Models we build
Demand and sales forecasts that account for seasonality and promotions; customer churn-risk analysis; customer segmentation; price and discount analysis; and inventory recommendations.
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Forecasting and machine learning
Reports explain what has happened. Forecasts help estimate what may happen next. We build models for specific decisions and test them against historical data before they are used operationally.
How we test models
We compare forecasts with outcomes from earlier periods, publish accuracy measures and monitor performance as conditions change. A model is useful only when its limits are understood.
Decision support
Recommendations support the people responsible for the decision. They do not replace commercial judgement or guarantee a particular result.
Data requirements
The amount of history needed depends on the question and how frequently the data is recorded. We assess completeness, seasonality and business changes before estimating feasibility.
FAQ
Common questions
Is forecasting only for large companies?
No. Suitability depends on transaction volume, history, demand patterns and the value of the decision. A data review can establish whether a model is worthwhile.
Start with the business question.
Tell us what you need to measure and where the data lives. We will review the request and outline a practical next step.