Predictive Client Churn Model for a Subscription-Based Advisory Service

Overview

Built a machine learning model for a subscription-based advisory service that predicts which retainer clients are at elevated risk of canceling, based on engagement signals like meeting attendance, response times, and usage of the service's resources. The model surfaces an early warning to account managers weeks before a client would typically give notice, giving the team time to proactively address concerns rather than reacting to a cancellation email. We trained the model on the service's own historical churn data and validated it against a holdout period before it went live in the account team's workflow. The score is presented as a simple risk indicator inside the tool account managers already use daily, not a separate dashboard they'd have to remember to check. A subscription-based advisory service had no early warning system for retainer clients at risk of canceling, typically only finding out when a cancellation notice arrived. We built a machine learning model predicting churn risk from engagement signals like meeting attendance and response times, surfacing an early warning to account managers weeks before a client would typically give notice, inside the tool they already use daily. We trained the model on the service's own historical churn data and validated it against a holdout period to confirm real predictive value before it touched the account team's live workflow. Account managers gave feedback on early false positives, which shaped the final risk threshold. Account managers now get weeks of advance warning on at-risk clients instead of finding out at cancellation, contributing to a measurable improvement in retainer retention through proactive outreach.