Forecasting
Predictions grounded in your own history and the trends around it, at whatever granularity you plan on.
Technology Division
Machine Learning Sales Prediction
Forecasts built from data you already have, so decisions about stock, staffing and spend rest on evidence rather than last year's guess.
What it does
The models read your historical sales alongside market trends, customer behaviour and the other factors that actually move your numbers. What comes back is a forecast specific to your business, not an industry average.
Most clients use it for four things: keeping inventory in step with demand, planning cash with fewer surprises, anticipating what customers will want before they ask, and pointing marketing spend at the periods where it will do something.
Where it helps most
Inventory
Hold less stock without running out. The savings usually cover the work within a season.
Demand patterns
Anticipate what customers need and when, and adjust before the pattern has fully arrived.
Financial planning
Revenue projections with error bars, which is considerably more useful than a single confident number.
Marketing timing
Spend into demand rather than against it, with the forecast setting the calendar.
Ongoing accuracy
Models are retrained as new data arrives, so the forecast keeps up with the business.
What we need from you
Enough sales history to see a pattern, ideally two years or more, in whatever form you keep it. A spreadsheet export is a perfectly normal starting point.
The first step is a review of that data to establish whether a useful model can be built from it. If it cannot, we say so before anyone commits to a project.
Send us a year of sales data.
We will tell you what it can and cannot predict before you commit to anything.
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