西安地区企业管理咨询客户分级评价数据

采集销售记录表中西安地区的数据，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，通过对西安地区客户价值管理，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。

First, extract data of the Xi'an region from the sales record table. Calculate three metrics for each customer: Recency (R, the number of days between the most recent purchase date and July 1, 2025 within the period from July 1, 2019 to July 1, 2025), Frequency (F, the total number of purchases made during July 1, 2019 to July 1, 2025), and Monetary value (M, the total consumption amount during the same period). Use the RFM model to conduct customer value grading, enabling precise operations and meeting personalized needs of customers with different value tiers through Xi'an region customer value management. For Level A customers, conduct monthly follow-up visits and maintenance; for Level B customers, conduct quarterly follow-up visits and maintenance; for Level C customers, conduct semi-annual follow-up visits and maintenance; for Level D customers, conduct annual follow-up visits and maintenance. Additionally, this dataset can provide data support for enterprises with highly overlapping customer groups in this region to deliver personalized services targeting different customer value types. Preprocess the extracted data from the sales record table via desensitization, denoising, cleaning, aggregation and analysis. 2. Data processing: Perform comprehensive ranking of customers by combining the RFM model with the score rankings of R, F and M metrics defined above, and finally derive the overall RFM score. a. Classify customers based on their R metric (days since most recent purchase). Rank customers from the shortest interval to the longest, 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 following 20% get 2 points, and the last 20% get 1 point. b. Classify customers based on their F metric (total purchase frequency) from highest to lowest. The top 20% of customers get 5 points for their activity frequency score, and so on for the remaining tiers. c. Classify customers based on their M metric (total consumption amount). The top 20% of customers get 5 points for their consumption amount score, and the last 20% with the lowest consumption amount get 1 point. The RFM score is calculated as: RFM Score = 0.3 * R_score + 0.3 * F_score + 0.4 * M_score. Finally, define customer tiers as follows: customers with a score ≥4 are Level A customers; customers with 3 ≤ score <4 are Level B customers; customers with 2 ≤ score <3 are Level C customers; customers with score <2 are Level D customers.

搜集汇总

背景与挑战

背景概述

该数据集包含西安地区企业管理咨询客户的596条分级评价记录，基于RFM模型（考虑最近消费时间、消费频次和消费金额）对客户进行价值评级，分为A、B、C、D四个等级。数据集适用于企业精准化运营，通过分析2019年7月至2025年7月的消费数据，支持个性化客户服务和回访策略制定。