within the period from January 1, 2024 to June 30, 2025, relative to the analysis cutoff date of June 30, 2025), F (the customer's total purchase frequency during this period), and M (the customer's total purchase amount during this period). By managing the value of customers who purchase castings, we can meet the personalized needs of customers across different value tiers. For Class A customers, monthly return visits for maintenance are conducted; for Class B customers, quarterly return visits; for Class C customers, semi-annual return visits; and for Class D customers, annual return visits. Additionally, this dataset can provide data support for enterprises with highly overlapping customer groups to deliver personalized services for customers of different value types. 1. Data Collection: Perform data desensitization, denoising, cleaning, aggregation and analysis on the data collected from the sales record tables. 2. Data Processing: Utilize the RFM model to generate a comprehensive customer ranking and final overall RFM score based on the score rankings of the three metrics R, F and M. a. Extract the three metrics R (days since last purchase relative to the analysis cutoff date), F (purchase frequency during January 1, 2024 to June 30, 2025) and M (total purchase amount during this period). Sort customers in ascending order of R (i.e., customers with shorter days since last purchase rank first). Assign scores from 1 to 5 following the percentile grouping rule: the top 20% of customers receive 5 points, the next 20% receive 4 points, the subsequent 20% receive 3 points, the following 20% receive 2 points, and the last 20% receive 1 point. b. Sort customers in descending order of F (i.e., customers with higher purchase frequency rank first). Assign scores from 1 to 5 using the same percentile grouping rule: the top 20% receive 5 points, and so on. c. Sort customers in descending order of M (i.e., customers with higher total purchase amount rank first). Assign scores from 1 to 5 using the same percentile grouping rule: the top 20% receive 5 points, and the last 20% receive 1 point. The overall RFM score is calculated as: RFM Score = 0.3 * (R Score) + 0.3 * (F Score) + 0.4 * (M Score). Customers are divided into four tiers based on their overall RFM score: Class A customers with a score ≥ 4, Class B customers with 3 ≤ Score < 4, Class C customers with 2 ≤ Score < 3, and Class D customers with Score < 2.
