上海地区游戏充值客户分级评价数据

在游戏行业中，充值客户的行为数据（最近一次充值时间、充值频率、充值总金额）是衡量用户价值的核心指标。采集上海地区游戏充值客户的最近一次充值时间R(天数)、充值频次F（次数）、充值总金额M（元)，采用RFM模型对客户进行分级评价。用RFM分析方法把客户分为ABCD四级，对细分过后的不同客户采取相应营销策略，进行精准有效的促销推送。通过 RFM 划分客户群体后，游戏运营可实现：
1、精准营销：给核心A级客户推送高端活动，给D级流失客户推送大额福利券。
2、资源倾斜：优先保障高价值A级客户的服务，设置VIP专属客服。
3、风险预警：对最近一次充值时间R 值大的客户提前回访，防止流失。
RFM客户分级评价可为同行业企业管理不同等级的客户，实现精准个性化服务提供数据支持。数据处理：1、对从上海地区采集到的数据进行脱敏、降噪、清洗、聚集、分析。2、数据加工：基于RFM模型，结合客户最近一次充值时间R(天数)、充值频次F（次数）、充值总金额M（元)对客户进行打分，得到：R得分、F得分、M得分。 用percentrank函数对客户最近一次充值时间R(天数)、充值频次F（次数）、充值总金额M（元)进行评分，（a）、最近一次充值时间R(天数)间隔最短的客户排在最上面。按照从1-5评分，前20%的客户得5分，接下来的20%用户得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）、根据客户充值频次F（次数）从高到底依次对用户进行评分，前20%的客户在充值频次F（次数）的分数为5，以此类推。 (c)、根据客户充值总金额M（元)，前20%的客户在充值总金额M（元)的分数为5，以此类推。充值总金额M（元)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。3、通过对客户进行分级管理，满足不同等级客户的个性化需求， R、F、M 三个维度相互关联，共同构成对游戏充值客户的立体画像。三者结合可精准划分客户层级，为游戏行业分级运营、提高营销效率提供数据支撑。

In the gaming industry, behavioral data of in-game paying customers (including last recharge time, recharge frequency, and total recharge amount) are core indicators for measuring user value. This dataset collects the last recharge time R (in days), recharge frequency F (number of times), and total recharge amount M (in yuan) of game paying customers in Shanghai, and adopts the RFM model to conduct hierarchical evaluation of customers.

Using RFM analysis, customers are divided into four tiers: A, B, C, and D. Corresponding marketing strategies are implemented for segmented customer groups to deliver precise and effective promotional pushes. After grouping customers via RFM, game operators can achieve the following goals:
1. Precision Marketing: Push high-end activities for core Class A customers, and distribute large-value welfare coupons to Class D churned customers.
2. Prioritized Resource Allocation: Prioritize service support for high-value Class A customers, and set up VIP exclusive customer service.
3. Risk Early Warning: Conduct early follow-up visits for customers with a large R value (i.e., long interval since last recharge) to prevent customer churn.

The RFM-based customer hierarchical evaluation can provide data support for enterprises in the same industry to manage customers of different tiers and deliver precise personalized services.

Data processing steps are as follows:
1. Desensitize, denoise, clean, aggregate, and analyze the data collected from the Shanghai region.
2. Data enrichment and scoring: Based on the RFM model, score customers using the three dimensions of last recharge time R (in days), recharge frequency F (number of times), and total recharge amount M (in yuan) to obtain R score, F score, and M score. The PERCENTRANK function is used to score the three indicators:
(a) For the last recharge time R (in days): Customers with the shortest interval since last recharge are ranked first. Scores are assigned from 1 to 5: the top 20% of customers receive 5 points, the next 20% receive 4 points, the subsequent 20% receive 3 points, the following 20% receive 2 points, and the last 20% receive 1 point.
(b) For recharge frequency F (number of times): Customers are scored in descending order of F, with the top 20% receiving 5 points, and so on for subsequent tiers.
(c) For total recharge amount M (in yuan): The top 20% of customers receive 5 points, while the bottom 20% (those with the smallest total recharge 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 are classified as follows: Class A (score ≥ 4), Class B (3 ≤ score < 4), Class C (2 ≤ score < 3), and Class D (score < 2).
3. Implement hierarchical customer management to meet the personalized needs of customers at different tiers. The three dimensions of R, F, and M are interrelated and jointly form a three-dimensional portrait of in-game paying customers. Their combined use can accurately divide customer tiers, providing data support for tiered operations in the gaming industry and improving marketing efficiency.

搜集汇总

背景与挑战

背景概述

该数据集包含上海地区游戏充值客户的667条记录，基于RFM模型（最近一次充值时间、充值频次、充值总金额）对客户进行A、B、C、D四级评价，旨在通过精准分级支持游戏行业的个性化营销和客户管理。数据集每季度更新，采用Excel格式，适用于资源优化和流失预警等应用场景。