Guide · Practitioner
G2, Capterra and Review Sites in AI Answers
Software review sites such as G2 and Capterra are often cited when AI engines answer best-of and comparison questions. Earn reviews honestly: ask all customers, never tie incentives to a positive rating, and keep your profile accurate. In the US, buying reviews that express a particular sentiment is banned ([eCFR](https://www.ecfr.gov/current/title-16/chapter-I/subchapter-D/part-465/section-465.4)), and G2 labels incentivized reviews ([G2](https://legal.g2.com/community-guidelines)).
Facts are from the US eCFR and G2's community guidelines, accessed 30 Oct 2026. This isn't legal advice. Points marked "Our view" are interpretation; example figures are illustrative.
On this page
- Why do review sites matter for AI answers?
- What do the rules say?
- How do you earn reviews honestly?
- How do you keep your profile accurate?
- How should you reply to reviews?
- What should you never do?
- How do you measure impact?
- How do review themes help your product?
- Which review sites should you prioritise?
- Exercise
- Checklist
- From MentionX data: which question types bring review sites into play
- Worked example: Ledgerline revives a stale profile
- How do review sites end up in AI answers?
- How do you choose categories?
- How do you build a steady review flow?
- How do you use what reviews say?
- Who should own review sites?
- What if a review is false or abusive?
- How do you check how answers describe you?
- How long does it take to see a change?
- Common questions
- Related
- Sources
Checked on 31 October 2026. "Our view" marks MentionX opinion. Ledgerline is a fictional company used for illustration.
Why do review sites matter for AI answers?
Our view: when someone asks "best CRM for small teams" or "X vs Y", engines often cite review platforms alongside list articles. Your star rating, number of reviews, category placement and the words reviewers use can all shape how an answer describes you. Check this with the prompt-logging method in finding Reddit threads AI engines cite, logging review-site URLs instead.
What do the rules say?
| Rule | Source |
|---|---|
| Businesses may not write, create or sell fake reviews that misrepresent a reviewer's existence or experience | eCFR 465.2 |
| Businesses may not give compensation or incentives conditioned on reviews expressing a particular sentiment | eCFR 465.4 |
| Material connections behind endorsements must be disclosed | eCFR 255.5 |
| G2 labels incentivized reviews as incentivized | G2 |
| G2 says it will never require or ask that only positive reviews be submitted | G2 |
| G2 says it won't suppress negative reviews | G2 |
| G2 ratings include only data from verified reviewers and public sources | G2 |
Other review sites have their own guidelines; read each one before running a campaign.
How do you earn reviews honestly?
Our view:
- Ask everyone, not only happy customers, at a natural moment such as after onboarding or a renewal.
- Make it easy: a direct link to your profile's review form.
- If you offer an incentive, make it the same regardless of rating, and let the site label it.
- Never write reviews yourself or through staff, friends or agencies.
- Never offer rewards for changing a negative review.
| Good request | Bad request |
|---|---|
| "Could you share an honest review on G2?" | "Leave us 5 stars for a gift card" |
| Sent to all active users | Sent only to promoters from a survey |
| Same incentive for any rating | Incentive only for positive reviews |
How do you keep your profile accurate?
Our view:
- Claim your profile on each major site.
- Keep the product description, categories, pricing (dated) and screenshots current.
- Match facts with your own site and Organization schema.
- Choose categories that reflect what you actually do; wrong categories can put you in comparisons you lose.
How should you reply to reviews?
Our view: reply to negative reviews publicly, briefly and helpfully: thank the reviewer, address the specific issue and offer a direct contact. Don't argue or reveal private account details. Reply to positive reviews sparingly and genuinely. Prospective buyers, and possibly AI summaries, read your replies.
What should you never do?
| Tactic | Problem |
|---|---|
| Fake or employee reviews | Banned under 16 CFR 465.2 |
| Paying for positive reviews | Banned under 16 CFR 465.4 |
| Pressuring reviewers to change ratings | Deceptive; damages trust |
| Review swaps with partners | Undisclosed material connection |
| Flagging honest negative reviews as abusive | Misuse of reporting tools |
How do you measure impact?
Our view:
| Metric | Why |
|---|---|
| Review count and recency | Fresh reviews reflect the current product |
| Average rating trend | Watch direction over quarters |
| Themes in reviews | Tells product teams what to fix |
| Mentions in AI answers | Rerun buyer prompts monthly |
| Review-site referral traffic | Shows buyer use |
How do review themes help your product?
Our view: tag every review by theme, such as onboarding, support, pricing or a missing feature, and share the counts with product and support teams monthly. Fixing a repeated complaint does more for your rating, and for how AI answers describe you, than any review campaign.
Which review sites should you prioritise?
Our view: start with the sites cited most often in your prompt log, then add the ones your buyers mention in sales calls. Spreading effort thinly across a dozen platforms usually leaves each profile stale; two or three well-maintained profiles with recent reviews serve buyers better.
Exercise
Run five category prompts and log which review sites are cited. Then check your profile on each: is the description accurate, are prices dated, and how many reviews are from the last six months?
Checklist
- Profiles claimed and accurate on major sites
- Review requests sent to all customers
- Incentives, if any, independent of rating and disclosed
- No employee, fake or bought reviews
- Negative reviews answered helpfully
- Monthly prompt reruns
From MentionX data: which question types bring review sites into play
Method. Read-only aggregate queries over MentionX scans from 22 August to 29 October 2026 (UTC): 2,217 engine runs (2,154 completed) across 18 anonymised projects and six engines.
| Cited URL type | Share of 9,750 citations |
|---|---|
| Blog or article | 19.6% |
| Homepage | 10.0% |
| Review page | 2.7% |
| Docs | 2.6% |
| Pricing | 1.9% |
| Wikipedia | 1.6% |
Review platforms matter most for questions that name and compare brands. In our sample, the share of answers naming a brand varied by question type:
| Intent | Brand mentioned | Share of voice |
|---|---|---|
| Brand-specific | 100% | 64.3 |
| Comparison | 91.9% | 44.6 |
| Recommendation | 29.2% | 26.8 |
| Best-of | 26.6% | 22.7 |
| Problem | 5.7% | 7.7 |
Comparison and brand questions name brands most often, and those are the questions where review platforms tend to be useful sources. Whether you can see the citation depends on the engine:
| Engine | Brand mentioned | Answers with no citations |
|---|---|---|
| Gemini | 47.3% | 50.7% |
| OpenAI | 42.9% | 30.2% |
| Perplexity | 31.5% | 4.1% |
| AI Mode | 31.3% | 12.6% |
| Copilot | 26.0% | 47.0% |
| DeepSeek | 24.0% | 99.0% |
Our page-type classification doesn't separate review platforms as their own category, so we can't give a review-site share from our data. Our view: check your own prompt log to see how often review sites are cited in your category.
Limits. A convenience sample of mostly B2B software projects, run through engine APIs, which can behave differently from the consumer apps. Perplexity, Copilot and DeepSeek figures come from a single day (26 October 2026), so treat them as a snapshot, not a trend.
Worked example: Ledgerline revives a stale profile
Ledgerline is a fictional accounts-payable tool. Figures are illustrative.
Ledgerline runs ten category prompts and finds that a major review site is cited in several answers. Its profile there lists a product description from two years ago, an old pricing tier and a handful of reviews, the newest from last year. Competitor A and Competitor B have far more recent reviews.
The team claims the profile, updates the description to match its own site, dates the pricing and replaces old screenshots. It sets up an in-app prompt asking all active users, at the 60-day mark, to leave an honest review, with a direct link and no incentive.
Over the next quarter, recent reviews grow steadily, including a few critical ones about onboarding. Support replies to each publicly and briefly. Product uses the onboarding theme to prioritise a fix. Monthly prompt reruns track whether the profile is cited and whether the description in answers is current.
How do review sites end up in AI answers?
Assistants that search the web retrieve pages that answer the question well. For "best tool for X" and "X vs Y" questions, review platform category pages and comparison pages are strong candidates: they list many products, show ratings and summarise what users say. Our view: that means two things feed into answers: whether you're listed in the right category, and what your reviews and profile say about you. Both are partly in your control.
How do you choose categories?
Pick categories that match what your product actually does and what buyers search for. Being listed in a broad category where you're a weak fit can put you in comparisons you'll lose, and an answer drawn from that page may describe you as a lesser version of something else. Being missing from the category buyers use means you won't appear at all. Review your categories each year and after major product changes.
How do you build a steady review flow?
A burst of reviews followed by silence looks odd and soon goes stale. Build a steady flow instead. Add review requests to existing customer touchpoints: an onboarding completion email, a quarterly business review, a renewal confirmation or a customer success check-in. Track how many requests go out and how many reviews come back, so you can see when the flow drops.
If you offer an incentive, follow each site's rules and the law. US rules prohibit incentives conditioned on a particular sentiment (eCFR 465.4), and material connections must be disclosed (eCFR 255.5). G2 labels incentivised reviews as such (G2).
How do you use what reviews say?
Reviews describe your product in customers' words, and that language often matches how buyers phrase questions to assistants. Read them for the features and outcomes people mention, then check that your own pages use the same terms. If reviewers keep praising a capability your website barely mentions, that's a content gap. If they keep raising a complaint, that's a product or support issue worth fixing before it becomes the main thing answers say about you.
Who should own review sites?
Ownership is often split, with marketing owning the profile, customer success owning requests and support owning replies. Our view: name one owner for each platform who checks the profile monthly, coordinates requests and makes sure replies happen. Share a short monthly summary covering new reviews, rating trend, top themes and whether the platform was cited in your tracked prompts.
What if a review is false or abusive?
Honest negative reviews should stay, and G2 says it won't suppress negative reviews (G2). Reviews that break a site's rules are different: content about the wrong company, abusive language, private data or reviews from people who were never customers. Use the platform's reporting process for those, with evidence, and keep a record of what you reported and when. Don't flag reviews just because they're critical; that misuses the reporting tools and can damage your standing with the platform.
While a report is pending, reply publicly and calmly if the review could mislead readers. State the facts briefly without revealing private account details, and offer a direct contact.
How do you check how answers describe you?
Each month, rerun the same category and comparison prompts and note three things: whether a review site is cited, whether you're named and how you're described. Compare the description with your profile and recent reviews. If an answer repeats an old complaint you've since fixed, that's a sign the most visible reviews are out of date, and fresh reviews will matter more than any change to your own site.
Our view: treat the review profile as part of your product's public record. Keep it as accurate and current as your pricing page, because buyers and assistants may read it first.
How long does it take to see a change?
Expect months rather than weeks. New reviews need to accumulate, review pages need to be recrawled and answers need to draw on the updated pages. Track the trend in recent reviews and in your tracked prompts over at least a quarter before judging whether the work is paying off.
Common questions
Can I offer a gift card for reviews? Only if it doesn't depend on the rating and the review site's rules allow it. Disclose it and let the site label it.
Should I reply to every review? Reply to negative reviews and to positive ones where you have something useful to add.
Which review site matters most? The ones cited most often in your prompt log and mentioned by your buyers.
Related
- Getting into best-of listicles
- Affiliate and comparison sites
Sources
- US eCFR, 16 CFR 465.2; 16 CFR 465.4; 16 CFR 255.5
- G2, Community guidelines
All accessed 30 Oct 2026.
Questions
Can I offer gift cards for reviews?
Incentives conditioned on a positive or negative sentiment are banned under the FTC's consumer review rule. G2 labels incentivized reviews as incentivized.
Why do review sites matter for AI answers?
Our view: software best-of and comparison prompts often cite review platforms, so your rating, review volume and how reviewers describe you can shape the answer.
Can I ask only happy customers?
Our view: avoid it. G2 says it never asks for only positive reviews, and selective requests skew the picture buyers and AI engines see.