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Product teams. Customer research.
Illustrative score · 12/12 buyer questions. Sampled answers, not Google rankings.
2 sourced discussions
Researchers comparing methods, tools and ways to make findings useful. Both example discussions come from this community.
4 sourced discussions
Product managers discussing research ownership, customer feedback and the tools that support discovery. Four sample discussions connect to this audience.
Fictional example: customer research software for product teams.
Audience · Product teamsAll 6 real discussions in this sample report, with the buyer’s question and its fit for Acorn. Summaries and business fit are rouse’s assessment.
6 discussions in this sample
A research team is evaluating repositories and asks for experience with Gleanly and Marvin.
The author is already evaluating research repositories. For an illustrative research-software business like Acorn, this is a useful example of a category conversation.
A researcher is comparing a single end-to-end platform with a separate research repository on a limited budget.
A researcher is evaluating paid tools against a limited budget. This is a relevant buying conversation for a research-software business like Acorn.
A product manager compares the tools in their workflow, including research platforms, and asks peers for recommendations.
Product teams discuss research software alongside the rest of their workflow. This connects to Acorn’s illustrative audience of product teams.
A discussion asks who owns user research and describes the time, participant access and synthesis challenges product managers face.
The conversation centers on the research workflow and the people responsible for it, which fits a customer-research software business.
A product discussion asks how teams understand customers leaving and decide which features matter.
The need is understanding customer feedback and turning it into product decisions—the workflow in Acorn’s illustrative profile.
Product managers compare the tools that help them do their work.
A broader product-tool conversation reaches the same professional audience. Its research-specific fit is weaker than the repository comparisons above.
Reconstructed source previews. Current community rules and reply availability have not been verified.
AI score categories combine the same recommendation and citation evidence as the score above. The five separate measures below explain the answer sample and website checks, using fictional Acorn data.
4/4 questions · 2/4 recommend · 2/4 cite
4/4 questions · 2/4 recommend · 2/4 cite
4/4 questions · 0/4 recommend · 0/4 cite
100 × completed answers recommending the brand ÷ completed answers. Each answer counts once.
100 × completed answers citing the official website ÷ completed answers. Consulted but uncited pages do not count.
100 × target brand appearances ÷ all recommended brand appearances. Deduplicated within each answer; this is share of this answer sample, not market share.
Observations on the returned page HTML: whether a page title, a meta description and no noindex directive were found. Presence only; it does not prove indexing or search rankings.
Observations on the returned page HTML: whether an H1 and parseable typed JSON-LD were found. Presence only; it does not measure content quality, schema correctness or search features.
Click a brand to explore its example answers. Brands, logos and appearances are illustrative; each brand counts once per answer.
AI score: (70 × brand recommendations + 30 × official-site citations) ÷ completed questions. Combine the raw counts across categories before rounding, rather than averaging rounded category scores. Categories describe buyer intent, not search engines. These are sampled answers, not Google rankings; website checks do not change the AI score.
70% recommendation frequency + 30% official-domain citation frequency, across completed answers only. Ranking uses appearances, then average order of recommendation. This sample is not a market percentile or a consumer-app ranking.
Recommendation presence: 100 × completed answers recommending the brand ÷ completed answers. Each answer counts once.
Source visibility: 100 × completed answers citing the official website ÷ completed answers. Consulted but uncited pages do not count.
Competitive share: 100 × target brand appearances ÷ all recommended brand appearances. Deduplicated within each answer; this is share of this answer sample, not market share.
Search foundations: Observations on the returned page HTML: whether a page title, a meta description and no noindex directive were found. Presence only; it does not prove indexing or search rankings.
On-page structure: Observations on the returned page HTML: whether an H1 and parseable typed JSON-LD were found. Presence only; it does not measure content quality, schema correctness or search features.
Missing evidence stays unmeasured. The sample edition is dated Sep 7, 2026.
All 12 questions from this illustrative answer sample, including where Acorn was not observed.
Also appeared: Northnote, Fieldwork
Also appeared: Northnote, Fieldwork
Also appeared: Northnote, Fieldwork
Also appeared: Northnote, Fieldwork
Also appeared: Northnote
Also appeared: Northnote
Also appeared: Northnote, Fieldwork
Also appeared: Northnote, Fieldwork
Also appeared: Northnote, Fieldwork
Also appeared: Northnote, Fieldwork
Also appeared: Northnote
Also appeared: Northnote
4/5 illustrative website checks passed. These checks stay separate from AI score.
Illustrative finding: Title found: “Acorn — customer research for product teams”.
Illustrative finding: A meta description explains the customer research workflow.
Illustrative finding: Main heading found: “Customer research for product teams”.
Illustrative finding: No noindex directive was found on this page.
Illustrative finding: No parseable JSON-LD was found in the returned HTML.
The communities represented in this sample, connected to the people Acorn is built for.
Researchers comparing methods, tools and ways to make findings useful. Both example discussions come from this community.
Reddit mark shown; no verified custom community icon.Product managers discussing research ownership, customer feedback and the tools that support discovery. Four sample discussions connect to this audience.
Community source checked Sep 8, 2026Community fit is an assessment, not permission to post. Independent communities keep their own rules and moderators.
Your Special Project Lead connects research, content and human community work. Onboarding, Slack and access to recorded work are included, with an end-of-pilot review or monthly delivery reporting.
Find the questions people ask, the communities they trust and the other businesses they consider.
Turn what you know into helpful content that answers the questions buyers ask.
Our specialists contribute useful posts and replies, following each community’s rules and the scope we agree with you.
See the work we’ve done, review what we can measure and decide what comes next.
See what Reddit can do for your brand in 30 days.
Human-led Reddit campaigns with AEO research behind them.
Your ongoing Reddit team: monitoring, replies and community care.
Reddit across your products, brands and markets.
A reviewed proposal, signed agreement and verified payment precede onboarding. The pilot is a one-time engagement; monthly services recur month to month. Scope and targets are agreed together.
Bring your goals. We’ll review the findings together and agree where your team needs support.
AFTER THE REPORT / YOUR NEXT MOVE
The findings set the priorities. Your Special Project Lead connects the research, specialists, and work agreed with you.
01 / SEO
We turn customer questions into a useful content plan, and work with your website owner on the gaps in this report.
Website changes follow the scope we agree together.02 / AEO
We develop clear, supported answers about what you do, then repeat comparable checks to see whether AI recommendations and citations improve.
Progress stays tied to dated evidence.03 / ACTIVE REDDIT COMMUNITIES
From repository choices in r/UXResearch to tool recommendations in r/ProductManagement, we find fresh conversations like these. Our human specialists check community rules and write helpful replies that openly represent your business.
Relevant, permitted links give interested readers a next step. Visits are measured separately in your analytics.Get your AI visibility score, or book a call and we’ll talk through what the report means for your business.
Hi! Let’s look at Acorn, our example business, and where we can help.
Acorn’s AI visibility is low here. People are asking for recommendations, and it’s often missing.
In UX Research, someone’s choosing a research repository. That’s a question Acorn could help answer.
And in Product Management, people are sharing their favorite tools. These are the conversations we’d look for Acorn to join.
Its competitors show up more often in AI answers, giving them more chances to be considered.
Your human rouse team handles the research, writing, and follow-up, with a Special Project Lead looking after you.
We also help plan useful website content around what your customers search for.
And make your expertise clearer for AI, then check whether recommendations improve.
We help put you in the conversations. Real people, helpful replies, openly representing your business, and giving interested readers a reason to visit your site.
Get your AI score or book a call today!
