浙江省京东平台食品类客户分级评价数据

通过对京东平台的浙江省用户消费行为数据进行深入分析，采用RFM模型来对用户进行细致的价值评级，通过用户的最近一次消费时间（R）、一定时期内的消费频次（F）以及同一时期内的消费金额（M）三个维度，来评估用户的价值和潜在贡献。通过精细化的用户价值管理，为平台的不同价值用户群体提供个性化的服务方案，不仅有助于优化积分和优惠券等激励措施的运营策略，还能为其他营销活动提供坚实的数据支持。具体而言，通过识别出最近消费、消费频次高、消费金额大的高价值用户，并为其提供更加定制化的服务和优惠，对于消费频次较低或消费金额较小的用户，可设计针对性的营销活动，以激发其消费潜力，提升用户活跃度和忠诚度。1、对从销售记录表中采集到对客户的销售单数和销售金额等信息进行脱敏、降噪、清洗、聚集、分析。2、数据加工：运用RFM模型结合客户在统计分析时间段间隔内距离截止日的最近一次消费时间天数（R）、客户在统计分析时间段之间的最近一段时间消费频次(F)和客户在统计分析时间段之间的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.提取出最近一次消费时间的天数（R）、客户在统计分析时间段之间消费频次(F)和客户在统计分析时间段之间消费金额(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.根据客户在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, 根据客户在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。

Through in-depth analysis of consumer behavior data of users in Zhejiang Province on the JD.com platform, the RFM model is adopted to conduct detailed user value rating, evaluating user value and potential contribution from three dimensions: Recency (R, days since the customer’s most recent purchase relative to the deadline of the statistical analysis period), Frequency (F, consumption frequency within a certain period), and Monetary (M, consumption amount in the same period). Through refined user value management, providing personalized service plans for user groups of different values on the platform not only helps optimize the operational strategies of incentive measures such as points and coupons, but also provides solid data support for other marketing activities. Specifically, for high-value users who have recent consumption, high consumption frequency and large consumption amount, we can provide them with more customized services and preferential treatments; for users with low consumption frequency or small consumption amount, targeted marketing campaigns can be designed to stimulate their consumption potential, thereby improving user activity and loyalty. 1. Desensitization, denoising, cleaning, aggregation and analysis are conducted on information such as the number of sales orders and sales amount of customers collected from the sales record sheet. 2. Data processing: The RFM model is applied to generate a comprehensive ranking for customers based on the score rankings of the three above-mentioned metrics, finally obtaining the overall RFM score. a. Extract the days of the most recent consumption (R), the consumption frequency (F) and the consumption amount (M) of customers within the statistical analysis period for classification. Customers with the shortest interval since the most recent consumption are ranked first. Scoring is conducted from 1 to 5: the top 20% of customers get 5 points, the next 20% get 4 points, the following 20% get 3 points, the next 20% get 2 points, and the last 20% get 1 point. b. Classify users in descending order of their consumption frequency (F) in the recent period. The top 20% of customers get 5 points for their activity frequency score, and so on. c. Classify users based on their consumption amount (M) in the recent period. The top 20% of customers get 5 points for their consumption amount score, and so on. The 20% of customers with the smallest consumption amount get 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 a score ≥4 are classified as Level A customers; those with 3 ≤ score <4 are Level B customers; 2 ≤ score <3 are Level C customers; and those with a score <2 are Level D customers.

搜集汇总

背景与挑战

背景概述

该数据集包含623条记录，每月更新，采用RFM模型对浙江省京东平台食品类用户的消费行为进行分析，通过最近一次消费时间、消费频次和消费金额三个维度进行客户分级，旨在优化个性化服务和营销策略，提升用户活跃度和忠诚度。数据来源于协议获得，格式为xlsx，适用于企业数据管理和精准营销应用。