EX-03 HubSpot | The Hint Co.

HubSpot leads CRM. In problem-first, the lane looks different.

Across 270 responses, HubSpot appears in 78.1% of category-named queries. When a buyer describes a symptom instead of naming the category, the shape changes.

33/63

HubSpot in problem-first

HubSpot's presence in symptom-shaped queries.

Overall category presence: 211 of 270 (78.1%). In problem-first specifically, 24 of 63 responses in this bucket name no vendor at all (38.1%). 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.

my sales team lives in spreadsheets — how do I get them off

You get a sales team off spreadsheets by giving them a **simpler, faster way to sell** *and* cutting off spreadsheets as the “source of truth” after a short transition period. The move is part data migration, part behavior change.

Below is a practical, sales-leader-focused playbook.

---

## 1. Diagnose the real problem (it’s usually not “they like Excel”)

Sales reps stay in spreadsheets because they perceive that:

- It’s faster than the CRM

- It matches their mental model of deals

- The CRM feels like admin work, not a selling tool

So your plan needs to:

- Make the CRM directly useful to *their* daily workflow

- Remove the option to “just keep the spreadsheet updated instead”

---

## 2. Design a CRM that feels like *their* spreadsheet, but better

Before you move anything, sit with a few reps and sales leaders and:

- **Define pipeline stages** in plain sales language (e.g., New Lead → Contacted → Qualified → Proposal → Negotiation → Closed Won/Lost).[6][5]

- **Identify essential fields** they actually use:

- Contact: name, email, phone, company

- Deal: stage, amount, expected close date, owner, last activity date[4][6]

- Keep it lean: avoid heavy customization until everyone is comfortable with basics (contacts, pipeline, activity logging).[10]

Your goal: when a rep opens the CRM, the deal view looks like their current sheet—just more visual and...

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	211	78.1%	24.8%
02	Salesforce	153	56.7%	4.8%
03	Pipedrive	127	47.0%	7.0%
04	Zoho	90	33.3%	1.1%
05	Zoho CRM	78	28.9%	0.4%
06	Attio	61	22.6%	7.8%
07	Freshsales	59	21.9%	1.1%
08	Close	49	18.1%	0.7%
09	Folk	26	9.6%	0.0%
10	Notion	22	8.1%	0.0%
11	EngageBay	20	7.4%	0.4%
12	Bitrix24	18	6.7%	0.4%

HubSpot appears in 78.1% of responses and is the sole or primary pick in 24.8%. That is a 53.3-point gap. 12 appearances are UNFAVORABLE.

Top-mentioned brand in each type of buyer query.

Bucket	Top	n	Runner-up
Category discovery	HubSpot	36	Pipedrive 31
Comparison	HubSpot	36	Salesforce 18
Segment fit	HubSpot	33	Pipedrive 31
Evaluation	HubSpot	28	Salesforce 20
Pricing	HubSpot	19	Zoho 18
Trust	Salesforce	18	Attio 9
Switching	HubSpot	18	Salesforce 18
Problem-first	Salesforce	34	HubSpot 33

Problem-first zoom.

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

#	Brand	Count	%
01	Salesforce	34	54.0%
02	HubSpot	33	52.4%
03	Pipedrive	18	28.6%
04	Zoho	6	9.5%
05	Zoho CRM	5	7.9%
n/a	Zero-vendor responses	24	38.1%

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

salesforce.com Salesforce43
nutshell.com Nutshell43
hubspot.com HubSpot36
folk.app Folk21
help.salesforce.com Salesforce17
pipedrive.com Pipedrive16
attio.com Attio16
techradar.com120
prospeo.io57
monday.com56

SEO listing sites

hackceleration.com15
leadhaste.com14

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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