Intro
Most ROI numbers presented for AI visibility are invented. We will not invent ours.
What follows is the model itself — the variables, the equations, the sensitivities, the scenario framings. Owners, CFOs, and commercial directors can plug their own portfolio data into it and produce a defensible business case. The point is not to predict a number for you. The point is to give you the structure that makes any number you produce honest.
This page is the commercial counterpart to the Capston Core methodology and the Capston Hospitality Scorecard.
Why the ROI model needs to be honest
AI visibility is new. Decision makers know it matters. They also know vendors will say anything.
The temptation is to publish a single headline number — “every AI citation is worth €X.” That number is meaningless without context, and any commercial leader who has run a P&L sees through it immediately. Worse, it sets up the engagement to be judged against a fabricated benchmark.
The honest move is to give the buyer the model, not the answer. A model is testable. A model can be argued with. A model produces conservative, base, and ambitious scenarios so the buyer chooses which assumptions they are willing to defend internally.
That is what this page is.
The value-side equation
The commercial value of an AI citation, expressed per citation per period, is the product of seven variables.
Value per citation = Prompt traffic estimate × Citation share × Click-through rate × Direct-booking conversion rate × Average daily rate (ADR) × Stay nights × Incremental margin vs OTA
Each variable, in plain terms:
- Prompt traffic estimate — the volume of relevant prompts surfacing the brand or its category in a given period, across the AI engines that matter for the market.
- Citation share — the proportion of those prompts where the brand’s own domain (not an OTA, not a review aggregator) is cited.
- Click-through rate — the proportion of cited surfaces that produce a click to the brand’s site.
- Direct-booking conversion rate — the proportion of those clicks that complete a booking on the brand’s own channel.
- ADR — the brand’s average daily rate for the relevant segment.
- Stay nights — the average length of stay per booking.
- Incremental margin vs OTA — the margin the brand keeps because the booking landed direct instead of through an OTA. Industry-standard OTA commissions sit in the 15–25% range; the incremental margin captured by direct routing tracks that gap, net of direct-channel costs.
This is not a forecast. It is a structure. The numbers you plug in are yours.
The cost-side equation
A model without a cost side is a sales deck.
Total investment = Capston Core engagement cost + Partner execution cost + Internal team time
- Capston Core engagement cost — the audit, scoring, prompt library design, and quarterly retest cycle. Predictable, scoped, fixed.
- Partner execution cost — the SEO, content, PR, and technical work commissioned by the brand to act on Capston Core findings. This sits with Digidatale or the partner the brand prefers.
- Internal team time — hours from the brand’s own marketing, revenue, and digital teams to review evidence, approve content, and validate facts.
The brand evaluates the value side against the full cost stack, not just the Capston Core line. A model that hides partner cost or internal time is not credible.
Why the OTA margin gap is the high-leverage cell
If you stare at the seven value-side variables long enough, one cell carries disproportionate weight: the incremental margin vs OTA.
Here is why. The other variables move in narrow bands. Citation share might shift from 8% to 22%. Click-through rate might shift from 4% to 9%. Conversion might shift from 2% to 5%. Those moves matter, but they are bounded.
The OTA margin gap, by contrast, is a fixed structural delta — typically 15 to 25 percentage points of margin per booking. Every booking routed direct, rather than through an OTA, captures that delta. The brand does not have to “earn” it through a better funnel; it captures it the moment routing changes.
This is why AI visibility, properly executed, is not primarily a traffic story. It is a routing story. The commercial gain is in moving demand that already exists from intermediated channels to direct channels. That is the same thesis behind direct booking recovery and OTA capture defense.
When you sensitivity-test the model, the variable to flex hardest is the share of demand that routes direct versus through an OTA. That single cell drives most of the spread between scenarios.
Three scenario framings (conservative / base / ambitious)
Scenarios are not predictions. They are bands within which the engagement could land.
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Conservative scenario. Citation share moves modestly. Click-through stays below market norms. Conversion holds at the brand’s current direct-booking baseline. ADR and stay nights are held flat. Direct-routing gain is treated as partial — some captured demand was already going direct anyway. In this scenario the engagement pays back, but slowly, and the case rests on defensive value (avoided OTA commission, brand-fact correction) more than on incremental revenue.
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Base scenario. Citation share lifts to the upper end of the brand’s competitive set. Click-through reaches category benchmarks. Conversion holds. Direct-routing gain is credited for the demand that genuinely shifts channel because of corrected AI answers and stronger brand citations. In this scenario the engagement is clearly accretive within the typical hospitality budget cycle.
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Ambitious scenario. The brand becomes the dominant cited entity for its high-intent prompt set. Citation share, click-through, and conversion all move into the upper quartile. Direct-routing gain compounds because the brand now also appears in comparison and discovery prompts that previously went to OTAs and aggregators. This scenario requires sustained execution and is the one to underwrite cautiously — but it is the upside the methodology is built to pursue.
Note what is missing: invented numbers. The brand fills in its own ADR, its own stay nights, its own current channel mix, its own commission structure. The framework gives the math; the brand owns the inputs.
What to plug in for your portfolio
To produce a credible business case for an AI visibility investment, gather:
- The relevant prompt set (Capston Core co-designs this; see the methodology).
- Current citation share, by engine, by prompt bucket — measured, not estimated.
- Direct-channel conversion rate, segmented by source where possible.
- ADR and average stay nights by property and segment.
- Current OTA commission terms, blended across the portfolio.
- The internal cost of a direct booking (paid search, loyalty cost, payment processing) so the OTA gap is calculated net, not gross.
What you do not need: industry averages, vendor case studies, or invented benchmarks. Every variable above already exists in the brand’s own systems.
The output is a one-page model with conservative, base, and ambitious totals, the sensitivity of each variable, and a clear identification of which cells the engagement is designed to move.
How this fits into Capston Core
This ROI model is the commercial framing layer on top of everything else Capston Core produces.
- The methodology defines the five-stage process.
- The hospitality scorecard measures the brand’s starting position.
- The direct booking recovery and OTA capture defense playbooks turn findings into action.
- This page tells the CFO what the math looks like before the engagement begins, and what it will look like in review.
→ Back to Capston Core
FAQ
Why won’t you publish a single ROI number?
Because every portfolio differs in ADR, stay length, channel mix, and commission structure. A single number flatters one brand and misleads another. The model gives every brand a defensible answer using its own inputs.
Is the 15–25% OTA commission range accurate?
That range reflects the published commission terms widely seen across major OTAs in the hospitality sector. Brands with negotiated terms or higher-value programs may sit inside or outside that band. Use your actual blended figure.
How quickly does the model produce results in the base scenario?
The Capston Core engagement cycle is quarterly. Most of the value-side movement appears in the second and third cycles, once citation share lifts and routing changes compound. Conservative scenarios are slower; ambitious scenarios are sustained.
Does this model work outside hospitality?
The structure transfers. The OTA-margin variable is specific to hospitality; in other verticals the equivalent is the intermediated-channel margin gap (marketplaces, aggregators, brokers). The rest of the equation is industry-agnostic.
Final CTA block
Build the business case before the engagement.
See your scenario ranges
Read the methodology