A system and method for minimizing customer churn in device service businesses commences with execution of a customer service contract. Ongoing customer data capture is made for each contract. Customer data includes contract events, environmental events, service events, device usage analytics and personnel events. Machine learning is applied to captured customer data, which machine learning is based on a state of customer data at the time of contract determination. Customer data is assigned weights, and aggregate data for each customer is compared to a preselected threshold level. Customers above a threshold are deemed happy and customers below the threshold are deemed to be at risk. Remedial measures relative to at risk customer data generates levels of automated remediation followed by remedial measure suggestions to an administrator when not sufficiently successful.

A method can include receiving input data comprising a plurality of features for a plurality of users. A method can including providing the input data to a risk prediction model configured to predict a termination likelihood for each user. In some implementations, the risk prediction model can be a random forest model. A method can include identifying, based on the predicted termination likelihood for each user, an at risk population including users with a termination risk above a threshold amount. A method can include determining, for each user of the at risk population, a profile type of a plurality of profile types. The profile type can describe certain attributes of the user. In some implementations, an end user can select a profile type. A method can include outputting members of the at risk population having the selected profile type.

A method for automated wish processing and execution. Data of a user including a wish of the user is received and analyzed with one or more algorithms to identify at least one actionable solution corresponding to the wish. Structured digital content, including execution plans, analyses, or templates, is generated based on the analyzed data and output in a format for presentation on a user device. In the system, a memory stores instructions and a processor executes the instructions to perform the operations of receiving, analyzing, generating, and outputting.

The present disclosure provides a system for predicting customer churn and generating retention strategies. The system includes a data ingestion module that collects and normalizes customer interaction data and network telemetry data comprising latency, bandwidth, and packet loss metrics. A generative artificial intelligence (GenAI) labeling agent applies chain-of-thought reasoning to categorize this data based on contextual features and temporal patterns. A machine learning module executes time-series regression models to predict customer churn probabilities using the labeled datasets. Finally, a prescriptive GenAI agent generates actionable customer retention recommendations based on these predictions, which are delivered through an automated engagement system. The system integrates real-time data processing with artificial intelligence (AI)-driven analysis to identify at-risk customers and develop targeted retention strategies.