EX-04. Marketing Automation. AI visibility audit.

HubSpot

HubSpot leads Marketing Automation in both category-named and problem-first responses.

Across 270 responses, HubSpot appears in 57.8% of category-named queries. When a buyer describes a symptom instead of naming the category, the shape changes.

14/63

HubSpot in problem-first

HubSpot's presence in symptom-shaped queries.

Overall category presence: 156 of 270 (57.8%). In problem-first specifically, 35 of 63 responses in this bucket name no vendor at all (55.6%). 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.

our onboarding emails aren't working — how do I diagnose that

# Diagnosing Broken Onboarding Emails

Great question — "not working" can mean a lot of things, so the trick is to isolate *where* in the pipeline things are breaking. Here's a systematic way to diagnose it:

## 1. Define what "not working" means

Before touching anything, get specific:

- **Not sending at all?** (trigger/automation issue)

- **Sending but not delivered?** (deliverability/spam issue)

- **Delivered but not opened?** (subject line, sender, timing)

- **Opened but no clicks/conversions?** (content/CTA issue)

Each of these has a totally different fix.

## 2. Check the trigger

Start at the top of the funnel:

- Is the event that *should* fire the email actually firing? (e.g., signup webhook, user property update, list add)

- Log into your ESP/automation tool and check the **automation history** or **journey logs** for a specific test user

- Did the user actually enter the workflow? If not, the trigger condition is broken (filter too strict, event name mismatch, etc.)

- Run a test signup with a fresh email and trace it end-to-end

## 3. Check sending status

If users are entering the workflow but not receiving mail:

- Look at your ESP's **send logs** — was the message queued? Sent? Bounced? Suppressed?

- Check suppression lists — the test address may be on a bounce/complaint/unsubscribe list

- Verify sending domain authentication (SPF, DKIM, DMARC all passing)

- Check...

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 270 responses. The gap between the two is the recommendation gap.

#	Brand	Appear	App %	Pick %
01	HubSpot	156	57.8%	21.9%
02	ActiveCampaign	121	44.8%	2.2%
03	Customer.io	87	32.2%	7.4%
04	Marketo	82	30.4%	4.4%
05	Brevo	79	29.3%	5.9%
06	Sendinblue	57	21.1%	3.7%
07	MailerLite	47	17.4%	0.4%
08	Klaviyo	41	15.2%	0.0%
09	Mailchimp	41	15.2%	0.0%
10	Braze	39	14.4%	2.6%
11	Pardot	35	13.0%	0.0%
12	Salesforce Marketing Cloud	28	10.4%	0.4%

HubSpot appears in 57.8% of responses and is the sole or primary pick in 21.9%. That is a 35.9-point gap. 10 appearances are UNFAVORABLE.

Top-mentioned brand in each type of buyer query.

Bucket	Top	n	Runner-up
Category discovery	HubSpot	34	ActiveCampaign 27
Comparison	HubSpot	27	ActiveCampaign 19
Segment fit	ActiveCampaign	33	Brevo 27
Evaluation	HubSpot	20	Marketo 12
Pricing	Brevo	18	HubSpot 17
Trust	Customer.io	9	Marketo 9
Switching	ActiveCampaign	18	HubSpot 18
Problem-first	HubSpot	14	Customer.io 13

Problem-first zoom.

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

#	Brand	Count	%
01	HubSpot	14	22.2%
02	Customer.io	13	20.6%
03	ActiveCampaign	8	12.7%
04	Resend	7	11.1%
05	MailerLite	7	11.1%
n/a	Zero-vendor responses	35	55.6%

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

hubspot.com HubSpot44
customer.io Customer.io39
nation.marketo.com Marketo32
blog.hubspot.com HubSpot22
activecampaign.com ActiveCampaign20
mailsoftly.com72
superframeworks.com66
encharge.io63
saasemailplatforms.com40
moosend.com39

SEO listing sites

sequenzy.com168
saashero.net54
default.com32
hellyeahai.com23
aigrowthagent.co17

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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