湖州地区企业管理咨询客户分级评价数据

采集销售记录表中湖州地区的数据，通过客户在2019年7月1日距离2025年7月1日间隔的最近一次消费时间天数R、客户在2019年7月1日至2025年7月1日之间消费频次F和客户在2019年7月1日至2025年7月1日之间消费M元， 采用 RFM 模型对客户进行价值评级，实现精准化运营，通过对湖州地区客户价值管理，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。另外可以为本地区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2019年7月1日距离2025年7月1日间隔的最近一次消费时间天数R、客户在2019年7月1日距离2025年7月1日之间消费频次F和客户在2019年7月1日距离2025年7月1日之间消费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2019年7月1日距离2025年7月1日之间消费频次F和客户在2019年7月1日距离2025年7月1日之间消费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2019年7月1日距离2025年7月1日消费频次F从高到底依次对用户进行分类，前20%的客户在用户活动频率的分数为5，以此类推。 C, 根据客户在2019年7月1日距离2025年7月1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 is compiled by extracting data from Huzhou region's sales records. Three core metrics are calculated for each customer: Recency (R), defined as the number of days between July 1, 2025 and the customer's most recent purchase within the study period from July 1, 2019 to July 1, 2025; Frequency (F), referring to the total number of purchases made by the customer during this period; and Monetary value (M), representing the total consumption amount (in yuan) of the customer over the same timeframe. The RFM model is adopted to grade customer value for precise operational management. By managing customer value in Huzhou region, personalized demands of customers across different value tiers can be satisfied. For Class A customers, monthly follow-up visits and maintenance are conducted; Class B customers receive quarterly follow-ups; Class C customers are serviced with semi-annual follow-ups; and Class D customers get annual follow-ups. Additionally, this dataset can provide data support for local enterprises with highly overlapping customer groups to deliver personalized services targeting customers of various value types.

Preliminary data processing is performed on the collected sales records, including data desensitization, denoising, cleaning, aggregation and analysis.

2. Data Enrichment: A comprehensive customer ranking is generated based on the score rankings of the three RFM metrics, and a final overall RFM score is derived.
a. For the Recency (R) metric: Customers are categorized into 5 tiers by the days since their last purchase relative to July 1, 2025, with customers having the shortest interval (most recent purchases) ranked highest. The top 20% of customers are assigned 5 points, the next 20% 4 points, the subsequent 20% 3 points, the following 20% 2 points, and the last 20% 1 point.
b. For the Frequency (F) metric: Customers are sorted in descending order of their total purchase count between July 1, 2019 and July 1, 2025. The top 20% are awarded 5 points, with the remaining tiers following the same point assignment rule.
c. For the Monetary (M) metric: Customers are ranked in descending order of their total consumption amount during the study period. The top 20% receive 5 points, while the bottom 20% with the lowest consumption amount get 1 point, with intermediate tiers assigned points in sequence.

The overall RFM score is calculated as: Overall RFM Score = 0.3 * (R Score) + 0.3 * (F Score) + 0.4 * (M Score). Customers are classified into value tiers based on their overall score: Class A for scores ≥4, Class B for 3 ≤ score <4, Class C for 2 ≤ score <3, and Class D for scores <2.