Expected click-through rate, from your own data
CTR Curve Modeling
Fits position-specific click-through curves from your own Search Console history and flags the queries that underperform the rate their position should earn.
How it works
Every query and position pair in the period is bucketed by average position, and the click-through rate of each bucket becomes the expected curve for your site. A query is underperforming when its own rate sits far enough below the curve at its position that random variation is an unlikely explanation. Published industry curves are not used: a recipe site and a B2B documentation site have genuinely different curves, and borrowing someone else's benchmark produces confident nonsense.
What you get
- The fitted curve for your property, position by position
- Every query with its actual rate, its expected rate and the gap between them
- Estimated clicks available if an underperformer reached its expected rate
- A shortlist of titles and meta descriptions worth rewriting first
What it needs
Enough queries with impressions across a spread of positions; a period of 28 days or longer.
What it will not tell you
A gap is a signal to look, not proof that the snippet is at fault. Search features, brand recognition and intent mismatch all move click-through rate at a fixed position.
Runs on demand
Experimental analyses are not part of the nightly job. You open the report, set the period and run it, so the cost lands only when you want the answer.
The rest of Experimental Analytics
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