西安地区元宇宙名片客户分级评价数据

采集管理后台中西安地区的数据，通过客户的最近一次消费时间（R）、最近一段时间消费频次(F)、最近一段时间消费金额(M)， 采用 RFM 模型对客户进行价值评级，实现精准化运营，通过对西安地区客户价值管理，满足不同价值客户的个性化需求。并为同行业企业不同价值类型的客户个性化服务提供数据支持。1、数据处理：对从管理后台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模型结合客户最近一次消费时间（R）、客户最近一段时间消费频次(F)和客户最近一段时间消费金额(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.提取出客户最近一次消费时间（R）、客户最近一段时间消费频次(F)和客户最近一段时间消费金额(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.根据客户最近一段时间消费频次(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, 根据客户最近一段时间消费金额(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。

Data for the Xi'an region was collected from the enterprise customer management backend. The RFM model is adopted to conduct customer value rating based on three metrics: Recency (R, time since the customer's most recent purchase), Frequency (F, purchase frequency over a recent specified period), and Monetary (M, total purchase amount over a recent specified period), so as to enable precise operational management. By managing customer value in the Xi'an region, it meets the personalized demands of customers across different value segments, and provides data support for personalized service offerings targeting customers of different value types for peer enterprises.
1. Data Processing: The collected backend data is subjected to desensitization, denoising, cleaning, aggregation and analysis.
2. Data Refinement: A comprehensive customer ranking is generated by combining the score rankings of Recency (R), Frequency (F) and Monetary (M) metrics using the RFM model, and the final overall RFM score is calculated.
a. Extract the three metrics of R, F and M for classification. Sort customers by the shortest time elapsed since their most recent purchase in ascending order, and assign scores from 1 to 5: the top 20% of customers receive a score of 5, the subsequent 20% get 4, followed by 3, 2, and the bottom 20% get a score of 1.
b. Classify customers based on their purchase frequency (F) over the recent specified period in descending order, with the top 20% receiving a score of 5 for purchase frequency, and follow the same quintile-based scoring rule for the remaining groups.
c. For the customer's total purchase amount (M) over the recent specified period, the top 20% of customers receive a score of 5 following the same scoring rule, while the bottom 20% with the lowest total purchase amount get a score of 1.
The overall RFM score is calculated as: RFM Score = 0.3 * R_score + 0.3 * F_score + 0.4 * M_score.
Customers are categorized into four value tiers: 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

数据集介绍

特点

该数据集为西安地区元宇宙名片客户分级评价数据，包含595条记录，采用RFM模型对客户进行价值评级，支持精准化运营和客户价值管理。