EX-07. Customer Support Software. AI visibility audit.

Zendesk

Zendesk leads Customer Support Software in both category-named and problem-first responses.

Across 269 responses, Zendesk appears in 64.3% of category-named queries. When a buyer describes a symptom instead of naming the category, the shape changes.

21/63

Zendesk in problem-first

Zendesk's presence in symptom-shaped queries.

Overall category presence: 173 of 269 (64.3%). In problem-first specifically, 41 of 63 responses in this bucket name no vendor at all (65.1%). That's empty inventory the leader could plausibly claim.

How AI answered a real buyer question in this category.

Query. Bucket 8. Problem-first. Excerpted from published audit.

we get the same 10 support questions every day and nothing changes

That sounds exhausting, and honestly, it's a signal worth paying attention to. Getting the same 10 questions daily means one of a few things is happening:

**The likely culprits:**

1. **Your product has friction points** that aren't being fixed — support is patching over a UX problem instead of the team solving it at the source.

2. **Your documentation/help content isn't discoverable** — the answers might exist but customers can't find them (or don't trust them).

3. **There's no feedback loop** from support back to product/engineering, so the same issues keep resurfacing.

4. **Onboarding is missing something** — if the same "how do I..." questions keep coming, users are hitting the same wall right after signup.

**Some questions worth asking:**

- Are these "how do I" questions (→ UX/onboarding issue) or "why is this broken" questions (→ product bug or unclear behavior)?

- Does anyone track these systematically, or are they just answered and forgotten?

- Who owns fixing the root cause? If the answer is "no one," that's the actual problem.

- What would it cost (in eng hours) to eliminate the top 3 vs. what does it cost in support time weekly?

**A concrete move:** Log every ticket into a simple tally for two weeks. Rank by frequency. Take the top 3 and either (a) fix the underlying product issue, (b) add an in-app tooltip/empty state at the exact moment of confusion, or (c)...

Vendors named in this response NONE

Appearance rank and pick-share rank tell different stories.

Every brand ranked two ways. Appearance = how often the brand is named at all. Pick share = how often the brand is the SOLE or PRIMARY pick, as a percentage of all 269 responses. The gap between the two is the recommendation gap.

#	Brand	Appear	App %	Pick %
01	Zendesk	173	64.3%	8.2%
02	Intercom	143	53.2%	7.8%
03	Help Scout	114	42.4%	10.0%
04	Freshdesk	103	38.3%	2.6%
05	Fin	66	24.5%	2.6%
06	Front	47	17.5%	0.0%
07	Zoho Desk	46	17.1%	1.1%
08	Crisp	45	16.7%	1.5%
09	HubSpot Service	41	15.2%	1.1%
10	Plain	38	14.1%	8.6%
11	Pylon	28	10.4%	0.0%
12	Gorgias	26	9.7%	0.0%

Zendesk appears in 64.3% of responses and is the sole or primary pick in 8.2%. That is a 56.1-point gap. 45 of 173 appearances land as NEUTRAL (cited but not recommended). 23 appearances are UNFAVORABLE.

Top-mentioned brand in each type of buyer query.

Bucket	Top	n	Runner-up
Category discovery	Intercom	36	Zendesk 36
Comparison	Help Scout	27	Intercom 23
Segment fit	Freshdesk	35	Intercom 27
Evaluation	Zendesk	23	Intercom 20
Pricing	Zendesk	19	Freshdesk 13
Trust	Zendesk	10	Fin 9
Switching	Intercom	17	Zendesk 17
Problem-first	Zendesk	21	Intercom 11

Problem-first zoom.

The highest-leverage queries: the buyer describes a symptom rather than naming the category.

#	Brand	Count	%
01	Zendesk	21	33.3%
02	Intercom	11	17.5%
03	Help Scout	3	4.8%
04	Front	2	3.2%
05	Freshdesk	1	1.6%
n/a	Zero-vendor responses	41	65.1%

The domains the assistants pulled from.

Two lists. Vendor-owned and editorial sources on the left. SEO listing sites on the right. The split reveals which channel is doing the citation work.

Established, own content

plain.com Plain155
intercom.com Intercom80
zendesk.com Zendesk79
helpscout.com Help Scout69
helpcrunch.com HelpCrunch39
front.com Front34
support.zendesk.com Zendesk32
kustomer.com Kustomer28
kayako.com Kayako23
getmacha.com96

SEO listing sites

ustechautomations.com15

Methodology and caveats.

Run date. 2026-07-24 UTC.
Sample. 30 buyer questions across 8 buckets, 3 samples per engine. Target 270 responses.
Engines. anthropic, openai, perplexity.
API is not the consumer app. Directionally representative, not literal.
Answers vary. Every prompt sampled 3 times per engine. Frequencies, not binaries.
Two-stage matcher (v0.4). Brand extraction is case-insensitive, word-boundary-anchored. Vendors whose canonical name overlaps with a common English word are flagged ambiguous in the config, and every candidate hit goes to a second-stage LLM adjudication (Claude Haiku 4.5) that reads the surrounding context and answers whether the token is the company or generic usage.
Recommendation classification (v0.5). Every confirmed brand mention runs through a second LLM pass (Claude Haiku 4.5) that classifies the treatment of that brand in that response as one of six codes: SOLE (only vendor named as the answer), PRIMARY (named as the top pick), ALTERNATIVE (named in a short recommended set), CONDITIONAL (recommended only for a stated narrow case), NEUTRAL (mentioned without endorsement, or cited as a source only), or UNFAVORABLE (named with a negative or dismissive frame). Pick share of total = (SOLE + PRIMARY) ÷ total responses.
No editorializing. This exhibit reads the data. It does not claim causation.
Right of reply. Any named brand can request their own free audit. Findings publish alongside.

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