Arrow 
A credible AI visibility case study separates the work completed, the answers observed, the traffic recorded and the commercial outcomes verified. Show the baseline, dates, denominators, qualification rules and competing explanations. This article is an evidence framework with a fictional example, not a report of Arrow AI or customer performance.
What claim should an AI visibility case study make?
Choose the narrowest claim the records support. A content team may have made product information easier to verify. A monitoring panel may show more citations. A source cohort may contain qualified inquiries. Each is useful, but none automatically proves that the work caused new revenue.
Write the intended headline before analysis, then revise it to match the evidence. If the only verified result is improved citation coverage in a fixed panel, say so. If sales outcomes are not mature, publish an interim observation with a review date. Do not imply a customer engagement exists when the story is hypothetical. The example below is deliberately labeled fictional throughout.
What evidence should you collect before making changes?
Freeze a baseline packet with the monitored questions, observation dates, surfaces, repeat schedule and scoring rules. Preserve the responses and cited URLs, including missing mentions and unfavorable answers. Record the versions of the pages you plan to change. A before-and-after narrative is difficult to audit if the original evidence is missing.
For commercial analysis, record the inquiry definition, sales acceptance criteria, source evidence categories and observation period. State how duplicates are handled. Use the citation tracking guide for the panel and the attribution model for the website-to-CRM records.
- Scope: product, audience, country, language and buyer questions.
- Measurement: sample denominator, successful runs, failures and dates.
- Intervention: exact pages, edits, release dates and responsible owners.
- Commercial records: distinct inquiries, qualification decisions and stage dates.
- Context: launches, pricing changes, campaigns and sales-process changes.
How should you describe the work and its limitations?
List concrete edits rather than a broad claim that a site was optimized for AI. Examples include correcting an outdated integration statement, publishing a supported-use-case table, or adding a worked example to an implementation guide. For each edit, record what information changed and why it mattered to the buyer question.
Keep an alternative-explanations log. A new product, brand campaign, competitor outage or changed observation setup may help explain the results. A comparison group can strengthen the analysis when it is reasonably comparable, but a loosely matched page is not a randomized control. If causal attribution is not supported, describe the outcome as an observed change after the work.
What does a transparent worked example look like?
Fictional teaching example: an imaginary B2B software team updates four existing pages over a month. It uses the same 30 non-branded commercial questions on one predefined consumer surface, with four fresh-session observations per question before and after the edits: 120 scheduled observations per period. All observations succeed in this simplified example. These numbers are invented to demonstrate the method.
The panel contains owned-site citations in 18 of 120 baseline observations and 27 of 120 follow-up observations. The measured citation rate changes from 15% to 22.5%, an increase of 7.5 percentage points. This is a result within the fictional panel, not a market-wide share or a ranking claim. The commercial counts below remain a separate evidence layer.
| Fictional measure | Baseline period | Follow-up period | Interpretation |
|---|---|---|---|
| Owned-site citation observations | 18 / 120 | 27 / 120 | A change within the fixed test panel |
| Recorded AI-referral sessions | 60 | 80 | Recorded traffic under the same source rule |
| Distinct source-associated inquiries | 4 | 6 | Requests after deduplication |
| Accepted inquiries | 1 | 2 | Small counts under the same qualification rule |
| Won deals | 0 | 0 | No won-revenue claim is supported |
How should you interpret the example without overstating it?
The fictional team can report a citation increase in its panel and two accepted inquiries in the follow-up source cohort. It cannot attribute both inquiries to the four edited pages without additional journey evidence. It also cannot claim statistical certainty from a headline percentage. Repeated prompts within a panel are related observations, and the panel was selected rather than randomly sampled from all buyer conversations.
Google explains that acquisition and event attribution use different scopes. A case study should identify which measure it uses. OpenAI also notes that search placement is not guaranteed. A favorable snapshot should not be presented as a durable position that every user receives.
What should the final case-study page contain?
Use this publication order: business question, scope, baseline, work completed, observed results, commercial follow-through, costs, limitations and next review. Attach an evidence reference to each quantitative claim. Obtain the necessary permission for any identifiable customer information; otherwise present an appropriately anonymized account with enough methodological detail to assess it.
End with the decision the evidence supports: continue a bounded test, correct a measurement issue, improve a specific page or pause an unproductive activity. The qualified-lead journey helps separate funnel stages, and the CFO memo turns the case into a budget discussion. Browse the measurement cluster for related methods.
- Headline: the observed outcome with its actual scope.
- Evidence note: data sources, dates, definitions and sample sizes.
- Result table: counts and denominators before percentages.
- Limitations: missing journeys, competing changes and incomplete sales cycles.
- Decision: next action, owner, budget boundary and review date.
Sources and editorial scope
This is Arrow AI's implementation guidance. Examples are illustrative unless identified as dated observations. Source access and good content do not guarantee a recommendation.
Continue through the GEO evidence library, inspect Arrow GEO's measurement limits, or start an audit.