南昌地区企业管理咨询客户分级评价数据

采集销售记录表中南昌地区的数据，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，通过对南昌地区客户价值管理，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 Collection: Extract data of the Nanchang area from the sales record table. Calculate three core metrics for customers within the period from July 1, 2019 to July 1, 2025: R, the number of days between the customer's most recent purchase and the cutoff date July 1, 2025; F, the total number of purchases made by the customer during this period; and M, the total consumption amount of the customer during this period. Utilize the RFM model to grade customer value, achieve precise operational management, and meet the personalized demands of customers with different value levels through customer value management in the Nanchang area. For Class A customers, conduct revisit and maintenance once a month; for Class B customers, once every quarter; for Class C customers, once every six months; and for Class D customers, once a year. This dataset can also provide data support for enterprises with highly overlapping customer groups in this region to deliver personalized services for customers of different value tiers.

Preprocessing: Perform data desensitization, denoising, cleaning, aggregation and analysis on the collected sales data.

2. Data Processing: Use the RFM model combined with the score rankings of R, F and M to conduct a comprehensive ranking of customers, and finally generate the overall RFM score.
a. R Metric Scoring: Sort customers by the length of the interval between their most recent purchase and July 1, 2025, with those having the shortest interval ranked first. Score customers from 1 to 5 according to the sorted order: the top 20% of customers receive 5 points, the next 20% receive 4 points, the following 20% receive 3 points, the next 20% receive 2 points, and the last 20% receive 1 point.
b. F Metric Scoring: Sort customers in descending order of their total purchase frequency F within the period from July 1, 2019 to July 1, 2025. The top 20% of sorted customers receive 5 points for their purchase frequency score, and the remaining tiers follow the same 20% ranking rule.
c. M Metric Scoring: Sort customers in descending order of their total consumption amount M within the period from July 1, 2019 to July 1, 2025. The top 20% of sorted customers receive 5 points for their consumption amount score, following the same 20% tiered rule, with the bottom 20% receiving 1 point.

The overall RFM score is calculated as: RFM Score = 0.3 * (R Score) + 0.3 * (F Score) + 0.4 * (M Score).
Customer value grading criteria: Class A customers with a score ≥ 4; Class B customers with 3 ≤ score < 4; Class C customers with 2 ≤ score < 3; Class D customers with a score < 2.

搜集汇总

背景与挑战

背景概述

该数据集包含583条南昌地区企业管理咨询客户的RFM分级评价数据，采用xlsx格式，基于客户在2019年7月1日至2025年7月1日期间的消费行为（包括最近消费时间、频次和金额）进行评分和等级划分（A、B、C、D级），旨在实现精准客户管理和个性化服务支持。