AI visibility platforms, like Radix or Promptwatch, have discovered G2 to be probably the most cited software program assessment platform.
Radix analyzed 10,000+ searches on ChatGPT, Perplexity, and Google’s AI Overviews and located G2 has “the very best affect for software-related queries” with 22.4%.
Moreover, PromptWatch discovered G2 to be probably the most seen B2B software program assessment platform throughout 100 million+ clicks, citations, and mentions from AI search like ChatGPT, tracked throughout 3,000+ web sites.
The info means that G2 has a significant impression on software program searches on LLMs (e.g., ChatGPT, Perplexity, Gemini, Claude, and so forth.). As an unbiased researcher, I wished to see if I might detect a relationship in our knowledge and validate the claims.
To get there, I analyzed 30,000 AI citations and share of voice (SoV) from Profound, which span throughout 500 software program classes on G2.
- Citations: A website, G2 on this case, is cited in an LLM with a hyperlink again to it.
- SoV: The variety of citations a website will get divided by the overall obtainable variety of citations
What the info revealed
Classes with extra G2 Opinions get extra AI citations and a better SoV. When ChatGPT, Perplexity, or Claude must suggest software program, they cite G2 among the many first. Right here’s what I discovered.
1. Extra evaluations are linked with extra citations
The info reveals a small however dependable relationship between LLM citations and G2 software program evaluations (regression coefficient: 0.097, 95%, CI: 0.004 to 0.191, R-squared: 0.009).
Classes with 10% extra evaluations have 2% extra citations. That is after eradicating outliers, controlling for class measurement, and utilizing conservative statistical strategies. The connection is clear.
2. Classes with extra evaluations have a better SoV
I additionally discovered a small however dependable relationship between G2 Opinions and SoV (regression coefficient: 0.113, 95% CI: 0.016 to 0.210, R-squared: 0.012).
If evaluations rise by 10%, SoV will increase by roughly 0.2-2.0%.
What does all this imply?
The variety of citations and the SoV are primarily decided by elements exterior this evaluation: model authority, content material high quality, mannequin coaching knowledge, natural search visibility, and cross-web mentions. Opinions clarify lower than 2% of the variance, which implies they seem to be a small piece of a bigger puzzle.
However why G2 particularly?
AI fashions face a verification drawback. They want scalable, structured indicators to evaluate software program high quality. G2 offers three attributes that matter: verified patrons (reduces noise), standardized schema (machine-readable), and assessment velocity (present market exercise). With greater than 3 million verified evaluations and the very best natural visitors in software program classes, G2 presents sign density that different platforms cannot match.
A ten% improve in evaluations correlating with a 2% improve in citations sounds modest. However think about the baseline: most classes obtain restricted AI citations. A 2% raise on a low base could also be virtually negligible. Nonetheless, in high-volume classes the place a whole lot of citations happen month-to-month, a 2% shift might meaningfully alter aggressive positioning. In winner-take-most classes the place the highest three outcomes seize disproportionate consideration, small quotation benefits compound.
What issues is not your uncooked assessment rely, however your place relative to opponents in your class. A class with 500 evaluations the place you maintain 200 positions has a special impression than a class with 5,000 evaluations the place you maintain 200.
Why this issues now
The shopping for journey is remodeling. In G2’s August 2025 survey of 1,000+ B2B software program patrons, 87% reported that AI chatbots are altering how they analysis merchandise. Half now begin their shopping for journey in an AI chatbot as a substitute of Google — a 71% bounce in simply 4 months.
The true disruption is in shortlist creation. AI chat is now the highest supply patrons use to construct software program shortlists — forward of assessment websites, vendor web sites, and salespeople. They’re one-shotting choices that used to take hours. A immediate like “give me three CRM options for a hospital that work on iPads” immediately creates a shortlist.
Once we requested patrons which sources they belief to analysis software program options, AI chat ranked first. Above vendor web sites. Above salespeople.
When a procurement director asks Claude to share the “greatest CRM for 50-person groups” right now, they’re getting a synthesized reply from sources the AI mannequin trusts. G2 is a kind of sources. The software program business treats G2 as a buyer success field to test. The info suggests it is grow to be a distribution channel — not the one one, however a measurable one.
What actions you may take primarily based on these analysis insights
One of the simplest ways to use the info is to spend money on evaluations and G2 Profiles:
- Write a profile description (+250 characters) that clearly highlights your distinctive positioning and worth props.
- Add detailed pricing data to your G2 Profile.
- Drive extra evaluations to your G2 Profile, equivalent to by linking to your G2 Profile web page from different channels.
- Provoke and have interaction with discussions about your product and market.
Methodology
To conduct this analysis, we used the next methodology and method:
We took 500 random G2 classes and assessed:
- Accredited evaluations within the final 12 months
- Citations and SoV within the final 4 weeks
We eliminated rows the place:
- Citations within the final 4 weeks are underneath 10
- Visibility rating is 0 p.c
- Accredited evaluations within the final 12 months are beneath 100 authorized evaluations
- Opinions have been important outliers
For the result, the median was unchanged, which helps that pruning didn’t bias the middle of the distribution.
We analyzed the regression coefficient, 95% confidence interval, pattern measurement, and R-squared.
Limitations embody the next:
- Cross-sectional design limits causal inference: This evaluation examines associations at a single cut-off date (evaluations from the prior 12 months, citations from a 4-week window). We can’t distinguish whether or not evaluations drive citations, citations drive evaluations, or each are collectively decided by unobserved elements equivalent to model power or market positioning. Time-series or panel knowledge could be required to ascertain temporal priority.
- Omitted variable bias: The low R² values (0.009-0.012) point out that assessment quantity explains lower than 2% of the variation in citations and SoV. The remaining 98% is attributable to elements exterior the mannequin, together with model authority, content material high quality, mannequin coaching knowledge, natural search visibility, and market maturity. With out controls for these confounders, our coefficients could also be biased.
- Aggregation on the class degree: We analyze classes fairly than particular person merchandise, which obscures within-category heterogeneity. Classes with equivalent assessment counts however totally different distributions throughout merchandise could exhibit totally different AI quotation patterns. Product-level evaluation would offer extra granular insights however would require totally different knowledge assortment.
- Pattern restrictions have an effect on generalizability: We excluded classes with fewer than 100 evaluations, fewer than 10 citations, or excessive outlier values. Whereas this improves statistical properties, it limits our capability to generalize to small classes, rising markets, or merchandise with atypical assessment patterns. The pruning maintained the median, suggesting central tendency is preserved, however tail conduct stays unexamined.
- Single platform evaluation: This research focuses solely on G2. Different assessment platforms (like Capterra, TrustRadius, and so forth.) and data sources (like Reddit and business blogs) additionally affect AI mannequin outputs. G2’s dominance in software program classes could not prolong to different verticals, and multi-platform results stay unquantified.
- Mannequin specification assumptions: We use log transformations to handle skewness and assume linear relationships on the reworked scale. Various practical types (like polynomial and interplay phrases) or modeling approaches (equivalent to generalized linear fashions and quantile regression) might reveal non-linearities or heterogeneous results throughout the distribution.
- Measurement issues: Citations and SoV rely upon Profound’s monitoring methodology and question choice. Completely different monitoring instruments, question units, or AI fashions could produce totally different quotation patterns. Evaluate counts rely upon G2’s verification course of, which can introduce choice results.
These limitations counsel our estimates must be interpreted as suggestive associations fairly than causal results. The connection between evaluations and AI citations is statistically detectable however operates inside a posh system of a number of affect elements.
