广州市窝里快购平台客户消费行为分析数据

通过对窝里快购平台广州市地区的用户进行用户画像,根据算法获得用户价值分析,根据分析制定营销策略,提高用户粘性和忠诚度。RFM模型可以与用户属性数据结合,实现精细化的客户细分和精准营销。此外,通过分析用户RFM综合评分的变化趋势,平台可以预测用户生命周期价值,优化用户留存策略。RFM模型通过计算广州市地区用户最近一次消费时间(R)、消费频率(F)和消费金额(M)这三个维度来评估用户价值。R 维度,根据时间（用户最后支付时间）距离分析时间（2024年10月18日）的天数（D）,划分为5个等级: 0≤D≤5为5分，5＜D≤10 为4分，10＜D≤20 为3分，20＜D≤30为2分，D ＞30为1分；F 维度,根据用户在过去180天订单数量（C）,划分为5个等级: 0≤C≤5为1分，5≤C≤10 为2分，10≤C≤20 为3分，20≤C≤30为4分，C≥30为5分；M 维度,根据用户在过去180天消费金额（G）,划分为5个等级，G≥3000为5分，3000≤G＜2000为4分，1000≤G＜2000为3分，500≤G＜1000为2分，0≤G＜500为1分。RFM综合评分（X）=R+F+M，再根据RFM综合评分（X）对客户进行分类，0≤X＜1为新客户，1≤X＜2为种子客户，2≤X＜4 为潜力客户，4≤X＜6为重要客户，X ≥6为核心客户。

We conduct user profiling for users in the Guangzhou region of the Wolikuaigou platform, perform user value analysis using algorithms, and formulate targeted marketing strategies based on the analysis to enhance user stickiness and loyalty. The RFM model can be integrated with user attribute data to achieve refined customer segmentation and precision marketing. Furthermore, by analyzing the changing trends of users' comprehensive RFM scores, the platform can predict customer lifetime value (CLV) and optimize user retention strategies.

The RFM model assesses user value by calculating three core dimensions for users in the Guangzhou region: Recency (R), Frequency (F), and Monetary Value (M).
For the Recency (R) dimension: Users are classified into 5 levels based on the number of days (D) between the user's last payment time and the analysis cutoff date (October 18, 2024): 5 points for 0 ≤ D ≤ 5, 4 points for 5 < D ≤ 10, 3 points for 10 < D ≤ 20, 2 points for 20 < D ≤ 30, and 1 point for D > 30.
For the Frequency (F) dimension: Users are divided into 5 levels based on the number of orders (C) placed by the user in the past 180 days: 1 point for 0 ≤ C ≤ 5, 2 points for 5 ≤ C ≤ 10, 3 points for 10 ≤ C ≤ 20, 4 points for 20 ≤ C ≤ 30, and 5 points for C ≥ 30.
For the Monetary Value (M) dimension: Users are categorized into 5 levels based on their total consumption amount (G) in the past 180 days: 5 points for G ≥ 3000, 4 points for 3000 ≤ G < 2000, 3 points for 1000 ≤ G < 2000, 2 points for 500 ≤ G < 1000, and 1 point for 0 ≤ G < 500.
The comprehensive RFM score (X) is calculated as X = R + F + M. Customers are then segmented based on their comprehensive RFM score (X): new customers for 0 ≤ X < 1, seed customers for 1 ≤ X < 2, potential customers for 2 ≤ X < 4, key customers for 4 ≤ X < 6, and core customers for X ≥ 6.

搜集汇总

数据集介绍

特点

该数据集包含8166条广州市窝里快购平台用户的消费行为记录，应用RFM模型进行用户价值评估和分类，旨在优化营销策略和用户留存。