呼和浩特地区购买公司软件客户价值评估数据

采集销售记录表中呼和浩特地区的数据，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，通过对呼和浩特地区客户价值管理，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 is collected from sales records of the Hohhot region. It applies the RFM model to conduct customer value rating, based on three indicators: R (the number of days between a customer's most recent consumption transaction and March 31, 2025 within the analysis period from January 1, 2022 to March 31, 2025), F (the total number of consumption transactions made by the customer during the analysis period), and M (the total consumption amount of the customer during the analysis period), to realize precise operational management, meet the personalized demands of customers of different value tiers via customer value management in the Hohhot region. Specifically, Level A customers will be revisited and maintained once a month, Level B customers once every quarter, Level C customers once every six months, and Level D customers once a year. Additionally, this dataset can provide data support for local enterprises with highly overlapping customer groups to deliver personalized services for different value types of customers.
Data processing: Desensitize, denoise, clean, aggregate and analyze the data collected from the sales record table.
2. Data scoring and processing: Use the RFM model combined with the score rankings of R, F and M to conduct a comprehensive customer ranking, and calculate the final total RFM score.
a. R indicator scoring: Rank customers in ascending order of the days since their most recent consumption relative to March 31, 2025. Assign scores from 1 to 5 with a 20% percentile interval: the top 20% of customers (shortest R interval) receive 5 points, the next 20% receive 4 points, the subsequent 20% receive 3 points, the next 20% receive 2 points, and the bottom 20% receive 1 point.
b. F indicator scoring: Rank customers in descending order of their total number of consumption transactions during the analysis period. The top 20% receive 5 points, with remaining scores assigned following the same 20% percentile rule.
c. M indicator scoring: Rank customers in descending order of their total consumption amount during the analysis period. The top 20% receive 5 points, the bottom 20% receive 1 point, and the remaining scores are assigned per the 20% percentile interval rule.
The total RFM score is calculated as: Total RFM Score = 0.3 * (R Score) + 0.3 * (F Score) + 0.4 * (M Score)
Customers are classified into four tiers based on their total RFM 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

该数据集包含720条呼和浩特地区购买公司软件的客户数据，采用RFM模型评估客户价值，包括R值、F值、M值及其得分、RFM总评分和客户等级等字段，用于精准化运营和个性化服务。