河北省淘宝平台食品类客户分级评价数据

采集河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，通过对河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 collects customer consumption behavior data from the Taobao Platform in Hebei Province. It adopts the RFM model to conduct value grading for customers based on three metrics: time since last consumption (R), consumption frequency over a recent period (F), and total consumption amount over a recent period (M), so as to realize precise operational management. By managing the customer value of Taobao platform users in Hebei Province, it meets the personalized demands of customers with different value levels, and provides data support for enterprises in the same industry to carry out personalized services for customers of different value types.
1. Data Processing: Denoise, clean, anonymize, aggregate and analyze the collected raw data.
2. Data Processing & Scoring: Use the RFM model to perform a comprehensive ranking of customers based on the score rankings of their time since last consumption (R), consumption frequency (F) and consumption amount (M), and finally obtain an overall RFM score.
a. Extract the three metrics of time since last consumption (R), recent consumption frequency (F) and recent consumption amount (M). Classify users according to their time since last consumption (R): users with the shortest time interval since last consumption are ranked first. Score users from 1 to 5: the top 20% of customers get 5 points, the next 20% get 4 points, the subsequent 20% get 3 points, the next 20% get 2 points, and the last 20% get 1 point.
b. Classify users in descending order of their consumption frequency (F): the top 20% of customers get 5 points, and the rest are scored in the same way.
c. For consumption amount (M), the top 20% of customers get 5 points, and so on; the 20% of customers with the lowest consumption amount get 1 point.
The overall RFM score is calculated as: RFM Score = 0.3 * R_score + 0.3 * F_score + 0.4 * M_score. Customers with a score greater than or equal to 4 are classified as Class A customers, those with a score between 3 and 4 (inclusive of 3, exclusive of 4) as Class B, those with a score between 2 and 3 (inclusive of 2, exclusive of 3) as Class C, and those with a score lower than 2 as Class D.
3. Through tiered customer management, this dataset provides data support for enterprises in the same industry to provide personalized services for customers of different value types.

搜集汇总

数据集介绍

特点

该数据集通过RFM模型对河北省淘宝平台食品类客户进行分级评价，包含1487条记录，每月更新，适用于精准化运营和客户价值管理。