浙江区域用户对无比滴消费分析数据

通过对京东平台浙江区域的旗舰店用户的最近消费日期、消费频次、消费金额来衡量其价值和创利能力。电商平台的商家可以通过该方法对用户进行分类分级，来帮助商家制定个性化运营策略，以实现精细化运营，提高用户满意度和忠诚度。对于被多家商家标为高价值的用户，平台可以为用户提供极速退款等便利措施，促进电商行业良好发展，为客户提供更优质的服务，实现互利共赢。1.数据采集：采集产品运营数据，订单时间、订单金额、上一次消费时间等数据。
2.数据处理：对采集到数据进行去重、合并、累加，便于分析使用。数据清洗时，查看是否有缺失值和异常值，对数据进行整理。并基于RFM模型：R表示客户最近一次购买的时间有多远，F表示客户在时间内购买的次数，M 表示客户在时间内购买的金额。R=365-（截止日期-最近一次购买日期），日期越近，数值越大；F=购买次数；M=消费金额。将所有用户分别按照R、F、M值，由大排列到小，前20%的客戶给5分，前20%~40%的客戶给4分，以此类推，最后的20%给1分，分别记为r，f，m。
3.数据分析：根据RFM的数值可以对用户进行分类分级，客户类型=if（and（r=5，f=5，m=5），“高价值客户”，if（and（r=5，m＜5），“高潜力客户”，if（and（r＜5，m＝5），“重点维护客户”，if（and（r＝1，f＝1，m＝1），“无效客户”，“一般客户”））））
4.数据应用：可以推测用户的异动状况，根据用户流失的可能性，列出用户列表，就可以把重点放在贡献度高且流失机会也高的用户上，重点制定召回策略。

This dataset measures the value and profitability of users from flagship stores under JD.com's Zhejiang region based on their latest consumption date, consumption frequency and consumption amount. E-commerce merchants can use this method to classify and grade users, helping them formulate personalized operation strategies to achieve refined operations, improve user satisfaction and loyalty. For users marked as high-value by multiple merchants, the platform can provide preferential measures such as instant refunds, which promotes the healthy development of the e-commerce industry, provides better services for customers and achieves win-win results.
1. Data Collection: Collect product operation data, including order time, order amount, last consumption time and other related data.
2. Data Processing: Deduplicate, merge and accumulate the collected data to facilitate analysis. During data cleaning, check for missing values and outliers, and organize the data. Based on the RFM model: R represents the time elapsed since the customer's last purchase, F represents the number of purchases made by the customer within a specific period, and M represents the total purchase amount of the customer within the period. The calculation formulas are as follows: R = 365 - (analysis cutoff date - latest purchase date), where a closer purchase date results in a larger R value; F = number of purchases; M = total consumption amount. Rank all users in descending order according to their R, F and M values respectively, assign a score of 5 to the top 20% of customers, 4 points to the top 20%~40%, and so on, with the bottom 20% receiving a score of 1, and record the scores as r, f and m respectively.
3. Data Analysis: Users can be classified and graded based on their RFM values. The customer type is determined by the following logical judgment: Customer Type = IF(AND(r=5, f=5, m=5), "High-Value Customer", IF(AND(r=5, m<5), "High-Potential Customer", IF(AND(r<5, m=5), "Key Maintenance Customer", IF(AND(r=1, f=1, m=1), "Invalid Customer", "General Customer"))))
4. Data Application: Abnormal changes of users can be predicted. By listing user lists according to the possibility of user churn, priority can be given to users with high contribution and high churn risk, and targeted win-back strategies can be formulated.

搜集汇总

数据集介绍

特点

该数据集包含浙江区域用户在京东平台对无比滴的消费数据，共1645条记录，每季度更新。通过RFM模型分析用户消费行为，帮助商家进行用户分类和精细化运营。