湖南省淘宝平台食品类客户分级评价数据

采集湖南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，通过对湖南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 curates consumer behavior data of Taobao platform customers in Hunan Province. Leveraging the Recency-Frequency-Monetary (RFM) model, it conducts customer value rating to enable precise operational management, optimize customer value management for Taobao platform customers in Hunan Province to meet personalized demands of customers with different value segments, and provide data support for enterprises in the same industry to deliver personalized services for customer groups of various value types.

The dataset development involves three core stages:
1. Data Processing: Perform noise reduction, data cleaning, anonymization, aggregation and analysis on the collected raw data.
2. Data Scoring and Grading: Utilize the RFM model combined with the score rankings of three indicators—Recency of last consumption (R), consumption frequency in a recent period (F) and consumption amount in a recent period (M)—to generate a comprehensive customer ranking and calculate the final overall RFM score. The specific steps are as follows:
a. Extract the three core indicators first. Classify users based on their last activity time interval: users with the shortest interval since their last activity are ranked highest. Assign scores from 1 to 5, where the top 20% of customers receive 5 points, the subsequent 20% get 4 points, the next 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 activity frequency (F). The top 20% of customers are assigned 5 points for their frequency score, and the scoring rule for the remaining groups follows the same percentile-based logic.
c. For consumption amount (M), sort customers in descending order of their total consumption in the target period. The top 20% get 5 points, and the bottom 20% with the lowest consumption amount receive 1 point, with the rest assigned scores proportionally.
The final RFM total score is calculated using the formula: RFM Score = (R Score) * 0.3 + (F Score) * 0.3 + (M Score) * 0.4. Customers are then categorized into four tiers: Class A customers (score ≥4), Class B customers (3 ≤ score <4), Class C customers (2 ≤ score <3), and Class D customers (score <2).
3. The hierarchical customer management based on the above grading provides data support for enterprises to deliver personalized services tailored to customers of different value types.

搜集汇总

数据集介绍

特点

湖南省淘宝平台食品类客户分级评价数据集包含1480条记录，每月更新，通过RFM模型对客户进行价值评级，旨在实现精准化运营和客户价值管理。