Forecasts your planning team can act on

Predictive analytics uses historical patterns plus known future events, such as promotions, holidays or contract renewals, to estimate what is likely to happen next. The output is not a single number but a range, and the width of that range is as useful to a planner as the midpoint, because it tells them how much buffer to hold. A forecast without that range invites false confidence.

Two choices shape most projects. The first is granularity: forecasting by product and store each week is far harder than total revenue by month, and the right level depends on the decisions being made. The second is method: statistical time-series models are transparent and robust, while machine learning models can capture more drivers but need more data and care. We often test both and compare them.

A trustworthy forecast is backtested honestly, refreshed automatically and shown where planners already work. It also shows its assumptions plainly. When a forecast moves sharply, planners should be able to see which driver changed, whether a promotion, a holiday shift or a run of unusual weeks, rather than being asked to take the number on faith.

Most planning still runs on last year’s numbers plus a guess. Forecasting models can account for seasonality, promotions, pricing, weather and trends at once, and they express how confident they are. That range is often more useful than a single number, because it tells you how much buffer stock, cash or staff to hold.

We focus on forecasts that feed a real decision with a clear owner, and we keep measuring them against what actually happened so trust in each number is earned over time rather than assumed.