How We Built a Churn Prediction ML Engine in Sigma: From POC to Production

Retaining customers is crucial for the growth of SaaS businesses, as it limits churn risk, which is often evaluated through time-consuming and manual research by Customer Success (CS) teams. To address this, Sigma developed a scalable framework for predicting churn risk by using their platform for data prototyping and exploration, enabling CS teams to proactively address potential churn. The project involved creating a churn prediction system that detects unhealthy accounts early, prioritizes CS actions, and surfaces churn indicators, all within a rapid three-month timeline. Sigma facilitated fast, iterative data prototyping and exploratory analysis, allowing seamless collaboration between Data and CS teams, and enabling the development of a robust churn prediction framework using both supervised and unsupervised machine learning approaches. The process allowed for real-time feedback and iteration, ensuring that the model was continuously refined with input from domain experts. The outcome was a proactive, data-driven approach for CS teams to intervene earlier and protect revenue, with the framework designed to scale and incorporate more product signals and automation, ultimately enhancing the ability to predict and mitigate churn risk effectively.