Deep Learning-Driven Digital Marketing Customer Churn Warning and Recovery Model Construction

This paper introduces deep learning technology to optimize marketing effects through accurate customer churn prediction and efficient customer churn recovery strategies. First, a deep learning-based customer churn warning and recovery model is constructed by combining user behavior data, transaction patterns and social information, collecting customer historical behavior data, and cleaning and preprocessing these data. Then, based on business needs and data characteristics, key features that can reflect customer churn risks are extracted, and these features will be used as inputs to the model. Next, LSTM is used to capture the spatiotemporal information in customer behavior and predict future customer churn risks. Finally, the model is trained and verified by dividing historical data into training sets and test sets. Under this method, the overall average customer revisit rate reaches 90%, the recall rate is between 91.2%-98.5%, and the accuracy rate is concentrated between 88.9%-94.5%. This method not only improves the accuracy of prediction, but also effectively reduces customer churn through personalized marketing strategies.