安徽省闯货平台客户消费行为分析数据

通过对闯货平台安徽地区的用户进行分层,平台可以识别高价值用户,提供差异化的服务和营销策略,提高用户粘性和忠诚度。RFM模型还可以与其他用户属性数据结合,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。对于R维度,根据用户最后支付时间距离当前分析时间的天数（D）,划分为5个等级: 0≤D≤4为5分，4＜D≤7 为4分，7＜D≤15 为3分，15＜D≤29为2分，D ＞29为1分；对于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≤1为1分，2≤C≤5 为2分，6≤C≤11 为3分，12≤C≤19为4分，C≥20为5分；对于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≥2000为5分，1200≤G＜2000为4分，800≤G＜1200为3分，400≤G＜800为2分，0≤G＜400为1分。RFM综合评分（X）=0.3*R+0.4*F+0.6*M，再根据RFM综合评分（X）对客户进行分类，0≤X＜1为一般客户，1≤X＜2为新客户，2≤X＜4 为潜力深耕客户，4≤X＜6为重要维系客户，X ≥6为高粘度客户，基于消费频次、消费金额等不同维度获得的聚类分组成果，对聚类分组数量和分组阀值、以及维度权重进行人为干预，使客户分类趋于合理。

By stratifying users of the Chuanghuo Platform in the Anhui region, the platform can identify high-value users, provide differentiated services and marketing strategies, and enhance user stickiness and loyalty. The RFM model can also be integrated with other user attribute data to enable refined customer segmentation and precise marketing. Moreover, 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: Recency (R, time since the last consumption), Frequency (F, consumption frequency), and Monetary (M, consumption amount).
For the R dimension, based on the number of days (D) between the user’s last payment timestamp and the current analysis time, users are graded into 5 levels: 5 points for 0≤D≤4, 4 points for 4<D≤7, 3 points for 7<D≤15, 2 points for 15<D≤29, and 1 point for D>29.
For the F dimension, based on the total number of orders (C) placed by the user in the past 180 days, it is divided into 5 levels: 1 point for 0≤C≤1, 2 points for 2≤C≤5, 3 points for 6≤C≤11, 4 points for 12≤C≤19, and 5 points for C≥20.
For the M dimension, based on the total consumption amount (G) of the user in the past 180 days, it is divided into 5 levels: 5 points for G≥2000, 4 points for 1200≤G<2000, 3 points for 800≤G<1200, 2 points for 400≤G<800, and 1 point for 0≤G<400.
The comprehensive RFM score (X) is calculated as: X = 0.3*R + 0.4*F + 0.6*M. Customers are then categorized based on this comprehensive score: general customers for 0≤X<1, new customers for 1≤X<2, potential deep-cultivation customers for 2≤X<4, key retention customers for 4≤X<6, and high-stickiness customers for X≥6. Based on the clustering grouping results derived from dimensions such as consumption frequency and consumption amount, manual interventions are conducted on the number of clustering groups, grouping thresholds and dimension weights to optimize the rationality of customer classification.