By collecting and analyzing user historical data category email list performance, including user behavior data such as login frequency, login duration, browsing duration, browsing depth, bounce rate, order frequency, etc., an observation window can be determined. Lost users. Secondly, a performance window is established, and a churn rule set is established by establishing a user big data model to analyze the characteristics of known churn user portraits, consumption behavior characteristics, and user life cycle characteristics, and continuously optimize the category email list model to improve the prediction coverage and hit rate.
Establish a prediction window again. In the next few category email list weeks or months, use the model to predict users who have not yet clearly churned, and establish a churn scoring system, and use the scoring rules to label the corresponding churn, such as: high-risk churn users, medium-risk churn Users, low-risk churn users. What is the essence of lost user operations? Analyze how to build a user loss early warning system from three aspects The above picture is a schematic diagram of the next step to build a loss early warning model, and to do loss prediction analysis, we category email list analyze them one by one from the perspective of big data: In the observation period window, we need to mine a batch.
At this stage, the churn evaluation dimension needs category email list a full range of user field data, so that in the next modeling process, the model can pass The multivariate algorithm evaluates the relationship between each dimension and churn and sorts it out. In the performance period window, the final churn prediction model needs to be built. The model is trained by the sample data users during the observation period to determine who are the known churn users? At this time, the category email list accuracy of the model is evaluated from several aspects: Hit rate: When predicting user churn.