小程序流失用户召回分析数据

相比于获取新用户，召回老用户的成本通常较低，且老用户对产品或服务的接受度更高，有助于降低整体的获客成本。通过分析不同渠道和方式的流失用户召回成功率，企业可以更有效地分配营销资源，提高流失用户的召回率，延长用户的生命周期，提高用户的生命周期价值，为企业带来更持久的收益。
流失用户召回分析在帮助企业基于数据做出更精准的运营和营销决策，提高决策效率和效果的同时，也为行业的流失用户召回提供营销方案和效果参考。1、数据收集清洗：从企业内部数据库里筛选出商业空间租赁小程序流失用户2024年10月的召回相关数据，包含用户ID、召回方式等，对原始数据进行处理，去除异常数据。
2、数据处理：计算用户不活跃时间间隔=召回时间-发起召回消息前最新活跃时间，利用COUNTIF函数计算不同召回方式成功数A、不同召回方式召回总数B，不同不活跃时间间隔不同召回方式成功召回数C、不同不活跃时间间隔不同召回方式召回总数D，进一步计算得到不同召回方式成功率K1=A/B、不同不活跃时间间隔召回成功率K2=C/D。
3、数据分析：通过分析不同不活跃间隔、不同召回方式的流失用户召回成功率，帮助企业更好的召回流失用户。

Compared to acquiring new users, recalling existing churned users typically incurs lower costs, and these users have higher acceptance of the product or service, which helps reduce overall customer acquisition costs. By analyzing the recall success rates of churned users across different channels and methods, enterprises can allocate marketing resources more efficiently, improve the recall rate of churned users, extend user lifecycles, enhance user lifetime value (LTV), and generate more sustained revenue for the business.
Churned user recall analysis not only helps enterprises make more precise operational and marketing decisions based on data to improve decision-making efficiency and effectiveness, but also provides marketing solutions and effectiveness references for industry-wide churned user recall practices.
1. Data Collection and Cleaning: Screen recall-related data of churned users of the commercial space rental mini-program from the enterprise's internal database for October 2024, which includes user ID, recall method and other relevant fields; process the raw data and remove abnormal entries.
2. Data Processing: Calculate the user inactivity time interval = recall time - the latest active time before initiating the recall message; use the COUNTIF function to calculate the number of successful recalls for different recall methods (A), the total number of recall attempts for different recall methods (B), the number of successful recalls for different inactivity time intervals and recall methods (C), and the total number of recall attempts for different inactivity time intervals and recall methods (D); further calculate the success rates of different recall methods K1 = A/B, and the recall success rates for different inactivity time intervals K2 = C/D.
3. Data Analysis: Analyze the recall success rates of churned users across different inactivity intervals and recall methods, to assist enterprises in better recalling their churned users.