江门地区购买公司软件客户价值评估数据

采集销售记录表中江门地区的数据，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，通过对江门地区客户价值管理，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Collect customer data of the Jiangmen region from the enterprise sales record table. Three core metrics are utilized for customer value segmentation via the RFM model: 1) R (Recency): Number of days from the customer's most recent purchase within the analysis window (January 1, 2022 to March 31, 2025) to March 31, 2025 (the analysis cutoff date); 2) F (Frequency): Total purchase frequency of the customer during the analysis window; 3) M (Monetary): Total purchase amount of the customer during the analysis window. This work aims to enable precise operational management, conduct customer value management in the Jiangmen region, and meet personalized demands of customers with different value tiers. Targeted maintenance strategies are formulated for each customer tier: Class A customers receive monthly return visits and maintenance; Class B customers receive quarterly return visits and maintenance; Class C customers receive semi-annual return visits and maintenance; Class D customers receive annual return visits and maintenance. Additionally, this dataset can provide data support for enterprises with highly overlapping customer groups in the Jiangmen region to deliver personalized services for customers of different value types. Data Processing: Desensitize, denoise, clean, aggregate and analyze the data collected from the sales record table. 2. Data Enrichment and Scoring: Conduct a comprehensive customer ranking by leveraging the RFM model and the score rankings of the three metrics (R, F, M) defined above, and ultimately derive the overall RFM score. a. Extract the three metrics (R, F, M) for all customers, then rank customers in ascending order of days since their most recent purchase (i.e., customers with shorter intervals are ranked higher). Assign scores from 1 to 5: the top 20% of ranked customers receive 5 points, the subsequent 20% receive 4 points, the next 20% receive 3 points, the following 20% receive 2 points, and the last 20% receive 1 point. b. Rank customers in descending order of their total purchase frequency (F) during the analysis window. Assign scores from 1 to 5 following the same percentile-based rule as above: the top 20% get 5 points, and so on for the remaining groups. c. Rank customers in descending order of their total purchase amount (M) during the analysis window. Assign scores from 1 to 5 following the same percentile-based rule: the top 20% get 5 points, and the bottom 20% (with the lowest total purchase amount) get 1 point. The overall RFM score is calculated as: RFM Score = 0.3 * R_Score + 0.3 * F_Score + 0.4 * M_Score. Customers are classified into four tiers based on their overall RFM score: Class A (score ≥ 4), Class B (3 ≤ score < 4), Class C (2 ≤ score < 3), and Class D (score < 2).

搜集汇总

背景与挑战

背景概述

该数据集包含江门地区720条企业客户数据，采用RFM模型对客户进行价值评级，分为A、B、C、D四个等级，用于精准化运营和客户价值管理。