The hard part of GEO isn’t making numbers look good — it’s proving the improvement came from your intervention, not from market drift or seasonality. That requires a causal experiment.
Why correlation isn’t enough
If recommendation rate rises after you ship changes, it could be GEO working — or category interest rising, a competitor slipping, or a model update. Looking only at “before vs after” credits all of that to you.
How a holdout works
Randomly split the optimizable units (pages / SKUs / prompt sets) into a treatment group and a holdout (control) group: the treatment group gets the GEO changes, the control group stays untouched. After a period, compare the difference in changes between the two (difference-in-differences). That gap is the true incremental lift.
Make the conclusion hold up
- Pre-register the experiment design to avoid cherry-picking afterward;
- Use bootstrapping to put a 95% confidence interval on the lift;
- Check the two groups were comparable before treatment (balance).
A causal lift with a confidence interval is what justifies renewal — and what most monitoring tools can’t give you.