杭州地区信息投放服务客户价值评估数据

本数据聚焦于杭州地区信息投放服务客户的价值度分级评估，反映了不同客户在投放频次、预算投入及服务合作深度方面的差异。对于广告服务商而言，该评估数据有助于识别本地具有高增长潜力的客户群体，优化服务资源配置与客户优先级管理，提升客户粘性与满意度。同时，也为区域数字广告平台、内容服务商及相关运营主体提供客户分层参考，支持其制定更具针对性的客户维系与服务提升策略，增强市场响应效率与客户转化能力。1.数据采集：采集公司信息投放服务在杭州地区的销售情况数据，包括客户编号、销售区域、分析时间、统计期间、上次购买时间、距离上一次购买的天数R（天）、最近一段时间购买频次F（次）、最近一段时间购买金额M（元）等数据字段。其中，最近一段时间指最近30天。
2.数据预处理：对采集到的数据进行清洗，去除重复记录，处理缺失值。
3.数据加工：运用RFM模型并结合该客户的R、F、M的排名，分别得出该客户的R、F、M的得分。赋分规则如下：提取所有客户的R​，R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；提取所有客户的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；提取所有客户的M，前20%的客户在消费金额的分数为5，以此类推。
4.数据处理：（1）RFM得分计算：RFM得分=0.3*R得分+0.3*F得分+0.4*M得分。（2）客户等级划分：评分≥4分（A级客户），3≤评分＜4（B级客户），2≤评分＜3（C级客户），评分＜2（D级客户）。

This dataset focuses on the value grading assessment of customers of information delivery services in Hangzhou, reflecting the differences among customers in terms of delivery frequency, budget investment and depth of service cooperation. For advertising service providers, this assessment data helps identify local customer groups with high growth potential, optimize service resource allocation and customer priority management, and improve customer stickiness and satisfaction. Meanwhile, it also provides reference for customer segmentation for regional digital advertising platforms, content service providers and relevant operating entities, supporting them to formulate more targeted customer retention and service improvement strategies and enhancing market response efficiency and customer conversion capabilities.
1. Data Collection: Collect sales data of the company's information delivery service in Hangzhou, including data fields such as customer ID, sales region, analysis time, statistical period, last purchase time, days R since last purchase (in days), purchase frequency F in the recent period (times), and recent purchase amount M (yuan). Here, the recent period refers to the latest 30 days.
2. Data Preprocessing: Clean the collected data, remove duplicate records, and handle missing values.
3. Data Transformation and Scoring: Apply the RFM model and combine the rankings of the customer's R, F and M values to separately obtain the R, F and M scores of the customer. The scoring rules are as follows: Extract the R values of all customers, sort the customers with the shortest R first, and score 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; Extract the F values of all customers, sort users from highest to lowest, the top 20% of customers get 5 points for user activity frequency, and so on; Extract the M values of all customers, the top 20% of customers get 5 points for consumption amount, and so on.
4. Data Processing: (1) RFM Score Calculation: RFM Score = 0.3 * R Score + 0.3 * F Score + 0.4 * M Score. (2) Customer Level Classification: Customers with a score ≥ 4 are classified as Level A customers, 3 ≤ score < 4 as Level B customers, 2 ≤ score < 3 as Level C customers, and score < 2 as Level D customers.

搜集汇总

背景与挑战

背景概述

该数据集是杭州地区信息投放服务的客户价值评估数据，包含601条记录，每日更新，采用RFM模型对客户进行分级评估，包括R（最近购买时间）、F（购买频次）和M（购买金额）得分计算，最终划分客户等级（A、B、C、D级）。它主要用于帮助广告服务商识别高潜力客户，优化资源配置和提升客户管理效率，数据已通过区块链存证确保可信。