贵州省淘宝平台食品类客户分级评价数据

采集贵州省淘宝平台客户消费行为数据，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，通过对贵州省淘宝平台客户价值管理，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：对采集到的数据进行降噪、清洗、脱敏、聚集、分析。
2、数据加工：运用RFM模型结合用户的最近一次活动（R）、用户活动频率（F）和消费金额（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（R）、最近一段时间消费频次(F)、最近一段时间消费金额(M)，将用户按照最近一次活动（R）进行分类，最近一次活动时间间隔最短的用户排在最上面。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.根据客户活动频率（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.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=（R）得分*0.3+（F）得分*0.3+（M）得分*0.4。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。
3、通过对客户的分级管理，为不同价值类型的客户个性化服务提供数据支持。

This dataset collects consumer behavior data of Taobao platform customers in Guizhou Province. The RFM model is employed to perform value grading for customers based on three core metrics: Recency (R, time elapsed since the most recent consumption), Frequency (F, total number of consumption activities within a recent period), and Monetary (M, total consumption amount within a recent period), aiming to achieve precise customer operation management. Through customer value management for Taobao platform customers in Guizhou Province, this dataset meets the personalized demands of customers with different value tiers, and provides reliable data support for personalized service provision for customers of various value types by enterprises in the same industry.
1. Data Processing: Denoise, clean, anonymize, aggregate and analyze the collected raw consumer behavior data.
2. Data Refinement and Scoring: Conduct comprehensive ranking of customers by leveraging the RFM model combined with the score rankings of the three metrics (R, F, M), and finally derive the overall RFM score.
a. Extract the three metrics of Recency (R), Frequency (F) and Monetary (M). Classify users based on their latest consumption time, and sort users in ascending order of the time interval since their most recent consumption. Assign scores ranging from 1 to 5 as follows: the top 20% of customers receive 5 points, the next 20% get 4 points, the subsequent 20% obtain 3 points, the following 20% get 2 points, and the last 20% are awarded 1 point.
b. Sort users in descending order of their consumption frequency (F). The top 20% of customers are assigned 5 points for their frequency score, and the rest follow the same scoring pattern.
c. For the consumption amount (M), the top 20% of customers get 5 points, following the same scoring rule for the remaining customers. The 20% of customers with the lowest total consumption amount are given 1 point.
The overall RFM 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 tiers: Grade A customers with a score ≥ 4, Grade B customers with 3 ≤ Score < 4, Grade C customers with 2 ≤ Score < 3, and Grade D customers with a Score < 2.
3. Hierarchical Customer Management Support: Through the hierarchical management of customer value tiers, this dataset provides data support for enterprises in the same industry to deliver personalized services for customers of different value types.