湖北省淘宝平台食品类客户分级评价数据

采集湖北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，通过对湖北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 compiles consumer behavior data of Taobao platform customers in Hubei Province. We conduct customer value assessment via the RFM model using three core metrics: Recency (R, time elapsed since the most recent consumption), Frequency (F, total number of consumption occurrences in a recent period), and Monetary (M, total consumption amount in a recent period), to enable precise customer operation management. Through targeted customer value management for Taobao platform users in Hubei Province, this work meets the personalized demands of customers with varying value tiers, and provides data support for peer enterprises to deliver personalized services for customers of different value categories.

1. Data Processing: Denoise, clean, anonymize, aggregate and analyze the collected raw data.
2. Data Scoring Workflow: Apply the RFM model to generate a comprehensive customer ranking based on the score rankings of users' Recency (R), Activity Frequency (F) and Consumption Amount (M), and derive the final overall RFM score.
a. Extract the three core metrics: Recency (R, time since last consumption), recent-period consumption frequency (F) and recent-period consumption amount (M). Classify users based on their Recency (R): users with the shortest time interval since their last activity are ranked highest. Assign scores from 1 to 5: the top 20% of customers receive 5 points, the next 20% receive 4 points, the subsequent 20% receive 3 points, the following 20% receive 2 points, and the final 20% receive 1 point.
b. Classify users in descending order of their Activity Frequency (F): the top 20% of customers are assigned 5 points, and the remaining groups follow the same scoring rule.
c. For Consumption Amount (M): the top 20% of customers receive 5 points, while the bottom 20% with the lowest total consumption amount receive 1 point.
The overall RFM score is calculated as: RFM Score = (R Score) * 0.3 + (F Score) * 0.3 + (M Score) * 0.4. Customers are categorized into four tiers: Grade A (score ≥ 4), Grade B (3 ≤ score < 4), Grade C (2 ≤ score < 3), and Grade D (score < 2).
3. Through hierarchical customer value management, this dataset provides data support for peer enterprises to develop personalized services targeting customers of different value types.

搜集汇总

数据集介绍

特点

该数据集包含1481条湖北省淘宝平台食品类客户的消费行为数据，每月更新。通过RFM模型（最近一次消费时间、消费频次、消费金额）对客户进行价值评级，分为A、B、C、D四个等级，适用于精准化运营和个性化服务。