Revenue impact per fix
Every finding gets a projected revenue range based on query volume, current visibility, and recoverable position. Not a guess. A model with the inputs visible.
What it actually is
For each finding from the schema, AI visibility, and crawlability work, I model the revenue at stake. Inputs: query volume from your search console plus third-party data, current visibility (rank or citation share), recoverable visibility based on competitor benchmarks, your conversion rate, your AOV or LTV. Outputs: a low/mid/high revenue range per fix, with the math shown.
Agencies will hand you a deck with a number and no model behind it. I hand you a spreadsheet where you can change your conversion assumption and see the projection update. The model is yours. Argue with it. That's the point.
Deliverables
- Per-fix revenue projection model (spreadsheet, formulas exposed)
- Documented assumptions for query volume, recoverable position, and conversion
- Sensitivity analysis showing how the projection moves under pessimistic/realistic/optimistic inputs
- Ranked list of fixes by projected ROI per engineering day
- Notes on which fixes I have high confidence on and which depend on factors outside engineering
What breaks without it
Without revenue tied to fixes, schema work loses prioritization fights inside the company. Your engineering team has a roadmap of feature work backed by PM revenue projections. A pile of schema fixes without dollar impact lives at the bottom of that backlog forever. The point of the model is to give the work a number that competes.
The most common pattern: the highest-revenue fix is not the most technically interesting. It's the boring one. A Product schema field missing across 30,000 PDPs, fixable in a one-line template change, often outranks a glamorous entity-graph rebuild on revenue per engineering day. The model surfaces that.
How it fits the Audit
The model is the bridge between findings and roadmap. Without it the roadmap is just a list. With it, the roadmap is a sequence of decisions tied to recoverable revenue, which is what makes the Sprint scope defensible to a CFO.
The full Audit breakdown
- 01componentSchema graph auditOpen
- 02componentAI visibility scanOpen
- 03componentCrawlability and Core Web VitalsOpen
- 04currentRevenue impact per fixyou are here
- 05component90-day remediation roadmapOpen
Stop pouring budget into a broken foundation.
If your SEO retainer hasn’t compounded, your AI citations have stalled, or your last technical audit ended in a deck nobody read, that’s not a content problem. It’s an engineering problem. The same engineer who diagnoses ships the fix.