Case Study
Target Visibility Rate: A Stripe Case Study
AI visibility does not mean much if the wrong audience is seeing you. Kojable's Target Visibility Rate shows whether a brand appears when its ideal buyers ask AI systems the questions that matter.
In this experiment, Stripe was measured across finance personas to understand whether visibility held up when buyer context changed.
Measurement problem
Why prompt-level tracking is not enough
AI visibility is not just about finding the best prompt. It is a problem of who is asking, when they are asking, and why they are asking. A brand can look visible in one generic prompt and still miss the buyer segments that matter most.
Prompt checks are useful for spotting examples, but they can overstate progress when the sample is small or the prompt wording is fragile. Kojable built Target Visibility Rate to make visibility measurement repeatable by persona.
Answer: TVR shows whether a brand appears when its ideal buyer asks an AI system high-intent questions in the right context.
Target Visibility Rate
What TVR measures
TVR measures visibility lift by persona in a controlled environment. Scores run from 0 to 100, where 50 represents parity. Higher scores indicate stronger visibility for the target persona and context being tested.
Persona fit
Whether the brand appears for the buyer function and use case that matters.
Context quality
Whether visibility appears in high-intent finance questions, not only generic prompts.
Controlled scoring
Whether visibility lifts above parity when response volume is large enough to reduce noise.
Operational signal
Whether teams can use the score to prioritize content, sources, and competitive work.
Experimental setup
Stripe visibility across finance personas
Kojable used Stripe as the example brand and compared visibility across persona-specific finance queries. The goal was to understand whether Stripe appeared when different finance buyers asked AI systems high-intent questions.
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Targeted 12 finance personas
Each persona represented a different buyer context and finance workflow.
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Analyzed 1,500 AI search responses
The larger sample helped avoid overreacting to noisy, low-sample wins.
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Tracked 7 Stripe domains
The experiment measured visibility across the domains most relevant to Stripe's AI footprint.
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Compared persona-specific finance queries
Responses were grouped by buyer context so visibility could be compared across functions.
Findings
What TVR reveals
TVR becomes more useful when measured across enough responses and personas. Instead of treating each prompt as a one-off result, teams can group persona signals into buyer-function groups and see where the brand is strong or under-indexing.
For Stripe, the point of the experiment was not to find a single winning prompt. It was to evaluate whether visibility persisted across finance contexts where buyer intent, terminology, and evidence needs differ.
Operational use
How teams can use TVR operationally
TVR can guide content strategy, competitive strategy, and measurement. For content, teams can build specific pillars for the buyer segments that under-index. For competition, they can identify the evidence ecosystems competitors are winning in. For measurement, they can track progress by persona instead of relying only on fragile prompt strings.
AI visibility becomes useful when it is tied to buyer context. TVR gives teams a way to move from isolated prompt checks to repeatable visibility measurement by persona.
Next step
Measure visibility by buyer context
Visibility means being visible to the right person, in the right context, at scale.