浙江省淘宝平台食品类客户分级评价数据

采集浙江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，通过对浙江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 Zhejiang Province. The RFM model is utilized to assess customer value using three core metrics: Recency (R, time since the most recent consumption), Frequency (F, total consumption count within a specified recent period), and Monetary Value (M, total consumption amount within the same recent period), to facilitate precise customer operation management. Through value-based management of Taobao customers in Zhejiang Province, this work caters to the personalized needs of customers across different value tiers, and offers data support for peer enterprises to deliver personalized services for customers of varying value types. 1. Data Preprocessing: Denoise, clean, anonymize, aggregate and analyze the collected raw consumer behavior data. 2. Data Scoring and Enrichment: The RFM model is applied to generate a comprehensive customer ranking based on the score rankings of the three metrics (most recent consumption R, consumption frequency F, and consumption amount M), ultimately deriving an overall RFM score. a. Extract the three metrics of Recency (R), Frequency (F) and Monetary Value (M). First, sort users by their most recent consumption time (R), with customers having the shortest interval since their last consumption ranked first. Assign scores from 1 to 5 using quintile segmentation: the top 20% of customers are awarded 5 points, the next 20% get 4 points, the subsequent 20% receive 3 points, the following 20% get 2 points, and the last 20% are given 1 point. b. Sort users in descending order of their consumption frequency (F): the top 20% of customers obtain 5 points for their frequency score, and the remaining groups follow the same quintile-based scoring rule. c. For the consumption amount (M) metric, the top 20% of customers receive 5 points, and the 20% of customers with the lowest total consumption amount are assigned 1 point, adhering to the same quintile segmentation method. The overall RFM score is calculated via the formula: RFM Score = (R score) × 0.3 + (F score) × 0.3 + (M score) × 0.4. Customers are categorized into four value 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. Tiered Customer Management: Through hierarchical classification of customer values, this dataset provides data support for enterprises to implement personalized services targeting customers of different value types.

搜集汇总

数据集介绍

特点

该数据集通过RFM模型对浙江省淘宝平台食品类客户进行分级评价，包含1500条数据，每月更新，适用于精准化运营和客户价值管理。