Predictive Analytics

Predictive Analytics turns historical data into forward-looking signals: which customers will churn, what demand will look like next quarter, which transactions carry risk. We build models that run in production - scored on live data, monitored for drift, and measured against what actually happens.

The model is the easy half. The hard half is everything around it: clean training data, a serving pipeline, and a decision process that uses the output. We build all of it, because a forecast nobody acts on is a science project.

Planning runs on gut feel and last year's numbers. Churn is discovered in the cancellation email, stockouts in the empty shelf, and risk after the loss. The data to see these things coming exists - it just has never been turned into a working forecast.

Failed attempts share a pattern: a model built in a notebook, impressive in a demo, and dead within a quarter because nobody owned its data, its drift, or its decisions.

We start from the decision, not the algorithm: what will be done differently when the prediction exists, and what accuracy would change it. That determines the model - often simpler than expected, always validated honestly on held-out time periods.

Production is part of the scope from day one: scoring pipelines, drift monitoring, and retraining schedules. A RedSage forecast comes with an owner manual and a maintenance plan, not just weights.