Customer churn prediction based on customer value and user evaluation emotions in online marketing

In order to improve the accuracy and usefulness of the churn prediction model, the core elements of the research content were designed to include collecting data on customer purchase behaviour and reviews, quantifying and analysing customer value, analysing customer sentiment in reviews, and combining customer value factors and review sentiment factors in the model. The results of the study show that the model performs best on different indicators, and the area of the main characteristic curve is the largest, which is significantly higher than that of the traditional model. Its hit rate, coverage rate and improvement coefficient also perform well. At the same time, when the sample size increases, the improvement coefficient increases the most, reaching 0.41. In conclusion, the model performs well in customer churn prediction, and it can provide certain reference value for the research field of customer churn prediction.