青海省淘宝平台食品类客户分级评价数据

采集青海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，通过对青海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 Qinghai Province. We utilize the RFM model to conduct customer value rating based on three core metrics: Recency (R, the most recent consumption time), Frequency (F, recent consumption frequency), and Monetary (M, recent consumption amount), aiming to realize precise operational management. By managing the customer value of Taobao platform users in Qinghai Province, this work meets the personalized demands of customers with different value tiers, and provides data support for personalized services targeting customers of various value types for enterprises in the same industry.

1. Data Preprocessing: Noise reduction, data cleaning, data anonymization, aggregation and exploratory analysis are performed on the collected raw data.

2. Data Processing & Scoring: A comprehensive customer ranking is generated by combining the score rankings of the three RFM metrics, and an overall RFM score is finally derived.
a. Extract the three metrics: Recency (R, most recent consumption time), Frequency (F, recent consumption frequency), and Monetary (M, recent consumption amount). Classify users based on their Recency (R) values, ranking users with the shortest time interval since their last consumption first. Assign scores from 1 to 5: the top 20% of customers receive 5 points, the next 20% get 4 points, the following 20% get 3 points, the next 20% get 2 points, and the last 20% get 1 point.
b. Classify users in descending order based on their consumption frequency (F). The top 20% of customers are awarded 5 points for their frequency score, and the remaining users are assigned scores following the same percentile-based rule.
c. For the consumption amount (M) metric, the top 20% of customers get 5 points, and so on. The 20% of customers with the lowest 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 with a score ≥4 are categorized as Class A customers, those with 3 ≤ score <4 as Class B, 2 ≤ score <3 as Class C, and those with score <2 as Class D.

3. Through hierarchical customer management, this dataset provides data support for personalized services tailored to customers of different value types for relevant enterprises.

搜集汇总

数据集介绍

特点

该数据集为青海省淘宝平台食品类客户分级评价数据，包含1484条记录，每月更新，采用RFM模型对客户进行价值评级，适用于精准化运营和客户价值管理。