Demand Forecasting
Price Prediction
Conversion Prediction
Data Pipeline & Model Training
Agentic Coding Coaching
Large Language Models
See which customers are about to leave. Before they do
By the time most companies notice a customer is churning, it's too late. Cancellation is the last step in a pattern that started weeks or months earlier. Without a model, those signals are invisible.
We build churn prediction models from your usage patterns, billing history, and support interactions. You get a model that flags at-risk accounts early enough to intervene, so your retention team can focus where it matters most.
It costs 5x more to acquire a new customer than to retain one. Without early warning signals, most retention efforts start after the cancellation request, too late to change the outcome. Harvard Business Review.
A model that scores every customer by their likelihood to leave, every week
Three places this changes your business
Most customers who cancel show signs weeks before they do. A churn model surfaces those signals early, when a conversation or an offer can still change the outcome. By the time the cancellation request arrives, it is too late.
A SaaS company reduces monthly churn by 22% after giving their CS team a weekly list of at-risk accounts to contact proactively.
Customer success teams have limited capacity. Without a churn model, they spread attention evenly or react to whoever complains loudest. A risk score tells them where intervention actually matters.
A B2B platform cuts average response time for at-risk accounts from 11 days to 2, by giving CS reps a prioritised daily queue based on churn scores.
A churn model tells you which customers are at risk. The features behind the predictions tell you why. That is the input your product and support teams need to fix the underlying problems, not just the symptoms.
A telecoms provider discovers that customers who contact support more than twice in their first month are 4x more likely to churn, prompting an onboarding redesign.