约训系统客户价值RFM分析数据

通过“驾考约训平台”系统采集了客户的预约信息和消费行为数据，通过这些数据，能够执行精细化的客户关系管理。根据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模型对进行价值评级，从而识别出高价值客户和核心客户。针对不同价值类型的客户提供个性化服务，比如为高价值客户提供VIP预约通道和额外的优惠，以增强客户忠诚度和提升服务体验。1、数据采集：通过“驾考约训平台”系统采集客户的预约信息和消费行为数据：序号、驾校、训练日期、预约开始时间、预约结束时间等字段；2、数据处理：对采集到的数据进行清洗、分类汇总，对驾校进行匿名化处理；3、数据加工：从原始数据中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)，根据RFM模型计分法对用户进行分层管理，RFM计分规则如下：0≤R<4为5分，4≤R<8 为4分，8≤R<12为3分，12≤R<16 为2分，16<R 为1分；0≤M≤13000 为1分，13000<M≤26000 为2分，26000<M≤39000 为3分，39000<M≤52000为 4分，52000<M 为5分；0≤F≤45 为1分，45<F≤90 为2分，90<F≤140 为3分，140<F≤185 为4分，185<F为5分；计算每条数据的RFM综合得分X，根据公司要求对客户进行分层，1≤X≤5 为基础客户，5<X≤10 为核心客户，10<X 为高价值客户；；4、数据应用：采用RFM模型对进行价值评级，从而识别出高价值客户和核心客户、基础客户。Customer reservation and consumption behavior data were collected via the "Driving Test Reservation and Training Platform" system, enabling refined customer relationship management. Based on the recency of a customer's last consumption (R), their consumption frequency (F) within a specified recent period, and their total consumption amount (M) within the same period, the RFM model is applied to conduct value rating, so as to identify high-value customers and core customers. Personalized services are tailored for customers of different value tiers: for example, high-value customers are offered VIP reservation channels and exclusive discounts, to enhance customer loyalty and improve service experience. 1. Data Collection: Customer reservation and consumption behavior data are collected through the "Driving Test Reservation and Training Platform" system, including fields such as serial number, driving school, training date, reservation start time, and reservation end time. 2. Data Preprocessing: The collected data are cleaned, classified and aggregated, and anonymization processing is performed on the driving school information. 3. Data Feature Extraction and Scoring: Extract the recency of last consumption (R), consumption frequency within the recent period (F), and consumption amount within the recent period (M) from the raw dataset. Then, perform hierarchical user management based on the RFM model scoring framework. The specific RFM scoring rules are as follows: - For R: 0 ≤ R < 4 → 5 points; 4 ≤ R < 8 → 4 points; 8 ≤ R < 12 → 3 points; 12 ≤ R < 16 → 2 points; R ≥ 16 → 1 point - For M: 0 ≤ M ≤ 13000 → 1 point; 13000 < M ≤ 26000 → 2 points; 26000 < M ≤ 39000 → 3 points; 39000 < M ≤ 52000 → 4 points; M > 52000 → 5 points - For F: 0 ≤ F ≤ 45 → 1 point; 45 < F ≤ 90 → 2 points; 90 < F ≤ 140 → 3 points; 140 < F ≤ 185 → 4 points; F > 185 → 5 points Calculate the comprehensive RFM score X for each customer record. Customers are stratified according to company requirements: basic customers (1 ≤ X ≤ 5), core customers (5 < X ≤ 10), and high-value customers (X > 10). 4. Data Application: The RFM model is used for value rating to identify high-value customers, core customers, and basic customers.

搜集汇总

数据集介绍

特点

该数据集为驾考约训平台的客户预约和消费行为数据，通过RFM模型对客户进行价值评级，支持精细化客户关系管理。数据集包含2263条记录，每日更新，适用于识别高价值客户和提供个性化服务。