GEO ROI: Formula, Metrics & Worked Example

By maxaeo

GEO ROI measures whether better visibility in AI-generated answers creates enough incremental gross profit to justify the work required. It does not turn prompt runs, mentions, or citations into imaginary impressions. It connects recommendation performance to identifiable demand, commercial outcomes, complete costs, and an explicit counterfactual.

This guide uses the maxaeo GEO Confidence Ledger, a four-layer framework that prevents leading indicators from being mistaken for revenue. It also provides formulas, attribution rules, worksheet fields, break-even calculations, and a worked example.

What Is GEO ROI?

GEO ROI is the incremental gross profit produced by work that improves a brand’s inclusion, accuracy, citations, and recommendations in AI-generated answers, divided by the full cost of that work. It is not the value of mentions or monitored prompts; it requires evidence connecting improved AI visibility to demand and commercial outcomes.

Use this formula for closed business:

Realized GEO ROI = (Incremental attributable gross profit − total GEO program cost) ÷ total GEO program cost

For planning, add the expected value of open pipeline:

Expected GEO ROI = (Realized attributable gross profit + expected open-pipeline contribution − total GEO program cost) ÷ total GEO program cost

Report the result as a percentage. An ROI of 40% means the program produced $1.40 in attributable gross profit and expected contribution for every $1.00 spent, leaving $0.40 after cost.

Use gross profit, not revenue, when margin data is available. A $100,000 contract does not create $100,000 of economic value if delivery, infrastructure, or service costs consume part of it.

Which GEO return should you report?

View	Included value	Best use
Realized ROI	Closed business supported by approved attribution evidence	Finance and retrospective reporting
Expected ROI	Realized gross profit plus probability-weighted open pipeline	Planning and budget decisions
Brand-impact scorecard	Recommendation share, accuracy, sentiment, citations, and recall	Leading indicators without invented dollar values

Keep these views separate. Do not combine closed revenue, full open pipeline, and unmonetized brand impact into one oversized return claim.

What Evidence Does a Defensible GEO ROI Claim Need?

A defensible claim must pass five tests: a measured change occurred, evidence connects the change to real buyers, a counterfactual estimates what would have happened anyway, channel ownership is reconciled, and commercial value is calculated after margin and cost. Missing evidence should lower the claim—not disappear inside a blended score.

The GEO ROI Claim Test makes that standard operational:

Test	Question	If the test fails
Change	Did recommendation, citation, demand, or pipeline performance improve from a fixed baseline?	Report current performance, not lift
Connection	Is there person-, account-, or session-level evidence of AI-assisted discovery?	Classify the result as directional
Counterfactual	What would likely have happened without the GEO work?	Call the result attributed or influenced, not incremental
Ownership	Has the opportunity been reconciled with SEO, PR, paid, partner, and sales programs?	Do not assign full credit
Economics	Were margin, complete costs, time horizon, and pipeline probability applied consistently?	Do not label the result ROI

This distinction matters because attribution and incrementality are different. Attribution identifies a connection between an AI interaction and an outcome. Incrementality estimates whether the outcome would have occurred without the intervention.

What Is the GEO Confidence Ledger?

The GEO Confidence Ledger separates recommendation exposure, supporting evidence, attributable demand, and commercial value until reliable records connect them. Each layer has its own denominator, source system, and claim limit.

Layer	Core question	Primary records	What the layer can support
1. Recommendation exposure	How often is the brand accurately included or endorsed?	Repeated prompt observations	Visibility and recommendation trends
2. Evidence and citations	Which pages, domains, and claims support the answer?	Answer text and citation URLs	Why visibility may have changed
3. Attributable demand	Did identifiable people or accounts engage after AI-assisted discovery?	Analytics, forms, interviews, CRM notes	Sourced or influenced demand
4. Commercial value	What pipeline and gross profit can be connected to that demand?	Opportunities, contracts, margins, costs	Realized or expected return

The first layer measures coverage across monitored recommendation opportunities, not human reach. If a platform runs 5,000 test prompts and records 1,500 recommendations, the observed recommendation rate is 30%. It does not mean 1,500 prospective customers saw the answers.

Which Metrics Belong in Each Layer?

1. Recommendation exposure

Track:

• Mention rate
• Recommendation rate
• First-position recommendation rate
• Rank within generated shortlists
• Competitor inclusion
• Descriptor and category accuracy
• Positive, neutral, negative, or risky framing
• Performance by platform, market, persona, topic, and buying stage

A mention records whether the model names the brand. A recommendation records whether the answer presents the brand as a suitable option. Use a dedicated AI recommendation rate rather than treating every occurrence as an endorsement.

For a binary recommended/not-recommended outcome:

Weighted recommendation rate = Σ(prompt weight × recommendation outcome) ÷ Σ(prompt weights)

Prompt weights should reflect commercial relevance. A high-intent comparison prompt should normally carry more decision value than a broad educational question. A documented prompt-weighting model based on buying intent makes this choice auditable.

Store the prompt, answer, timestamp, platform, market, language, persona, location, and model details when available. Without that context, natural answer volatility can be mistaken for optimization impact.

2. Evidence and citations

Track:

• Answers containing citations
• Owned, earned, and third-party citation share
• Cited pages and source domains
• Citation freshness
• Claim-to-source alignment
• Competitor citation overlap
• Unsupported or outdated claims
• Citations associated with actual recommendations

A citation is a diagnostic signal, not revenue. It may improve trust or supply factual grounding without generating a click. Assign commercial value only when another record connects the citation or answer to demand.

Define denominators explicitly. For example:

Owned citation coverage = Eligible monitored answers citing an owned page ÷ all eligible monitored answers

Changing the denominator from “all answers” to “answers with any citation” will change the rate even when performance does not. Record the formula with every baseline.

3. Attributable demand

Capture:

• Identifiable AI referral sessions
• Landing-page engagement
• Trial, demo, contact, or checkout events
• Structured “How did you hear about us?” responses
• Buyer-provided platform and use-case details
• Sales-call references to AI research
• Branded search and direct-traffic changes
• Target-account activity after relevant recommendation changes

Referral data alone is incomplete. Buyers can copy a URL, search for the brand later, switch devices, or use an interface that does not preserve a recognizable referrer.

Use a structured discovery field with named answer-engine options plus a short free-text prompt such as: “What did you search for, and what did the answer recommend?” Preserve the buyer’s wording rather than asking salespeople to reinterpret it.

4. Commercial value

Track:

• AI-sourced opportunities
• AI-influenced opportunities
• Opportunity amount and stage
• Historical stage-to-win probability
• Sales velocity
• Closed-won revenue
• Gross margin
• Retention and expansion
• Attribution confidence
• Incrementality treatment
• Program cost

Keep sourced and influenced outcomes mutually exclusive in the primary report. One opportunity may have several supporting touchpoints, but it should appear only once in the total being used to calculate return.

How Should GEO Attribution Confidence Be Scored?

Attribution confidence should describe the strength of the evidence connecting AI-assisted discovery to a commercial record. It is not the same as stage probability, and it does not prove incrementality.

The following weights are transparent starting assumptions, not industry benchmarks:

Evidence tier	Starting weight	Example
Deterministic	1.00	Identifiable AI referral produces a conversion tied to a CRM opportunity
Strongly corroborated	0.75	Buyer names the platform and use case; matching recommendation evidence exists
Probable	0.50	Contemporaneous sales notes record AI discovery, but no session record exists
Directional	0.25	Account engagement follows relevant recommendation growth without direct confirmation
Unattributed	0.00	Aggregate visibility improves with no account-level connection

Apply three controls:

1. Approve tiers before reviewing outcomes. Backfilling generous weights after a large deal closes creates confirmation bias.
2. Keep raw values beside weighted values. Finance should be able to see exactly what was discounted.
3. Recalibrate quarterly. Compare each tier’s progression and win rate with unattributed opportunities. If “strongly corroborated” records behave like the unattributed group, the evidence standard may be too loose.

For closed business, report deterministic sourced revenue separately from fractionally credited influenced revenue. Do not use decimal weights to make weak evidence look scientifically precise.

How Is Incrementality Different From Attribution?

Attribution asks whether GEO was present in the journey. Incrementality asks whether GEO changed the outcome. A buyer saying “I found you in ChatGPT” is strong attribution evidence, but it does not by itself prove that the buyer would not have discovered the brand through another channel.

Use the strongest counterfactual the organization can support:

Design	What it measures well	Main limitation
Fixed pre/post baseline	Change after intervention	Other market activity may explain the lift
Interrupted time series	Trend and level changes around documented interventions	Requires a sufficiently long, stable history
Matched content or market holdout	Difference between treated and untreated groups	Groups may not remain comparable
Difference-in-differences	Treatment change after subtracting holdout change	Sensitive to the parallel-trends assumption
Account-level comparison	Demand and pipeline differences across comparable account groups	Contamination can occur across channels
Prompt-cluster holdout	Incremental change in answer performance	Does not establish revenue incrementality by itself

A prompt holdout can show that optimization changed recommendations. It cannot show that those recommendations created incremental revenue unless demand or commercial outcomes are also compared.

If the pipeline amount has not already been made incremental through a baseline or comparison group, use an explicit incrementality factor:

Expected contribution = Opportunity amount × stage-to-win rate × gross margin × attribution weight × incrementality factor

If the opportunity amount already represents measured incremental lift, do not apply another incrementality discount. Document which treatment was used to prevent accidental double-discounting.

How Do You Calculate GEO ROI Step by Step?

Calculate GEO ROI by fixing the decision period, freezing the measurement method, establishing a counterfactual, capturing demand evidence, reconciling opportunities, applying margin and probabilities, and subtracting complete costs.

1. Define the decision. Specify whether the analysis supports budget renewal, channel comparison, forecasting, or retrospective performance review.

2. Choose one time horizon. Use at least one normal sales cycle for revenue evaluation. Compare annual benefits with annual costs and monthly benefits with monthly costs.

3. Freeze the monitoring methodology. Version the prompts, weights, platforms, locations, languages, personas, run frequency, and scoring definitions. The maxaeo AI search monitoring methodology provides a reproducible structure.

4. Create a baseline and counterfactual. Capture recommendation, citation, demand, opportunity, and conversion performance before material changes. Preserve a holdout where feasible.

5. Instrument demand capture. Retain raw referrers, landing pages, conversion events, self-reported discovery, campaign records, sales notes, and account identifiers.

6. Calculate complete program cost. Include allocated labor, software, content, authority-building, technical work, data operations, and management overhead.

7. Classify every commercial record. Assign one sourced/influenced status, evidence tier, measurement period, and opportunity ID.

8. Convert value to gross profit. Apply gross margin to closed revenue and historical stage probabilities to open pipeline.

9. Apply attribution and incrementality rules. Use approved weights and retain unweighted values for auditability.

10. Run conservative, base, and upside scenarios. Change only documented assumptions, such as stage probability or attribution confidence.

11. Reconcile the totals. Remove duplicates, test records, existing-customer revenue outside scope, pre-period opportunities, and closed deals still present in open pipeline.

12. Publish exclusions and limitations. A reproducible negative result is more useful than a positive number that cannot survive review.

What Costs Belong in a GEO ROI Calculation?

Include every incremental resource required to operate the program during the measurement period. Excluding internal labor, technical work, or agency fees makes GEO appear cheaper than it is.

Cost category	Include
Monitoring and data	Platforms, APIs, storage, reporting, analytics
Labor	Loaded, allocated cost of SEO, content, PR, analytics, sales operations, and management time
Content	Research, writing, editing, design, subject-matter review, updates
Authority development	Digital PR, expert contributions, original research, outreach
Technical work	Templates, structured data, rendering, analytics, data pipelines, QA
External services	Agency, consultants, contractors, research panels
One-time implementation	Instrumentation and setup, amortized under an approved policy
Overhead	Allocated management and operational costs required by the program

Use the percentage of a person’s loaded cost actually allocated to GEO. Do not charge a full salary when 20% of the role supports the program, and do not exclude the labor simply because no external invoice exists.

Total GEO cost = Monitoring + allocated labor + content + authority work + technical work + external services + approved overhead

How do you calculate break-even revenue?

At break-even, attributable gross profit equals program cost:

Break-even attributable revenue = Program cost ÷ (gross margin × attribution credit × incrementality factor)

For deterministic, fully incremental closed revenue, attribution credit and incrementality factor both equal 1.

With a $72,000 program cost and an 82% gross margin:

$72,000 ÷ 0.82 = $87,805 in attributable revenue to break even

If attribution or incrementality is uncertain, the required raw revenue will be higher.

The payback month is the first month in which cumulative attributable gross profit equals or exceeds cumulative program cost. This is more reliable than dividing annual cost by one unusually strong month.

What Should a GEO ROI Worksheet Contain?

A useful worksheet preserves the path from each monitored answer to demand and commercial outcomes while keeping assumptions versioned. Another analyst should be able to reproduce the summary without interpreting screenshots or undocumented channel rules.

Worksheet	Required fields
Prompt observations	Observation ID, date, platform, prompt version, cluster, weight, market, persona, mentioned, recommended, rank, competitors, answer text
Citation evidence	Observation ID, cited URL, domain, source type, claim supported, publication date, retrieval date
Demand events	Event ID, date, visitor or account ID, raw source/referrer, landing page, conversion, self-reported source, evidence tier
Opportunities	Account, opportunity ID, sourced/influenced status, stage, amount, probability, margin, confidence, incrementality treatment
Costs	Date, vendor or owner, category, direct or allocated cost, allocation method
Assumptions	Version, effective date, prompt weights, stage probabilities, margin, evidence weights, exclusions

A summary tab should calculate:

Output	Calculation
Weighted recommendation rate	Weighted recommendations ÷ total eligible prompt weight
Recommendation lift	Current weighted rate − baseline weighted rate
Realized attributable gross profit	Accepted closed revenue × gross margin × approved attribution credit
Expected open-pipeline contribution	Sum of opportunity-level expected contributions
Realized ROI	(Realized attributable gross profit − cost) ÷ cost
Expected ROI	(Realized gross profit + expected pipeline contribution − cost) ÷ cost
Break-even gap	Program cost − realized attributable gross profit
Cost per sourced opportunity	Program cost ÷ sourced opportunities

Add these reconciliation checks

• Each opportunity ID appears once in the primary return total.
• Closed opportunities are removed from open pipeline.
• Sourced and influenced totals do not overlap.
• Opportunity amounts reconcile with the CRM.
• Prompt and scoring versions match the baseline.
• Stage probabilities come from historical CRM outcomes, not default labels.
• Excluded and zero-confidence records remain visible in an audit tab.
• Scenario formulas change assumptions, not raw records.

How Should Open Pipeline Be Valued Without Double Counting?

Open pipeline should be valued as an expected gross-profit contribution. Closed business should be valued as realized gross profit. Once an opportunity closes, it must leave the open-pipeline calculation.

For an already incremental opportunity:

Expected pipeline contribution = Opportunity amount × historical stage-to-win probability × gross margin × attribution confidence

Suppose an opportunity has:

• $40,000 value
• 30% historical win probability from its current stage
• 80% gross margin
• 75% attribution confidence

Its expected contribution is:

$40,000 × 0.30 × 0.80 × 0.75 = $7,200

If the $40,000 is only influenced pipeline—not measured incremental pipeline—and the approved incrementality factor is 70%:

$40,000 × 0.30 × 0.80 × 0.75 × 0.70 = $5,040

Use the company’s observed stage-to-win rates, segmented by market, product, or sales motion where sample size permits. A CRM label such as “proposal: 70%” is not evidence unless historical outcomes support it.

What Does a Worked GEO ROI Model Look Like?

The following 12-month B2B SaaS example is illustrative, not a maxaeo customer result or an industry benchmark. It shows how answer-level observations, accepted commercial records, margin, costs, and scenario assumptions fit together.

Observed and financial inputs

Input	Illustrative value
Monitored prompt runs	4,800
Baseline recommendation rate	18%
Current recommendation rate	31%
Baseline citation rate	22%
Current citation rate	35%
Accepted incremental closed-won revenue	$90,000
Gross margin	82%
Incremental open GEO-linked pipeline	$240,000
Historical stage-to-win probability	25%
Pipeline attribution confidence	60%
Annual GEO program cost	$72,000

The 4,800 prompt runs are test observations, not impressions. Recommendation coverage increased by 13 percentage points, and citation coverage increased by 13 percentage points. Neither change is assigned revenue by itself.

In this example, the $240,000 pipeline input already represents incremental lift against the approved comparison, so no additional incrementality factor is applied.

Realized return

Realized gross profit:

$90,000 × 0.82 = $73,800

Realized GEO ROI:

($73,800 − $72,000) ÷ $72,000 = 2.5%

The program has crossed break-even on accepted closed business, but only narrowly.

Expected return

Expected pipeline contribution:

$240,000 × 0.25 × 0.82 × 0.60 = $29,520

Total expected contribution:

$73,800 + $29,520 = $103,320

Expected GEO ROI:

($103,320 − $72,000) ÷ $72,000 = 43.5%

Confidence range

Scenario	Pipeline treatment	GEO ROI
Conservative	Exclude all open pipeline	2.5%
Base	25% win probability and 60% attribution confidence	43.5%
Upside	35% win probability and 75% attribution confidence	74.3%

The scenario range exposes the assumptions behind the return. Finance can use realized ROI as the floor, while marketing and leadership can monitor how pipeline movement changes the expected case.

How Should Brand Impact Be Measured?

Brand impact should be measured through observable recommendation and buyer-perception changes before any dollar value is assigned. Greater visibility can be harmful if models repeatedly attach the wrong category, outdated pricing, unsupported claims, or unsuitable use cases to the brand.

Track:

• Share of relevant generated shortlists
• First-position recommendation rate
• Inclusion beside priority competitors
• Product, category, audience, and pricing accuracy
• Positive, neutral, negative, or risky framing
• Authoritative third-party citation share
• Unsupported-claim frequency
• Aided and unaided awareness among target buyers
• Branded search and direct-traffic trends
• Buyer references to AI research in forms and sales calls

Monetize brand impact only when all three conditions are met:

1. The brand metric is measured consistently.
2. A controlled study or validated historical model connects the metric to commercial behavior.
3. The resulting value does not overlap with attributed pipeline or closed revenue.

Otherwise, report brand impact beside financial return. An unmonetized, verified improvement is more credible than an invented “AI media value.”

What Should an Executive GEO Dashboard Show?

An executive dashboard should show financial outcomes, a few leading indicators, evidence quality, and the next decisions. It should answer three questions quickly: Did recommendation performance improve? Did qualified demand respond? Is the expected commercial contribution larger than cost?

Dashboard block	Recommended reporting
Return	Realized gross profit, expected pipeline contribution, cost, realized ROI, expected ROI
Recommendations	Intent-weighted recommendation rate, first-position rate, competitor share
Evidence	Citation coverage, authoritative-source share, inaccurate-claim count
Demand	Identifiable AI referrals, self-reported discovery, engaged target accounts
Pipeline	Sourced, influenced, stage progression, velocity, closed-won
Confidence	Value by deterministic, corroborated, probable, directional, and unattributed tiers
Counterfactual	Baseline or holdout change and documented confounding events
Action	Three prioritized content, source, measurement, or technical changes

Show absolute values and changes from baseline. Annotate product launches, pricing changes, PR coverage, site migrations, paid campaigns, and monitoring-method updates.

How Does AI Search Monitoring Support GEO ROI?

AI search monitoring supplies repeated observations for the recommendation and citation layers. It supports ROI analysis only when records are timestamped, segmented, exportable, and joinable with analytics and CRM data.

A suitable system should provide:

• Versioned prompt-set management
• Stable scheduling across relevant answer engines
• Mention, recommendation, rank, sentiment, and citation capture
• Answer history rather than current-state screenshots
• Market, language, persona, topic, and intent segmentation
• Competitor comparisons
• Source URL and citation-domain analysis
• Raw exports or reporting integrations
• Transparent scoring definitions

maxaeo can supply the exposure and citation evidence. Analytics, forms, CRM records, financial data, and counterfactual design must supply the remaining layers. Before committing budget, teams can use an AI search monitoring ROI and shortlist-risk model to test whether the expected decision value justifies the monitoring cost.

Google states that existing Search technical and content requirements also apply to its AI features and that no special AI-specific markup is required. Its official documentation for AI features and websites also explains how traffic from these experiences appears in Search Console reporting. Use that data as supporting landing-page evidence, not answer-level attribution.

Which GEO ROI Mistakes Should Teams Avoid?

The most damaging mistakes inflate audience size, commercial credit, or economic value. Avoid:

• Calling monitored prompt runs “impressions”
• Treating every mention as an endorsement
• Assigning revenue directly to citations
• Using AI referral traffic as the only discovery signal
• Combining sourced and influenced pipeline
• Counting open pipeline at full face value
• Leaving closed deals in open pipeline
• Applying generic stage probabilities instead of CRM history
• Reporting revenue without applying gross margin
• Comparing annualized benefits with partial-period costs
• Excluding internal labor, technical work, PR, or content expense
• Backfilling attribution rules after outcomes are known
• Changing prompts, weights, or scoring without a new baseline
• Selecting only prompts where the brand performs well
• Presenting attribution as proof of incrementality
• Monetizing brand visibility without validated evidence
• Hiding weak evidence inside a single blended return

A defensible model can show negative ROI. That result identifies whether the constraint is weak recommendation coverage, poor citations, incomplete demand capture, slow pipeline progression, low margin, or excessive program cost.

How Can a Team Build the Model in 90 Days?

A 90-day rollout should establish measurement integrity and leading indicators, not promise mature revenue attribution before a normal sales cycle has elapsed.

Days 1–30: Define and instrument

• Agree on the commercial decision, measurement period, and margin basis.
• Complete a no-code GEO baseline and scorecard.
• Freeze a balanced prompt set by topic, persona, market, and intent.
• Define mentions, recommendations, citations, sourced demand, and influenced demand.
• Preserve raw referral and landing-page data.
• Add structured self-reported attribution to high-intent forms.
• Create CRM fields for AI discovery evidence, confidence tier, and opportunity ownership.
• Calculate direct and allocated program costs.

Days 31–60: Establish the baseline

• Run prompts on a stable schedule.
• Audit citations, source quality, and descriptor accuracy.
• Record existing referrals, conversions, and opportunities.
• Train sales teams to capture buyers’ exact wording without leading them.
• Remove internal, agency, test, and duplicate activity.
• Calculate historical stage-to-win rates and gross margins.
• Select a holdout or document the limitations of a pre/post design.

Days 61–90: Test and report

• Optimize a defined treatment group.
• Leave the comparison group unchanged where practical.
• Annotate every material content, PR, product, and technical intervention.
• Compare recommendation, citation, demand, and pipeline changes.
• Reconcile channel ownership at opportunity level.
• Publish conservative, base, and upside scenarios.
• Assign owners to unresolved data gaps and the next optimization cycle.

Frequently Asked Questions About GEO ROI

How long does it take to measure GEO ROI?

Recommendation and citation indicators can change within weeks, but realized revenue usually requires at least one normal sales cycle. Report exposure and evidence weekly, attributable demand monthly, pipeline by stage, and realized return quarterly or over a rolling 12-month period.

Do not judge a six-month B2B sales motion using only first-month contracts. Early pipeline estimates should remain probability- and confidence-adjusted.

Can AI referral traffic be the only attribution source?

No. AI referrals provide strong evidence when the source is preserved, but they miss buyers who copy URLs, switch devices, search for the brand later, or use interfaces without recognizable referrer data.

Combine referral records with structured self-reporting, CRM campaign data, contemporaneous sales notes, and account-level engagement. Preserve the evidence types separately.

What is a good GEO ROI benchmark?

There is no reliable universal benchmark. Margins, contract values, sales cycles, program costs, prompt sets, attribution coverage, and pipeline treatment vary too widely.

Use the company’s pre-program baseline, approved investment hurdle rate, and alternative uses of the same budget. A positive ROI means value exceeded cost; it does not automatically mean the program outperformed other investments.

What attribution window should GEO use?

Use a window long enough to cover the normal period from research to conversion or opportunity creation, then evaluate closed revenue over a full sales cycle. Document separate windows for demand creation, opportunity creation, and closing.

Do not expand the window after discovering a large deal. Changes require a new methodology version and should not be applied retroactively without restating the baseline.

Should brand impact be included in the ROI formula?

Only when a defensible study connects the measured brand change to economic behavior. Controlled awareness research, conversion analysis, or a validated relationship between shortlist inclusion and opportunity creation may support monetization.

Otherwise, report recommendation share, descriptor accuracy, sentiment, citation authority, and buyer recall beside the financial calculation.

Is GEO ROI different from SEO ROI?

The financial logic is the same, but the observable journeys differ. SEO commonly begins with rankings, impressions, clicks, and landing-page conversions. GEO begins with generated answers, recommendations, citations, and visits that may be delayed or unattributed.

Both require complete costs, attributable demand, CRM outcomes, margin, and a counterfactual.

Is GEO ROI the same as ROAS?

No. ROAS compares attributed revenue with advertising spend. GEO ROI compares incremental economic contribution with the complete cost of monitoring, labor, content, authority-building, technical work, and operations.

ROAS can be useful for paid distribution inside a broader program, but it should not replace the full-cost GEO calculation.