泰安地区智能窗帘消费客户分级评价数据

采集销售记录表中泰安地区的数据，通过客户在2021年9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2021年9月1日至2025年7月1日之间消费频次F和客户在2021年9月1日至2025年7月1日之间消费金额M（单位：元）， 采用 RFM 模型对客户进行价值评级，实现精准化运营，通过对泰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2021年9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2021年9月1日至2025年7月1日之间消费频次F和客户在2021年9月1日至2025年7月1日之间消费金额M（单位：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2021年9月1日至2025年7月1日之间消费频次F和客户在2021年9月1日至2025年7月1日之间消费金额M（单位：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2021年9月1日至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2021年9月1日至2025年7月1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 from the Tai'an region. Three core metrics are calculated for each customer within the time window from September 1, 2021 to July 1, 2025: R, the number of days between a customer's most recent purchase in this window and July 1, 2025; F, the total number of purchases made during the period; and M, the total consumption amount (in yuan) during the same period. The RFM model is applied to conduct customer value segmentation for precise operational management, so as to meet the personalized needs of customers with different value tiers through targeted customer value management in the Tai'an region. For Class A customers, monthly return visits and maintenance services are provided; for Class B customers, quarterly return visits; for Class C customers, semi-annual return visits; and for Class D customers, annual return visits. Additionally, this dataset can offer data support for enterprises with highly overlapping customer groups in the local region to deliver personalized services tailored to different customer value types. The collected sales records are subject to data desensitization, noise reduction, cleaning, aggregation and analysis. 2. Data Processing: A comprehensive customer ranking is generated by combining the RFM model with the score rankings of the three metrics R, F and M, and a final overall RFM score is derived. a. Extract the three metrics R (days between most recent purchase and July 1, 2025), F (total purchases between September 1, 2021 and July 1, 2025) and M (total consumption amount in yuan during the same period) for classification. Customers are sorted by their R value in ascending order, i.e., those with the shortest interval since their last purchase are ranked first. Customers are scored 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. Classify customers based on their total purchase frequency F between September 1, 2021 and July 1, 2025 in descending order. The top 20% of customers are assigned 5 points for their purchase frequency score, and the remaining groups follow the same scoring rule. c. Classify customers based on their total consumption amount M between September 1, 2021 and July 1, 2025. The top 20% of customers are assigned 5 points for their consumption amount score, and so on, with the bottom 20% of customers with the lowest consumption amount receiving 1 point. The RFM score is calculated using the formula: RFM Score = 0.3 * (R Score) + 0.3 * (F Score) + 0.4 * (M Score). Customer segmentation is performed as follows: customers with an overall score ≥4 are categorized as Class A; those with 3 ≤ score <4 as Class B; those with 2 ≤ score <3 as Class C; and those with a score <2 as Class D.

搜集汇总

背景与挑战

背景概述

该数据集包含593条泰安地区智能窗帘消费客户记录，采用RFM模型基于消费时间、频次和金额对客户进行分级评价，分为A、B、C、D四个等级，旨在实现精准化运营和个性化服务支持。