成都地区智能家居咨询客户分级评价数据

采集会员管理系统(会员基础信息表)，DRP销售系统(销售记录表)，会员系统(会员登录数据表)中成都地区的数据，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，通过对成都地区客户价值管理，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。

（2）应用场景：采集销售记录表中成都地区的数据，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，通过对成都地区客户价值管理，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。数据处理：对从销售记录表中采集到的数据进行脱敏、降噪、清洗、聚集、分析。2、数据加工：运用RFM模型结合客户最近一次消费时间（R）、客户最近一段时间消费频次(F)和客户最近一段时间消费金额(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.提取出客户最近一次消费时间（R）、客户最近一段时间消费频次(F)和客户最近一段时间消费金额(M)进行分类，最近一次消费时间间隔最短的客户排在最上面。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.根据客户最近一段时间消费频次(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, 根据客户最近一段时间消费金额(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=0.3*（R得分）+0.3*（F得分）+0.4*（M得分） 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。

This dataset collects data from the Chengdu region across three systems: Member Management System (member basic information table), DRP Sales System (sales record table), and Member System (member login data table). Using the Recency (R, time since last purchase), Frequency (F, number of purchases in a recent period), and Monetary (M, total purchase amount in a recent period) metrics, we conduct customer value grading via the RFM model to enable precise operational management. By managing customer value in the Chengdu region, we meet the personalized needs of customers with different value tiers, and provide data support for personalized services targeting customers of various value types for enterprises in the same industry.

2. Application Scenario: Collect data from the Chengdu region in the sales record table, use the R, F, M metrics to grade customer value via the RFM model, achieve precise operational management, manage customer value in the Chengdu region to meet personalized needs of customers with different value tiers, and provide data support for personalized services for customers of various value types for same-industry enterprises. Data Processing: Perform desensitization, denoising, cleaning, aggregation and analysis on the data collected from the sales record table.

2. Data Scoring and Processing Workflow: Use the RFM model combined with the score rankings of customers' Recency (R), recent purchase frequency (F) and recent total purchase amount (M) to conduct a comprehensive ranking of customers, and finally derive an overall RFM score.
a. For Recency (R): Extract the three metrics for classification, customers with the shortest time interval since last purchase are ranked highest. Score customers from 1 to 5: the top 20% of customers receive 5 points, the next 20% receive 4 points, the following 20% receive 3 points, the subsequent 20% receive 2 points, and the last 20% receive 1 point.
b. For Frequency (F): Classify customers in descending order of their recent purchase frequency. The top 20% of customers get 5 points for their frequency score, and so on.
c. For Monetary (M): The top 20% of customers get 5 points based on their recent total purchase amount, and so on. The 20% of customers with the lowest total purchase amount receive 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 an overall RFM score ≥4 are classified 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.

搜集汇总

数据集介绍

特点

该数据集包含734条成都地区智能家居咨询客户的分级评价数据，采用RFM模型对客户进行价值评级，支持精准化运营和个性化服务。