Why do most GEO ROI claims collapse under questioning?
Because they hide the assumption chain. A typical claim — “GEO will deliver $X in pipeline” — silently multiplies five guesses: how many buyers ask assistants about your category, how often you appear, how much an appearance influences a shortlist, how many shortlists convert, and what a conversion is worth. Each guess is defensible alone; multiplied together without labels, they are fiction. The calculator below keeps the same math but names every input, attaches evidence where evidence exists, and reports a range.
What are the inputs, and what evidence supports each?
| Input | Definition | Evidence source |
|---|---|---|
| Q — category question volume | Monthly buyer questions in your category asked to AI assistants | Directional anchors: AI Overviews 2.5B+ MAU (Alphabet Q2 2026), ChatGPT ~900M weekly (OpenAI 2026); your category share is an explicit assumption |
| V — your Visibility score | Share of your question set where you appear (0-100) | Measured: your monthly question-set runs, per the Visibility standard |
| I — influence rate | Share of appearances that influence a shortlist | Assumed — declared openly, tested at 3 levels |
| C — shortlist-to-conversion rate | Your existing funnel math | Measured from your CRM, not invented for this model |
| D — deal value | Average revenue or pipeline per conversion | Measured from your books |
The model: influenced opportunities ≈ Q × V × I; revenue ≈ that × C × D. Only I is a free variable — everything else is measured or anchored. That is the entire trick: one assumption, clearly labeled, instead of five hidden ones.
How do you set the influence rate honestly?
Run three scenarios — pessimistic, base, optimistic — and defend the base with structural evidence rather than hope. The structural argument: with roughly 68% of search sessions ending zero-click and position-1 CTR down 58% where AI Overviews appear (Ahrefs, February 2026), the answer layer demonstrably absorbs buyer attention that previously became clicks; some of that absorbed attention influences purchases, or the entire advertising industry’s awareness spending is imaginary. Choose a conservative base, note that branded-search trend and assistant-referral conversion rates from your own analytics provide calibration signals, and revisit I quarterly as your data matures.
What does the cost side look like?
Sum the real costs for the period: content engineering hours, source-graph and PR work, technical fixes, measurement time, tooling, and agency fees if any. Most teams find the honest cost is lower than feared because the labor is largely re-pointed existing staff — but include it at full rate anyway; a model that survives full-cost accounting survives the CFO. Compare against the revenue range, not the point estimate: if even the pessimistic scenario clears cost, the case is strong; if only the optimistic one does, say that and fund a POC instead of a program. See the GEO vs paid ads framework for the budget conversation this feeds.
How do you present it in the meeting?
- One slide: the formula, the five inputs, and which are measured versus assumed.
- The measured inputs with receipts: question-set scores with screenshot archive, funnel rates from CRM.
- The three scenarios side by side, with the pessimistic one presented first.
- The ask: fund the measurement and the next quarter of interventions, with the model re-run on new data at each quarterly review.
What should you never do?
- Present a point estimate. A range with declared assumptions is credible; a single number is a target for the first skeptic in the room.
- Borrow influence rates from vendor case studies with no methodology.
- Attribute branded-search growth to GEO alone; present it as a calibration signal, not proof.
- Hide the measurement cost. The question-set ritual is what makes every future number believable; it is a feature, not overhead.
The deepest defense is procedural: this model gets more honest every quarter, because every measured input replaces an assumption. That trajectory — from five guesses to one — is what you are actually selling in the budget meeting. For the measurement machinery, see the measurement standard and reading AI traffic signals.