长春地区购买公司软件客户价值评估数据

采集销售记录表中长春地区的数据，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，通过对长春地区客户价值管理，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 data for the Changchun region from the regional sales record table. Three core metrics are calculated for each customer: 1) R: the number of days between the customer's most recent purchase and March 31, 2025 (the end of the analysis period spanning January 1, 2022 to March 31, 2025); 2) F: the total number of purchase transactions conducted by the customer between January 1, 2022 and March 31, 2025; 3) M: the total consumption amount generated by the customer in the same period. The RFM model is applied to grade customer value for precise operational management, so as to satisfy personalized demands of customers at different value levels via targeted customer value management in Changchun. Specifically, Grade A customers will receive monthly return visits and maintenance, Grade B customers will receive quarterly return visits, Grade C customers will receive semi-annual return visits, and Grade D customers will receive annual return visits. In addition, this dataset can provide data support for local enterprises with highly overlapping customer groups to deliver personalized services for customers of different value types.

Data processing steps include desensitization, noise reduction, cleaning, aggregation and analysis of the raw data collected from the sales record table. For data enrichment and scoring: A comprehensive customer ranking is generated using the RFM model combined with the score rankings of the three metrics (R, F, M) to derive an overall RFM total score, with the following specific procedures:
a. Extract the three metrics R, F and M. Score each customer on a 1-5 scale based on the R metric: customers with the shortest interval since their most recent purchase are ranked highest, with the top 20% receiving a score of 5, the next 20% receiving 4, the subsequent 20% receiving 3, the following 20% receiving 2, and the last 20% receiving 1.
b. Score customers on a 1-5 scale based on their purchase frequency (F) in descending order: the top 20% of customers by purchase frequency receive a score of 5, and the remaining customers are assigned scores in descending order of their transaction frequency.
c. Score customers on a 1-5 scale based on their total consumption amount (M) in descending order: the top 20% of customers by total consumption amount receive a score of 5, while the bottom 20% (with the lowest total consumption) receive a score of 1.

The overall RFM total 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 grades based on their final RFM score: Grade A for scores ≥ 4, Grade B for scores ≥ 3 and < 4, Grade C for scores ≥ 2 and < 3, and Grade D for scores < 2.