汕头地区企业管理咨询客户分级评价数据

采集销售记录表中汕头地区的数据，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，通过对汕头地区客户价值管理，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。

Data pertaining to the Shantou region was collected from the sales record table. Using the number of days since a customer's most recent purchase (denoted as R) between July 1, 2019 and July 1, 2025, their purchase frequency (F) within the same period, and their total consumption amount (M) within the same period, the RFM model is employed to grade customer value, enabling precise operational management. Through customer value management in the Shantou region, personalized needs of customers with different value tiers are met. Level A customers will receive monthly return visits and maintenance; Level B customers quarterly; Level C customers semi-annually; and Level D customers annually. Additionally, this dataset can provide data support for enterprises with highly overlapping customer groups in this region to deliver personalized services tailored to different customer value tiers. Collected data from the sales record table will undergo desensitization, noise reduction, cleaning, aggregation, and analysis.

2. Data processing: Conduct a comprehensive ranking of customers using the RFM model, combined with the score rankings of R (days from the customer's most recent purchase to July 1, 2025, the current analysis cutoff date), F (purchase frequency between July 1, 2019 and July 1, 2025), and M (total consumption amount within the same period), to derive an overall RFM score.
a. Extract R, F, and M for classification. Customers with the shortest time interval since their last purchase are ranked first. Scores are assigned from 1 to 5: 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. Rank customers in descending order of their purchase frequency F between July 1, 2019 and July 1, 2025, and assign scores: the top 20% get 5 points, and so on.
c. Rank customers in descending order of their total consumption amount M between July 1, 2019 and July 1, 2025, and assign scores: the top 20% get 5 points, and the bottom 20% (those with the lowest consumption amount) get 1 point.

The overall RFM score is calculated as: RFM Score = 0.3 * (R Score) + 0.3 * (F Score) + 0.4 * (M Score). Customers with a score ≥4 are classified as Level A customers; 3 ≤ Score <4 as Level B; 2 ≤ Score <3 as Level C; and Score <2 as Level D.

搜集汇总

背景与挑战

背景概述

该数据集包含628条汕头地区企业管理咨询客户的RFM模型评价数据，覆盖2019年7月至2025年7月的消费记录，用于客户分级（A、B、C、D级）。它通过分析最近消费天数、消费频次和消费金额，实现精准运营和个性化服务支持，适用于客户价值管理和企业决策。