浙江地区淘宝坐垫类消费者分析数据

采集客户消费行为数据，通过客户的最近一次消费时间间隔(R)、最近一段时间内消费频次(F)和最近一段时间内消费金额(M)，采用RFM模型对客户进行价值评级，为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：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)，运用RFM模型结合用户的最近一次活动、用户活动频率和消费金额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。评分＞＝4分的为A级客户，＞＝3小于4的为B级客户，＞＝2小于3的为C 级客户，＜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, and applies the RFM model to perform value grading on customers based on three metrics: recency of last consumption (R), consumption frequency within a recent period (F), and consumption amount within a recent period (M), so as to provide data support for personalized services targeting customers of different value types.
1. Data Processing: Conduct denoising, cleaning, anonymization, aggregation and analysis on the collected raw data.
2. Data Refinement and Scoring: Extract the three core metrics of R, F and M mentioned above. Then, use the RFM model combined with the score rankings of user's last activity, user activity frequency and consumption amount to generate a comprehensive ranking for customers, and finally calculate an overall RFM score. Customers with a score ≥4 are categorized as Class A customers, those with 3 ≤ score <4 as Class B customers, those with 2 ≤ score <3 as Class C customers, and those with score <2 as Class D customers.
3. Provide data support for personalized services for customers of different value types via customer tiered management.

搜集汇总

数据集介绍

特点

该数据集包含浙江地区淘宝坐垫类消费者的1099条行为数据，采用RFM模型对客户进行价值评级，包括R、F、M三个维度的得分和客户分级，旨在为个性化服务提供数据支持。