Forecasting Sales From Marketing Spend, 20–35% More Reliably
Budget was being allocated on last quarter's results and instinct. Modelling the channels together produced forecasts that were both more reliable and interpretable enough to act on.
- Client
- Multi-channel marketing team
- Industry
- Marketing analytics
20–35%
More reliable than baseline
4 channels
Paid, social, email, search
Interpretable
Per-channel contribution
Dashboard-ready
Weekly and monthly outputs
The problem
Marketing spend was split across paid advertising, social, email and search, and allocated largely on what had worked last quarter. Nobody could say what an extra unit of budget in a given channel was likely to return, which made every planning meeting an argument between people with different anecdotes.
The team did not need a perfect forecast. They needed one reliable enough to rank options and defensible enough to survive the meeting.
What we built
A regression model over multi-channel spend with feature engineering for the things that make marketing data awkward: seasonality, promotional periods, and the fact that spend in one channel is rarely independent of spend in another.
Evaluation on held-out periods rather than in-sample fit, because a marketing model that has seen the period it is predicting will always look excellent and will always disappoint in production.
Interpretable outputs by design — per-channel contribution rather than a single opaque prediction — since a forecast that cannot be reasoned about does not change how budget is allocated no matter how accurate it is.
Results
Forecast reliability improved 20–35% over the baseline methods the team had been using, measured on held-out periods.
Outputs feed weekly and monthly planning through the existing dashboards, so the model shows up where budget decisions are actually made. The planning conversation changed from competing anecdotes to a shared estimate people could argue with on the numbers.
What we'd flag
Correlation between spend and sales is not proof of causation, and this model does not claim to be a causal one. It forecasts under conditions resembling the training period, which is genuinely useful and is not the same as telling you what a channel would return if you tripled it.
Structural changes — a new channel, a major creative shift, a market event — invalidate the fit. It needs refitting on a schedule, and the schedule should be shorter than most teams expect.
