Counterfactual
A counterfactual is a modeled alternate reality used to estimate causality. In marketing measurement, it represents what total revenue would have been if a specific channel’s spend were zero. SpendSignal estimates counterfactuals to compute incremental impact by taking the difference between observed reality and this hypothetical baseline.
The Short Version
The 'What-If' scenario. It tells you what sales you would have made if you did nothing.
Visual Explanation
GA4 Can't Measure Incrementality
Why the world's most popular analytics tool is blind to the world's most important metric.
Data Without Context
You know you spent $50K and made $200K. But you don't know how much of that $200K would have happened anyway.
Without a counterfactual baseline, you cannot measure true lift. You are flying blind, assuming every sale is a result of your ad.
How it works
Train a model on historical data (spend, revenue, seasonality)
Simulate a scenario where Channel X spend = 0
The gap between the simulation and actual revenue is the 'Incremental Lift'
Common Misconceptions
Using 'Pre-Post' analysis (Correlation is not causation)
Assuming counterfactual is a flat line (It must account for seasonality)
Confusing it with 'Control Groups' (Counterfactuals are synthetic controls)
Frequently Asked Questions
QIs this a guess?
It's a statistical estimate based on historical patterns. It is far more accurate than assuming 100% or 0% incrementality.
QDo I need to stop spending to see this?
No. That's the beauty of synthetic counterfactuals. We model the zero-spend scenario mathematically so you don't have to experience the revenue loss of actually pausing ads.