The AI Engines Keep Bringing Up Matomo: 30 Analytics & Data Software Brands Measured

We asked 4 AI engines — ChatGPT, Perplexity, Claude, Gemini — the questions buyers actually type, about 30 Analytics & data software brands, on 2026-09-12. That is 360 answers. One name kept appearing that we never asked about: the engines volunteered Matomo against 11 of the 30 brands we measured.

Every figure below comes from those answers, and the query to re-run them is at the bottom of this page.

Which engine actually names brands?

Not equally. Gemini named a brand in 54.4% of its answers; Perplexity managed 36.7%. That is a 17.7-point gap between two engines asked identical questions on the same day, which is the whole argument for measuring more than one.

360 answers measured 2026-09-12.

Do the engines agree on who exists?

15 of the 30 brands were named by all 4 engines, and one was named by none of them (Fathom). The rest sit in between, visible to some engines and invisible to others.

The widest split was Amplitude: Claude named it in 100% of the answers we asked about it, ChatGPT in 0%. That comparison rests on the handful of questions we asked about that one brand, not on the full sample — treat it as a lead to investigate, not a law.

Brands, out of 30 measured.

Named is not the same as recommended

A mention is not an endorsement. Of every mention in this study, 39.6% was the engine's first recommendation, while 51.8% was the brand being offered as an alternative to something else. A brand can look visible and still only ever appear as the runner-up.

Share of all mentions in this study.

9 brands were named but never recommended first by any engine: Everhour, Harvest, Heap, Hotjar, Hubstaff, Pendo, Qlik Sense, Segment, TimeCamp. For those, visibility work is not the problem — positioning is.

Which sources are the engines reading?

The engines cited learn.g2.com more than any other source (99 citations, 2.8% of all citations in this study). But the long tail is the real story: the top 10 domains together account for only 16.9% of citations. There is no short list of sites to get listed on — the engines are reading widely.

Citations counted across 360 answers. "Engines" column below shows how many of the 4 cited each source.

Source	Citations	Engines citing it
learn.g2.com	99	3 of 4
domo.com	82	3 of 4
skyvia.com	72	3 of 4
thedigitalprojectmanager.com	65	3 of 4
contentsquare.com	54	3 of 4
toolradar.com	51	2 of 4
g2.com	50	3 of 4
worldmetrics.org	47	2 of 4
myhours.com	44	3 of 4
thoughtspot.com	44	3 of 4

Who do the engines bring up instead?

These names were never in our sample — the engines volunteered them while answering about someone else. Matomo came up against 11 of the 30 brands we measured. If you are in this category, these are the brands you are being compared to whether you like it or not.

Not part of the sample; named by the engines on their own.

Every brand we measured

Visibility is the share of scored answers in which an engine named the brand. Tableau led at 100%; Fathom came last at 0%.

Brand	Visibility	Answers naming it
Tableau	100%	12 of 12
Microsoft Power BI	92%	11 of 12
Power BI	92%	11 of 12
Clockify	83%	10 of 12
Google Analytics	83%	10 of 12
Mixpanel	83%	10 of 12
Google Analytics 4	75%	9 of 12
Looker	75%	9 of 12
Toggl Track	75%	9 of 12
Amplitude	58%	7 of 12
Domo	58%	7 of 12
Qlik Sense	58%	7 of 12
Adobe Analytics	50%	6 of 12
Hotjar	50%	6 of 12
FullStory	42%	5 of 12
Crazy Egg	33%	4 of 12
Heap	33%	4 of 12
Hubstaff	33%	4 of 12
RescueTime	33%	4 of 12
TimeCamp	33%	4 of 12
Time Doctor	25%	3 of 12
Everhour	17%	2 of 12
Segment	17%	2 of 12
Sisense	17%	2 of 12
Timely	17%	2 of 12
Contentsquare	8%	1 of 12
Harvest	8%	1 of 12
Paymo	8%	1 of 12
Pendo	8%	1 of 12
Fathom	0%	0 of 12

What to do with this

1. Measure every engine, not one. The spread between the most and least generous engine in this study was 17.7 points. A check against a single engine tells you about that engine.
2. Check the role, not just the mention. 51.8% of mentions here put the brand in the alternative slot. Moving from alternative to first recommendation is a positioning problem, not a visibility one.
3. Follow the citations, not the keyword list. The engines quoted a wide spread of sources here; learn.g2.com was the single most cited. Earning a place on the pages an engine already reads is what changes an answer.
4. Zero is a starting point, not a verdict. 1 of 30 brands scored zero here. The first mention on any engine is the milestone that proves the sources are being read.

What this does not show

• Each brand was measured on the questions our generator writes for that brand, not on one shared question set, so this is an average of per-brand measurements and not a like-for-like ranking.
• Per-brand splits between engines rest on the few questions asked about that brand, not on the full sample of 30.
• These are measurements taken on 2026-09-12. AI answers change; a score here is a reading on that date, not a permanent property of the brand.

Reproduce this

The raw answers are stored per brand, per engine, per question. The figures above come from:

select "brandName", "stratum", "visibility", "mentionCount" from "StudyResult" where "studyId" = 'cmtyqpr3o0003ji04bhwqpyua' order by "visibility" desc

Want the same measurement for your own brand? The free check runs this exact pipeline.