Know what's likely to happen before it happens

At minimum, historical records of the outcome you want to predict, past churn events, past demand figures, past claims, going back far enough to capture at least one full seasonal cycle where relevant. Additional context data, customer attributes, transaction history, external factors, improves accuracy but isn't required to start. We assess what you have before proposing a model approach.

Accuracy depends on how predictable the outcome genuinely is and how much historical data exists, so we validate against your own historical data before deployment and report the accuracy honestly rather than promising a number upfront. A model that is right most of the time with a known error range is more useful than one presented as more certain than it is.

Dashboards and BI tools report what already happened, current and historical numbers you can slice and filter. Predictive analytics uses that same data to estimate what happens next. The two are complementary, we often build predictive models that surface their output inside the BI tools your team already uses daily, rather than creating another separate destination.

It depends on how fast the underlying behaviour changes, a churn model for a stable subscription business might hold up for a year, a demand model in a fast-moving market might need quarterly retraining. We monitor accuracy in production and retrain when performance data indicates it is needed, rather than on an arbitrary fixed schedule.

With limited data, we typically start with a simpler model with wider confidence ranges, honest about the additional uncertainty, and improve accuracy as more data accumulates in production. It is rarely a reason to wait, an imperfect early model that improves over time still buys more lead time than no forecasting at all.

A first automation typically goes live in two to six weeks; a programme covering several workflows runs three to six months. We sequence by hours saved so the first release pays for the next.