Worked example / October 20, 2026

B2B agency AI visibility audit: from answers to actions

What should an agency actually do when AI recommends other providers? This walkthrough turns a reviewed ten-answer sample into a practical audit, without confusing brand mentions, citations, and proof of business impact.

By TrackAIMentions. This is an analysis of the agency slice of our October 13 benchmark, not a new study, customer engagement, or before-and-after success story. The named agencies are research candidates, not claimed customers.

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1. Freeze the scope before reading answers

The buyer is a mid-market B2B software company seeking agencies serving the United States. Eight candidate agencies were selected before sampling. Five questions were run once on each of two answer systems: ten answers and 80 candidate-answer observations. Neither the organic questions nor the system instruction named the candidate brands.

Questions: 5
Answers: 10
Candidates: 8
Candidate hits: 14 / 80

The 17.5% rate is 14 candidate-answer hits divided by 80 opportunities. It is not 17.5% of all AI users or market share. One answer can mention several candidates. ChatGPT-style means the API answer setup used in the study, not a verified reproduction of every ChatGPT web session.

2. Use organic questions that represent the buyer job

DSA-01 / category discovery
Which digital marketing agencies are best suited to help a mid-market B2B software company grow in the United States?
DSA-02 / use case fit
Which agencies should a mid-market B2B software company consider to grow non-brand organic demand?
DSA-03 / geography market fit
Recommend digital marketing agencies that serve the United States and have experience with B2B software.
DSA-04 / evidence fit
Which digital marketing agencies have credible evidence of success with SEO and content marketing for B2B software companies?
DSA-05 / alternative shortlist
Create a shortlist of digital marketing agencies for a mid-market B2B software company that needs to grow non-brand organic demand. Explain the fit of each option.

A question naming your own agency can diagnose how AI describes it, but must stay outside the organic score. Do not replace a difficult question just because it produced no mentions.

3. Read platform differences, not just a blended number

Candidate mentions, not a ranking of every provider in the answers. Each platform has five samples per candidate.
Agency	ChatGPT-style	Perplexity	Combined
Single Grain	4/5	0/5	4/10
WebFX	1/5	2/5	3/10
Ignite Visibility	2/5	0/5	2/10
Siege Media	0/5	2/5	2/10
KlientBoost	0/5	1/5	1/10
NP Digital	0/5	1/5	1/10
Victorious	1/5	0/5	1/10
Croud	0/5	0/5	0/10

Single Grain appeared in four ChatGPT-style answers and no Perplexity answers; Siege Media showed the opposite platform pattern in two Perplexity answers. These are observations from one sample per question, not evidence that either platform always prefers a brand. A zero for Croud here does not mean it is absent from every AI answer.

4. Inspect evidence before recommending work

DSA-02 / ChatGPT-style

Here are some categories and specific agency types to consider:

The answer described agency categories rather than naming specific providers. All eight candidate brands receive zero mentions for this answer; the sample is retained.

Answer ID: chatcmpl-EYRCFFvSyIdYcVjH2bHEgWt1TChOK

DSA-02 / Perplexity

Start with **Omniscient Digital, Siege Media, and Directive** for competitive evaluation:

The same question produced a named shortlist on Perplexity. Siege Media is in the frozen candidate set; names outside it remain separate context rather than expanding the scoring set after sampling.

Answer ID: resp_834bb8c1-7a5a-404d-b0f7-882330101199

DSA-04 / Perplexity

not merely rankings

The answer emphasized published business-outcome evidence and attached citations. That is a clue for evidence review, not proof that adding a case study causes an AI recommendation.

Answer ID: resp_92141473-3210-4e0a-989e-714ad9486914

For DSA-02, the category-only ChatGPT-style answer stays in the denominator as a valid zero-brand result. Perplexity named suppliers and returned source links. A cited domain is not automatically a recommended supplier, and a recommendation does not prove that its cited source caused inclusion.

Example source URLs returned with the Perplexity answers: DSA-02 source 1 and DSA-04 source 1. These links document the sampled citations, not our independent verification of every agency or revenue claim in the generated answers.

5. Turn observations into testable actions

Clarify service fit for DSA-02. Audit whether your service page explains the buyer, non-brand demand problem, market, deliverables, and relevant examples. Verify what is missing before rewriting; a category-only answer by itself cannot identify the cause.
Strengthen verifiable proof for DSA-04. Where you have permission and reliable evidence, publish case studies with baseline, timeframe, scope, and business outcomes. Do not copy the model's claimed numbers or promise that a new case study guarantees inclusion.
Investigate the source environment. Review returned citation pages and their editorial context. Correct factual descriptions of your business and pursue relevant earned coverage; do not buy placement to manipulate rankings or manufacture reviews.
Improve discovery paths. Link service, evidence, methodology, and audit pages in context. Check indexability and canonical first, rather than assuming every missing mention is a content problem.
Repeat a stable scope. Keep the questions, market, candidate set, and scoring rules unchanged when measuring again. Label the new date and model setup. Use repeated samples before claiming a trend, and track referrals / qualified leads separately.

Method, limits, and reproducible data

Sampled October 13, 2026, using the benchmark v1 frozen protocol, then two independent agent reviews and adjudication. No new paid sampling was run for this article. The parent study includes 31 brands and 40 answers; this article reuses only its eight-agency, ten-answer slice. It is directional, not statistically stable, and does not measure Google AI Overviews, continuous rank, or business impact.

Run the questions for your own business

The free check samples five organic questions across ChatGPT-style answers and Perplexity. Snapshot expands to 11 organic scoring questions plus nine auxiliary checks. Neither is continuous monitoring. Review the report example or one-time pricing before deciding what you need.

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