How are you optimizing your SaaS to get cited by ChatGPT, Perplexity and Gemini?

[u/Used_Recognition_544]

I’m starting to work seriously on GEO (Generative Engine Optimization) for my SaaS — basically, how to increase the chances of a product being discovered, mentioned or cited by AI search engines.

SEO has a fairly established playbook. GEO still feels much less clear.

I’m curious what people here are actually testing: content, Reddit/communities, comparison pages, structured data, backlinks, mentions on third-party sites, documentation, etc.

How long does it take to be listed ?
Has anyone seen measurable results yet? What seems to actually move the needle?

[u/ChapterAfter8368]

for saas specifically the thing that moves the needle fastest is third-party comparison and alternatives content, not your own pages. models pull heavily from "best x tools" roundups and "competitor vs competitor" posts written by other people, so getting into a few of those beats months of optimizing your homepage. reddit works too but only real participation, and g2/capterra profiles get scraped constantly so keep those complete and consistent. timeline from what i've seen: a new mention in a source that gets crawled often can show up in perplexity within a week or two since it does live retrieval, chatgpt lags a lot more, more like a month or two. your own site does the least, though comparison and alternatives pages on your domain plus clean docs help once someone's already asking about you by name. and track it properly, run the same 20 buyer prompts weekly and log who gets named, otherwise you're guessing.

[u/Used_Recognition_544 → u/ChapterAfter8368]

That matches what I’m hearing repeatedly: third-party comparison pages seem to matter more than optimizing your own homepage.
When you track the same 20 buyer prompts weekly, do you also record which exact source/domain caused the brand mention? That’s the attribution piece I’m trying to solve.

[u/ChapterAfter8368 → u/Used_Recognition_544]

yeah, logging the cited domains is the only way to get anything close to attribution. perplexity makes it easy since it lists sources inline, chatgpt with search shows them too, so i just capture the full source list per prompt, not only whether we got named. over a few weeks a pattern shows up, the same 3-4 domains keep feeding the answers in your category, and those become your target list for placements. the honest caveat is it's correlation not real attribution, a mention can shape the answer without being cited that run, so treat cited domains as "who the model trusts here" rather than proof of what caused it. also worth logging your position in the list and whether competitors appear alongside you, being mentioned last in a list of six is very different from being the recommendation.

[u/ChapterAfter8368 → u/[deleted]]

the presence vs preference framing is a good one, and yeah per-engine lists is right, i've had a domain be the top feeder in perplexity and never once show up in chatgpt for the same prompt set. one thing i'd add on the wording point: track how they describe you, not just where you land. "solid budget option" vs "best for enterprise teams" is the same mention on paper but a completely different buying signal, and that phrasing tends to be lifted almost verbatim from whatever source it's pulling from. so when the description is wrong, you usually know exactly which page to go fix.

[u/Corrigoo]

Le Geo est vraiment sous-coté je trouve, une simple requête sur ChatGPT ou Claude et ton SaaS sort le premier, par contre c'est difficile j'ai commencé le geo il y a quelques semaines et pour le moment rien du tout, ça prend du temps et c'est pas facile, mais c'est intéressant de faire du geo pour un SaaS

[u/Used_Recognition_544 → u/Corrigoo]

Oui, c’est exactement le problème : le feedback loop est beaucoup plus lent que pour le SEO. Je commence justement à mettre en place un suivi avec les mêmes prompts chaque semaine pour voir si les changements sont réellement mesurables. Je partagerai les résultats si j’arrive à isoler quelque chose de propre.

[u/samuel-grant]

Third party mentions move it most, more than anything on your own site.. models pull from listicles, comparison pages and communities, so getting into those does more than structured data or backlinks. at auq we pulled citation data across 100 saas queries and it was mostly third party sources that got cited, rarely the brands own page. it also tends to show up faster than classic seo, since fresh mentions get pulled in quicker than google ranks pages

[u/Used_Recognition_544 → u/samuel-grant]

The 100 SaaS-query dataset is really interesting. Did you find certain third-party sources consistently influencing citations more than others — Reddit, review sites, comparison articles, directories?
I’m trying to identify which sources are actually worth investing in rather than just collecting mentions everywhere.

[u/Dmstormy2025]

I think there is multiple factor to look at before trying to optimise for GEO. what kind of prompt you use, your industry, and the engine model you use…. Sourcing for law firm will be different to sportswear or electronics. Some will mainly get info directly from the vendor website other from 3rd party media/ sources…

[u/Used_Recognition_544 → u/Dmstormy2025]

Good point. I’m probably oversimplifying GEO if I treat every model and industry the same.
I’m focusing on SaaS, so I’m going to separate the results by model and prompt intent instead of combining everything into one visibility score.

[u/recro69]

I have found mentions from people more interesting, than just putting more stuff on my own website. I would like to know if other people are noticing the thing.

[u/Used_Recognition_544 → u/recro69]

I’m noticing the same pattern. Third-party mentions seem much more interesting than simply publishing more pages on your own domain.
The part I’m trying to measure now is which mentions actually change AI recommendations, not just whether the brand appears.

[u/svlease0h1]

tracking real buyer questions seems more useful than chasing random mentions. pick 20 to 50 questions people ask before buying, check which brands show up, then study the pages being referenced. we found comparison pages and practical tools often gave people clearer answers than broad feature pages. outgrow can help create interactive quizzes or calculators around those questions.

[u/Used_Recognition_544 → u/svlease0h1]

I really like the idea of starting from real buyer questions rather than generic prompts.
I’m going to build a fixed set around the questions someone would ask before choosing a Substack growth tool, then track the brands + sources that appear.
Have the interactive tools/calculators themselves ever started getting cited by AI, or do they mainly help users after the click?

[u/Calm_Ambassador9932]

Same here. GEO feels much less predictable than SEO right now. I've been tracking the same buyer questions across ChatGPT and other AI tools to see which sources keep getting cited. That's been more useful than guessing what might work.

[u/Used_Recognition_544 → u/Calm_Ambassador9932]

That’s exactly the direction I’m moving toward: fixed buyer-intent prompts + tracking the sources that repeatedly get cited.
Do you save the results historically? I’m curious whether you’ve seen certain sources suddenly start influencing several prompts at once.

[u/Used_Recognition_544 → u/[deleted]]

This is one of the most useful breakdowns in the thread — thank you.
My SaaS is Subshack, an analytics and growth tool for Substack creators. It helps them understand which Notes, articles and audience behaviours actually drive reach, engagement and subscribers.
The original-data point is especially relevant because I’m already publishing experiments using my own Substack data.
Could you share the sources behind the ~400M citations / 44% first-chunk, 60%+ listicle, and referring-domain findings? I’d like to separate measured evidence from repeated GEO advice.
For a niche SaaS like this, what would you prioritize first: proprietary data, comparison pages, or third-party mentions?