福州地区智能窗帘消费客户分级评价数据

采集销售记录表中福州地区的数据，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，通过对福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。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。

First, extract sales data of the Fuzhou region from the sales record table. Calculate three core metrics for customer value analysis: Recency (R), defined as the number of days between a customer’s most recent consumption date and the analysis cutoff date July 1, 2025, within the period from September 1, 2021 to July 1, 2025; Frequency (F), the total number of consumption transactions during this period; and Monetary (M), the total consumption amount in Yuan during this period. Employ the RFM model to grade customer values for precise operational management, and optimize customer value management in the Fuzhou region to cater to personalized demands of customers across different value tiers. For Class A customers, conduct regular return visits and maintenance once monthly; for Class B customers, once every quarter; for Class C customers, once every six months; and for Class D customers, once annually. Additionally, provide data-driven support for local enterprises with highly overlapping customer bases to deliver personalized services tailored to different value customer segments. The collected sales data will undergo preprocessing steps including desensitization, noise reduction, data cleaning, aggregation and exploratory analysis.

2. Data Processing: Conduct comprehensive customer ranking by leveraging the RFM model combined with score rankings of the three metrics R, F and M, and derive the final overall RFM score.
a. Metric Classification and Scoring: Extract the values of R, F and M for each customer within the analysis period (September 1, 2021 to July 1, 2025). Sort customers in ascending order of R value (i.e., customers with shorter time since last consumption rank first). Assign scores from 1 to 5 to each customer based on their metric ranking: the top 20% of customers receive 5 points, the next 20% get 4 points, the subsequent 20% get 3 points, the following 20% get 2 points, and the bottom 20% receive 1 point.
b. Frequency-based Scoring: Rank customers in descending order of their total consumption frequency F during the analysis period. Assign scores following the same 20% tier rule: the top 20% get 5 points, and the scoring decreases by 1 tier for each subsequent 20% group.
c. Monetary-based Scoring: Rank customers in descending order of their total consumption amount M during the analysis period. Assign scores per the 20% tier rule: the top 20% receive 5 points, while the bottom 20% with the lowest total consumption amount get 1 point, with the middle groups assigned scores in descending order accordingly.
The overall RFM score is calculated using the formula: RFM Score = 0.3*(R Score) + 0.3*(F Score) + 0.4*(M Score). Classify customers based on their final RFM score: Class A for scores ≥4, Class B for 3 ≤ score <4, Class C for 2 ≤ score <3, and Class D for score <2.

搜集汇总

背景与挑战

背景概述

该数据集包含595条福州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，旨在支持精准化运营和个性化客户服务。数据来源于企业自行产生的销售记录，格式为xlsx，更新频次按需进行。