US 2026/0310944 A1Published application

Complete human-in-the-loop agentic ai system for churn prediction and in-time retention planning and execution

AssigneeChurnShift Inc.

InventorRouzbeh Farahmand

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Abstract

A computer-implemented method for customer retention uses an agentic artificial intelligence pipeline comprising an orchestration layer that coordinates a plurality of specialized AI agents via a schema-validated inter-agent communication protocol. Customer data may be collected and processed through the AI agents. A custom churn modeling agent may automatically select and train a machine learning algorithm and generate local and global explanations for each churn prediction. Survival analysis techniques, including Kaplan-Meier estimators, may estimate when each at-risk customer is likely to churn. A Bayesian Network may be constructed to identify probable churn causes and map them to corresponding retention treatments. A Large Language Model agent conditioned on the identified causes and treatments may generate personalized retention communications. Retention recommendations may be presented to a human operator for approval prior to implementation. Observed retention outcomes may be used to update Bayesian Network conditional probability distributions to improve future recommendations.

Claims20

Dependent claims

A computer-implemented method for improving customer retention through an agentic artificial intelligence pipeline that reduces manual data science overhead, increases churn prediction accuracy through automated algorithm selection and cross-validated model evaluation, and generates personalized retention communications without human authoring, comprising:

collecting and processing customer data through a plurality of specialized artificial intelligence (AI) agents operating under an orchestration layer that coordinates inter-agent communication via a structured data protocol;
predicting which customers are likely to churn by executing a custom churn modeling agent that automatically selects a machine learning algorithm based on data characteristics, trains the selected algorithm with automatic hyperparameter tuning, and generates local and global explanations for each churn prediction;
estimating a churn timeline for each identified at-risk customer by applying survival analysis techniques, including Kaplan-Meier estimators, to predict when each customer is likely to churn;
determining causes of potential churn for each at-risk customer through a differential diagnosis process comprising constructing a Bayesian Network representing probabilistic relationships between churn causes and retention treatments, identifying probable causes of churn for the individual customer, and mapping the identified causes to corresponding retention treatments;
generating personalized retention communications and incentives through a Large Language Model agent conditioned on the identified churn causes and mapped treatments;
presenting the generated retention recommendations to a human operator for verification prior to implementation;
implementing only the retention actions approved by the human operator;
and
monitoring customer responses to implemented retention actions and updating the Bayesian Network based on observed outcomes to improve future retention recommendations.

6 more elements

A system for customer retention through agentic artificial intelligence, comprising:

an orchestration layer configured to activate, coordinate, and monitor a plurality of specialized AI agents via a structured inter-agent communication protocol;
a data processing subsystem comprising AI agents configured to collect customer data from multiple sources, transform the collected data for analysis, and validate data quality through automated quality tests;
a churn prediction subsystem comprising AI agents configured to automatically select and train machine learning models for churn prediction and generate local and global explanations for each prediction;
a churn timeline subsystem comprising AI agents configured to apply survival analysis techniques, including Kaplan-Meier estimators, to estimate when each at-risk customer is likely to churn;
a retention planning subsystem comprising AI agents configured to construct Bayesian Networks representing probabilistic relationships between churn causes and retention treatments, identify probable churn causes for individual customers, and map identified causes to corresponding treatments;
a customer interaction subsystem comprising a Large Language Model agent configured to generate personalized retention communications and incentives conditioned on identified churn causes and mapped treatments;
a human verification interface configured to present retention recommendations to a human operator for approval prior to implementation;
and
a persistence subsystem comprising AI agents configured to store churn prediction data and models, issue alerts for imminent churn risks, and update the Bayesian Networks based on observed retention outcomes.

6 more elements

Dependencies are inferred from the claim text.