抚州地区购买公司软件客户价值评估数据

采集销售记录表中抚州地区的数据，通过客户在2022年1月1日距离2025年3月31日间隔的最近一次消费时间天数（R）、客户在2022年1月1日至2025年3月31日之间消费频次(F)和客户在2022年1月1日至2025年3月31日之间消费金额(M)， 采用 RFM 模型对客户进行价值评级，实现精准化运营，通过对抚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。数据处理：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2022年1月1日距离2025年3月31日间隔的最近一次消费时间天数（R）、客户在2022年1月1日至2025年3月31日之间消费频次(F)和客户在2022年1月1日至2025年3月31日之间消费金额(M)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2022年1月1日至2025年3月31日之间消费频次(F)和客户在2022年1月1日至2025年3月31日之间消费金额(M)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2022年1月1日距离2025年3月31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2022年1月1日距离2025年3月31日消费金额(M)，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 sales record data of the Fuzhou region. Customer value rating is conducted via the RFM model based on three core metrics: 1) Recency (R): the number of days between a customer's most recent purchase and March 31, 2025 (the end of the analysis period spanning from January 1, 2022 to March 31, 2025); 2) Frequency (F): the total number of purchases made by the customer during the analysis period; 3) Monetary value (M): the total consumption amount of the customer during the analysis period. This work aims to achieve precise customer operation and meet personalized demands of customers with different value levels through value-based management of Fuzhou region customers. Specifically, monthly return visits and maintenance are provided for Level A customers, quarterly for Level B customers, semi-annually for Level C customers, and annually for Level D customers. Additionally, the dataset can provide data support for local enterprises with highly overlapping customer groups to deliver personalized services for customers of various value tiers.

Data processing: Desensitization, denoising, cleaning, aggregation and analysis are performed on the raw data collected from the sales record table.

Data enrichment and scoring: A comprehensive customer ranking is generated using the RFM model based on the score rankings of the three metrics above, to derive an overall RFM score. The specific steps are as follows:
a. Extract the recency (R), purchase frequency (F) and consumption amount (M) for metric classification. Customers with the shortest interval between their most recent purchase and March 31, 2025 are ranked first. All customers are scored from 1 to 5: the top 20% receive 5 points, the next 20% receive 4 points, the following 20% receive 3 points, the subsequent 20% receive 2 points, and the last 20% receive 1 point.
b. Classify customers in descending order of their purchase frequency (F) during the analysis period. The top 20% of customers get a score of 5 for their purchase frequency, and the remaining customers are assigned scores in the same descending order of frequency.
c. Classify customers in descending order of their total consumption amount (M) during the analysis period. The top 20% of customers receive a score of 5 for their consumption amount, while the bottom 20% with the lowest total consumption amount receive a score of 1.

The overall RFM score is calculated using the formula: RFM Score = 0.3 × (R Score) + 0.3 × (F Score) + 0.4 × (M Score). Customers are classified into four tiers based on their overall score: Level A (score ≥4), Level B (3 ≤ score <4), Level C (2 ≤ score <3), and Level D (score <2).

搜集汇总

背景与挑战

背景概述

该数据集专注于抚州地区购买公司软件的客户价值评估，包含720条记录，基于2022年1月1日至2025年3月31日的销售数据，采用RFM模型计算客户最近一次消费天数、消费频次和消费金额的得分，并综合评级为A、B、C、D级。它旨在通过精准的客户价值分析，支持企业实现个性化运营和服务优化，例如针对不同等级客户制定差异化的回访策略。