无锡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无锡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模型通过计算无锡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。

By conducting user profiling for users in the Wuxi region of the Wolikuaigou platform, deriving user value analysis via algorithms, and formulating marketing strategies based on the analysis to improve user stickiness and loyalty. The RFM model can be combined with user attribute data to achieve refined customer segmentation and precision marketing. In addition, 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 evaluates user value by calculating three core dimensions: Recency (R) of the user's last payment time, Frequency (F) of consumption, and Monetary (M) of total consumption amount for users in the Wuxi region. For the Recency (R) dimension, users are categorized into 5 levels based on the number of days (D) between their 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 their total order count (C) 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 (M) dimension, users are classified 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 grouped into different segments 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, important customers for 4 ≤ X < 6, and core customers for X ≥ 6.

搜集汇总

数据集介绍

特点

该数据集包含无锡市窝里快购平台的客户消费行为数据，通过RFM模型分析用户价值，支持客户细分和精准营销策略的制定。数据集每年更新，适用于提高用户粘性和忠诚度的应用场景。