Getting
found isn't enough. AI still needs enough information to recommend
you.
Getting your name into an AI answer feels like a win, and it is.
But then the customer asks a follow-up:
Which option is best for a birthday group?
What does the price include?
Is it private?
Will it work for a 5-year-old?
Is there availability on Saturday?
Is it good for a romantic date?
To answer those questions, AI needs to understand what you sell, who
it fits, what it costs, where it starts, when it runs, and what makes
you different.
Being found gets you into the conversation. Being understood keeps
you there.
A
broad question and a specific question need different sources
Ask AI for fun things to do in a destination, and it leans heavily on
broad articles and lists from neutral sources that provide insight and
an original perspective.
Ask which operator is the perfect fit for a specific customer, and
the source mix changes quickly.
We grouped our tracked questions into 3 levels because each one asks
the AI to do a different job. Look at the sources that AI cites for each
of these prompts/questions:
Level 1 · Broad
Local discovery
Asks about the destination before the customer has chosen an activity.
Best-of lists lead broad discovery with 34% of citations. Business websites of any kind supply 27.6%, while the tracked operator's own site supplies 1.4%.
Level 2 · Narrower
Specific activity
Names the kind of experience but not the customer's situation.
Prompt analysis
AI
What are the best wine tasting tours in Morgan Hill?
The source mix changes quickly. Once the customer names an activity, business websites supply 51.6% of citations and the tracked operator's own site supplies 12.2%. Category, product, and homepage content all take a larger role.
Level 3 · Ready to choose
Very specific
Adds the people, occasion, preferences, or constraints that determine fit.
Prompt analysis
AI
My friends and I want to celebrate a birthday with wine tasting near San Jose. What are the best tours in Morgan Hill?
When the customer adds a real-life constraint, business websites supply 53.2% of citations and the tracked operator's own site supplies 11.8%. Product, category, and homepage content now share the work.
The change happens as soon as the customer names an activity.
"Best-of" lists account for 34% of citations during local discovery,
then fall to 11.7% for specific-activity questions.
At that same step, product pages rise from 2.4% to 19.8%, while
homepages rise from 4.8% to 17.3%.
Very-specific questions do not push the numbers in one perfect
direction. They keep category, product, and homepage content in a much
larger role than those pages had during broad discovery. The important
change is not a smooth climb. It is the jump from “What can I do here?”
to “Which version of this activity fits me?”
This chart describes the kind of page AI cited. It does not tell us
who published it. A business page may belong to the tracked operator,
another operator, or a different local business. That distinction
matters, so we kept the publisher view in the deeper analysis below.
Based on 510 questions with available prompt text from the frozen
December 1, 2025 through June 9, 2026 study window. Those questions
cover 99.94% of citations in the 530-prompt source file. We calculated
citation share inside each question before averaging across
questions. Deeper analysis: how AI chooses sources as questions narrow
Source mix across 510 questions with available prompt text from
the frozen December 1, 2025 through June 9, 2026 study window. Citation
shares were calculated within each question and then averaged, so every
question has equal weight.
Business websites collectively supply 27.6% of citations during local
discovery, 51.6% for specific-activity questions, and 53.2% for
very-specific questions. The tracked operator's own site is a smaller
part of that total: 1.4%, 12.2%, and 11.8%, respectively. “Business
websites” should not be read as “the operator we tracked.”
This view includes the 71 “where to book” questions with at least
one positive citation to the tracked operator's own site during the
frozen December 1, 2025 through June 9, 2026 study window. Shares were
calculated inside each question before averaging across
questions.
When the tracked operator's site does enter a where-to-book answer,
its homepage and product pages do most of the work. Together they
capture 80.6% of the operator's citation share. Its articles and guides
capture 0.2%.
Page type crossed with publisher type for 71 “where to book”
questions during the frozen December 1, 2025 through June 9, 2026 study
window. Cells sum to 100%. The full-window URL classifier leaves 5.1% of
citations in “Other,” and those citations remain visible instead of
being forced into a cleaner category.
The cross-section is messy in a useful way. Booking answers pull from
operator sites, other businesses, official tourism sources, platforms,
and pages we could not classify cleanly. AI is not following one
universal source hierarchy. It is assembling an answer from whichever
pages can support the question.
Very specific questions from the frozen December 1, 2025 through
June 9, 2026 study window. The chart includes information needs
represented by at least 15 tracked questions. A question may contain
more than one need, but every row shows its own page-type mix and sums
to 100%.
Different details lead AI to different pages. Group-format and
transportation questions pull harder toward product and homepage
content. Timing questions lean more toward guides. That is why “just
make a page for AI” is not a complete strategy. The useful page depends
on what the customer needs to know.
These patterns describe Gondola's tracked questions. They do not
represent real-world query volume or prove that a page type caused a
recommendation or booking.
We
built 1 new page specifically for AI and it increased citation rate by
121%
This was one of our biggest findings in our study that influenced AI
visibility directly.
It's called the AI Start Here page and it's a
technique used by many leading websites already.
Operators with an AI Start Here page appeared in 53% of tracked
questions, compared with 24% for operators that did nothing.
It is a public HTML reference page that gathers information Gondola
already has about an operator at 1 stable URL. The page itself gets
regenerated automatically any time that an operator changes their
products, pages, or new reviews get imported.
Page builders appeared in 53% of tracked questions versus 24% for
the no-intervention group. After adjusting for site strength, the
estimated lift tied to the page itself was 7 percentage points.
The version we tested was assembled from 5 layers:
Business identity
Pulled from the operator's Gondola company and website
settings.
Includes the company name, phone number, domain, booking link, and
active LinkedIn, Twitter, Instagram, Facebook, YouTube, and WhatsApp
profiles.
Published website content
Starts with the live website's HTML structure so the page remains
part of the operator's own site.
Pulls every available WordPress page and includes its title and
published content.
Tours and activities
Pulls directly from the operator's product records in Gondola.
Includes descriptions, sales pitches, highlights, itineraries,
inclusions, exclusions, product FAQs, locations, and durations.
Customer evidence
Imports the latest qualifying reviews from the sources the operator
has enabled, including Google, Tripadvisor, Airbnb, Yelp, and
Trustpilot.
Uses the operator's configured minimum star rating and keeps the
review text, author, date, source, and rating together.
Questions and local knowledge
Generates up to 100 Q&As from the operator's products and
website pages, 100 from patterns in customer reviews, and 100 from the
destinations the operator serves.
Organizes those answers into products and services, customer
questions, and destination guides so AI can retrieve the relevant detail
quickly.
It is not a secret landing page or a replacement for the pages
customers use to compare and book.
Think of it as a reference library for the business: a single place
where AI can retrieve product details, pickup rules, itineraries,
cancellation policies, and pretty much everything else it would want to
know without having to piece together the entire record from
scratch.
The page has evolved several times since this experiment. Gondola
customers today get a more advanced version of it.
Across 14 page builders and 20 comparison operators, the builders
appeared more often at every question level: 10.8% versus 5.5% for local
discovery, 61.8% versus 41.3% for specific activity, and 59.8% versus
46.2% for very specific questions.
The page was much more useful once the customer had an activity in
mind. At that point, AI needs real details about the operator's
products, policies, and what makes each experience different. That is
exactly what the AI Start Here page brings together.
Deeper analysis: results by AI engine
The page builders were about 1.4x to 1.5x as visible on every AI
engine in the wider descriptive comparison. Page builders received 36% to 50% more citations on every engine
in the wider descriptive comparison.
These platform charts compare all operators that had the page with
operators in the other test groups. They are useful for seeing whether
the pattern was confined to 1 engine, but they are not a clean
page-versus-no-page treatment estimate.
AI
directly cited this new 'AI Start Here' page 5,784 times during our
experiment
The group comparison is encouraging, but the URL data is even more
useful because we can see AI using the pages themselves.
13 of the 14 operators with an AI Start Here page had that exact page
cited.
Together, the pages received 5,784 citations during the study, and
the most-cited page earned 1,387 on its own.
13 of the 14 AI Start Here pages were cited directly. The range
runs from 1,387 citations for the leader to 0 for 1 page that never
entered the index.
That proves something narrower than a booking result: AI used these
pages as sources. It does not prove that the pages caused a
recommendation or a booking.
Deeper analysis: what AI cited from these pages
AI Start Here pages earned 3,683 citations for very-specific
questions and 1,889 for specific-activity questions, compared with only
209 for local discovery.
This page seems to help after AI has a reason to consider the
operator and needs exact details about the business or activity. It
doesn't replace the outside sources that introduce businesses during
broad research, and it shouldn't try to.
Specific product descriptions, Q&As, and local details
attract more citations than generic boilerplate.
What happened when
2 operators left Gondola
I'm a little embarrassed to show this because we lost customers
during the experiment.
Still, it taught us something we could not have learned from the
operators who stayed.
One business closed and its website went down completely, while
another moved to the GoDaddy Airo website platform. In both cases, AI
kept mentioning the business by name while citations to its own website
fell drastically.
So the AI kept mentioning the brand because it was mentioned across
the web, but it failed to pull specific details and citations to the
site itself collapsed.
After each website change, brand mentions held comparatively
steady while citations to the operator's own website fell sharply. The
dashed line shows the median visibility of operators that stayed on
Gondola.
Two isolated cases don't prove that leaving Gondola would cause a
decline. I wouldn't trust that claim if a software company made it about
a competitor, and you shouldn't trust it from us either.
But the narrower lesson is still useful: AI can remember a brand
while losing a current source for its products and details.
Moving platforms isn't the problem by itself. The risk is removing
useful URLs, skipping redirects, or replacing detailed pages with thin
or stale information.
If you move your website, make sure you preserve or improve the
information and the URL routes so that AI has somewhere current to
point.
90%
of Gondola-powered operators hold the top 3 citations across all AI
systems for the prompts we tracked
[SHAMELESS PLUG WARNING]
While we were in there, we wanted to understand how Gondola website
operators compared to their immediate competition.
This was a very pleasant surprise.
For 23 of 31 (74%) tracked brands, Gondola-powered operators were the
most-cited business site in its market.
For 28 of 31 (90%) tracked brands, Gondola-powered operators ranked
in the top 3 across all AI prompts.
It means that, while the businesses were on Gondola, their own
websites usually supplied AI with more useful pages than any competing
operator website in the market.
For 23 of 31 active operators, the operator's own domain earned
more citations than every competing business domain in its market. 28 of
31 ranked in the top 3.
That is the golden part of this finding. Your website does not have
to be the biggest source on the internet. It needs to be the clearest
and most useful source about your own business, and for most of our
operators, it was.
What
we tested that did not meaningfully change AI visibility
The useful result is not only what worked. We also tested 3 common
ideas about operator websites that did not hold up well enough to build
a strategy around:
Adding more schema types: Keep structured data
complete and correct, but more types did not meaningfully improve AI
visibility.
Publishing more blog posts: Post count and total
blog volume had no positive relationship with visibility. Only about 2%
of article citations went to the operator's own blog.
Writing longer pages: The small content-depth
analysis did not produce a defensible word-count target.
The bias here is that Gondola websites are already schema-rich.
80% of the sites in our original cohort shipped the same rich 12-type
structured-data bundle, including FAQPage, Organization, TravelAgency,
BreadcrumbList, WebPage, and WebSite.
We keep that information complete because it helps search engines
understand the page and can support richer search results. It just did
not explain the wide differences in AI visibility we observed.
Deeper analysis: what did not meaningfully change AI visibility
Schema is useful
hygiene, not a growth strategy
Adding more schema types did not meaningfully separate the pages AI
cited most from the pages it cited least. Across 139 rival operators,
overall structured-data quality had a small positive relationship with
visibility, but the adjusted result stayed below the threshold we set
for a material finding.
Completeness carried a faint signal. Validity and the number of
schema types were essentially flat. So, yes, keep structured data
complete and correct. Then improve the information underneath it. Schema
can label a price or an FAQ, but it cannot invent a useful answer.
Across 139 rival operators, stronger structured data came with a
modest raw lift in visibility. After adjustment, the result stayed below
the threshold we set for a material finding. Schema completeness has a faint positive relationship with AI
visibility. Validity and the sheer number of schema types are
essentially flat.
Publishing
more did not make the operator's site more useful
Blog post count, total blog word volume, and the number of posts
tailored to specific questions had no positive relationship with
visibility in this sample. Only about 2% of article citations went to
the operator's own blog, while about 87% went to articles published by
other people.
About 2% of article citations go to an operator's own blog,
compared with roughly 87% for articles published by others.
That is not an argument to stop publishing useful articles. It is a
warning against using archive size as the goal. The AI Start Here pages
received about 10x as many citations per page as the typical operator
blog post because they concentrated useful business information instead
of simply adding another URL.
A separate, small content-depth analysis did not give us a defensible
word-count target either. Longer AI Start Here pages leaned toward fewer
citations, but the sample included only 14 pages and the uncertainty was
too wide to call that a reliable effect. The fair conclusion is that we
found no useful length target, not that shorter pages automatically
win.
Outside sources help AI learn that your business belongs in the
conversation. Your own website gives it the current details it needs
when the customer asks which option fits, what the price includes, or
where to book.
That is the difference between being known and being knowable.
Neither one guarantees a recommendation or a booking. Together, they
give AI better evidence and give the customer a clearer path from an
answer to a decision.
Free live webinar
Date coming soon
Now — what do you actually do about it?
Vitaliy walks through the findings live and shows what to tune on your website first. Bring
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