EX-09. Data Warehousing & Cloud Analytics. AI visibility audit.

Snowflake

Snowflake leads Data Warehousing & Cloud Analytics in both category-named and problem-first responses.

Across 269 responses, Snowflake appears in 71.7% of category-named queries. When a buyer describes a symptom instead of naming the category, the shape changes.

27/63

Snowflake in problem-first

Snowflake's presence in symptom-shaped queries.

Overall category presence: 193 of 269 (71.7%). In problem-first specifically, 17 of 63 responses in this bucket name no vendor at all (27.0%). 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 engineers hate our analytics tool and I don't know what to switch to

That's a common pain point! To help you figure out the best switch, I need to understand your situation better. Could you tell me:

1. **What tool are you currently using?** (e.g., Google Analytics, Mixpanel, Amplitude, Segment, etc.)

2. **What specifically do your engineers hate about it?** Common complaints include:

- Clunky SDK/API or poor developer experience

- Difficult instrumentation and event tracking setup

- Poor documentation

- Performance issues (slow dashboards, bloated scripts)

- Data quality/schema issues

- Vendor lock-in or hard-to-export data

- Privacy/compliance headaches

- Cost scaling badly with events

3. **What's your stack and use case?**

- Web, mobile, backend, or all three?

- Product analytics (funnels, retention) vs. marketing analytics vs. observability?

- Roughly how many events/month or MAUs?

- Self-hosted preference or SaaS is fine?

4. **Who else uses it besides engineers?** (PMs, marketing, data team?) Their needs matter too since a switch that pleases engineers but frustrates PMs won't stick.

5. **Budget ballpark?** Some tools (Amplitude, Heap) get expensive fast; others (PostHog, self-hosted options) can be cheaper but require more ops.

Once I know these, I can give you a much more targeted recommendation rather than just listing every tool on the market. In my experience, engineers usually gravitate...

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	Snowflake	193	71.7%	3.7%
02	BigQuery	144	53.5%	0.4%
03	Databricks	124	46.1%	0.0%
04	Redshift	114	42.4%	0.0%
05	Google BigQuery	81	30.1%	0.4%
06	dbt	80	29.7%	0.7%
07	Amazon Redshift	71	26.4%	0.0%
08	ClickHouse	71	26.4%	0.0%
09	Fivetran	49	18.2%	0.0%
10	DuckDB	49	18.2%	0.0%
11	MotherDuck	45	16.7%	0.0%
12	Looker	43	16.0%	0.0%

Snowflake appears in 71.7% of responses and is the sole or primary pick in 3.7%. That is a 68.0-point gap. 180 of 193 appearances land as NEUTRAL (cited but not recommended).

Top-mentioned brand in each type of buyer query.

Bucket	Top	n	Runner-up
Category discovery	BigQuery	36	Snowflake 36
Comparison	Snowflake	36	Databricks 22
Segment fit	BigQuery	32	Snowflake 30
Evaluation	Snowflake	23	BigQuery 17
Pricing	Snowflake	19	BigQuery 12
Trust	Databricks	14	MotherDuck 9
Switching	Redshift	17	Snowflake 17
Problem-first	Snowflake	27	dbt 24

Problem-first zoom.

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

#	Brand	Count	%
01	Snowflake	27	42.9%
02	dbt	24	38.1%
03	BigQuery	17	27.0%
04	Tableau	14	22.2%
05	Looker	12	19.0%
n/a	Zero-vendor responses	17	27.0%

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

motherduck.com MotherDuck119
databricks.com Databricks89
docs.databricks.com Databricks46
snowflake.com Snowflake38
clickhouse.com ClickHouse37
docs.snowflake.com Snowflake20
community.databricks.com Databricks13
tableau.com Tableau12
flexera.com73
definite.app71

SEO listing sites

none in this class0

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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