团购客户价值分析数据
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通过对历史下单团购客户进行分层，企业可以更加准确地了解市场动态和用户行为，将有限的资源投入到最具潜力的用户群体上，有助于提高营销活动的效率并增加用户的满意度和忠诚度。 团购用户的多样化需求也促使商家和平台不断创新产品和服务，这种创新竞争也推动了整个行业的进步和发展。 另外团购活动以社区为单位进行，这有助于增强社区居民之间的联系和互动。通过共同参与团购，居民之间可以建立更加紧密的关系，增强社区凝聚力的同时也有助于减少过度包装等造成的资源浪费。1、数据采集：从棒集团购后台管理系统导出团购相关数据； 2、数据处理：以客户手机号作为唯一标识，对数据进行清洗、去除无效数据和极限数据等操作； 3、算法加工：通过AVERAGE函数计算所有客户消费金额的平均数，通过RFM模型结合定档法计算客户的消费金额定档值Mn=IF(Mn>AVERAGE(M),1,0)，消费频次定档值Fn=IF(Fn>AVERAGE(F),1,0)，最近一次消费至8月31日时间定档值Rn=IF(Rn>AVERAGE(R),0,1)，再根据RFM模型客户分层规则将就客户分为8个层级，R+F+M=3为高价值客户、R+M=2且F=0为重点发展客户、F+M=2且R=0为重点保持客户、R+F=0且M=1为重点挽回客户、R+F=2且M=0为一般价值客户、F+M=0且R=1为一般发展客户、R+M=0且F=1为一般保持客户、R+F+M=0为潜在客户；
By segmenting historical group-buying customers who have placed orders, enterprises can gain a more accurate understanding of market trends and user behaviors, allocate limited resources to the most promising user segments, which helps improve the efficiency of marketing campaigns and enhance user satisfaction and loyalty. The diversified demands of group-buying users also drive merchants and platforms to continuously innovate their products and services, and such innovative competition promotes the progress and development of the entire industry. In addition, group-buying activities are carried out on a community-by-community basis, which helps enhance connections and interactions among community residents. By participating in group-buying together, residents can forge closer bonds, strengthen community cohesion, and also help reduce resource waste caused by excessive packaging and other similar issues. 1. Data Collection: Export relevant group-buying data from the Bang Group Buying backend management system. 2. Data Processing: Use customer phone numbers as unique identifiers, and perform operations such as data cleaning, removing invalid data and outlier data. 3. Algorithm Processing: Calculate the average value of all customers' consumption amounts using the AVERAGE function. Then, combine the RFM model with the grading method to calculate the grading values of customers: the consumption amount grading value Mn=IF(Mn>AVERAGE(M),1,0), the consumption frequency grading value Fn=IF(Fn>AVERAGE(F),1,0), and the time grading value Rn=IF(Rn>AVERAGE(R),0,1) based on the time from the customer's last consumption to August 31. Finally, divide customers into 8 tiers according to the RFM model customer segmentation rules: - High-value customers: R+F+M=3 - Key development customers: R+M=2 and F=0 - Key retention customers: F+M=2 and R=0 - Key win-back customers: R+F=0 and M=1 - General value customers: R+F=2 and M=0 - General development customers: F+M=0 and R=1 - General retention customers: R+M=0 and F=1 - Potential customers: R+F+M=0
