Do Extra G2 Evaluations Imply Extra AI Visibility? Insights from 30k Citations

AI visibility platforms, like Radix or Promptwatch, have discovered G2 to be essentially the most cited software program evaluate 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 essentially the most seen B2B software program evaluate 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 impartial researcher, I needed to see if I might detect a relationship in our information 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 web site, G2 on this case, is cited in an LLM with a hyperlink again to it.
SoV: The variety of citations a web site will get divided by the entire accessible variety of citations

What the information revealed

Classes with extra G2 Evaluations get extra AI citations and a better SoV. When ChatGPT, Perplexity, or Claude have to advocate software program, they cite G2 among the many first. Right here’s what I discovered.

1. Extra critiques are linked with extra citations

The info exhibits a small however dependable relationship between LLM citations and G2 software program critiques (regression coefficient: 0.097, 95%, CI: 0.004 to 0.191, R-squared: 0.009).

Classes with 10% extra critiques 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 critiques have a better SoV

I additionally discovered a small however dependable relationship between G2 Evaluations and SoV (regression coefficient: 0.113, 95% CI: 0.016 to 0.210, R-squared: 0.012).

If critiques 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 outdoors this evaluation: model authority, content material high quality, mannequin coaching information, natural search visibility, and cross-web mentions. Evaluations clarify lower than 2% of the variance, which suggests 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 supplies three attributes that matter: verified consumers (reduces noise), standardized schema (machine-readable), and evaluate velocity (present market exercise). With greater than 3 million verified critiques and the very best natural site visitors in software program classes, G2 affords sign density that different platforms cannot match.

A ten% improve in critiques correlating with a 2% improve in citations sounds modest. However think about the baseline: most classes obtain restricted AI citations. A 2% elevate 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 evaluate depend, however your place relative to rivals in your class. A class with 500 critiques the place you maintain 200 positions has a distinct impression than a class with 5,000 critiques 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 consumers, 87% reported that AI chatbots are altering how they analysis merchandise. Half now begin their shopping for journey in an AI chatbot as an alternative of Google — a 71% leap in simply 4 months.

The actual disruption is in shortlist creation. AI chat is now the highest supply consumers use to construct software program shortlists — forward of evaluate websites, vendor web sites, and salespeople. They’re one-shotting selections 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 consumers 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 “finest CRM for 50-person groups” as we speak, they’re getting a synthesized reply from sources the AI mannequin trusts. G2 is a type of sources. The software program business treats G2 as a buyer success field to examine. The info suggests it is turn into a distribution channel — not the one one, however a measurable one.

What actions you possibly can take primarily based on these analysis insights

The easiest way to use the information is to spend money on critiques 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 critiques to your G2 Profile, similar to by linking to your G2 Profile web page from different channels.
Provoke and interact 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:

Authorised critiques 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 below 10
Visibility rating is 0 p.c
Authorised critiques within the final 12 months are under 100 accepted critiques
Evaluations had been vital outliers

For the end 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 embrace the next:

Cross-sectional design limits causal inference: This evaluation examines associations at a single cut-off date (critiques from the prior 12 months, citations from a 4-week window). We can not distinguish whether or not critiques drive citations, citations drive critiques, or each are collectively decided by unobserved elements similar to model energy or market positioning. Time-series or panel information can be required to ascertain temporal priority.
Omitted variable bias: The low R² values (0.009-0.012) point out that evaluate quantity explains lower than 2% of the variation in citations and SoV. The remaining 98% is attributable to elements outdoors the mannequin, together with model authority, content material high quality, mannequin coaching information, 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 quite than particular person merchandise, which obscures within-category heterogeneity. Classes with similar evaluate counts however totally different distributions throughout merchandise could exhibit totally different AI quotation patterns. Product-level evaluation would supply extra granular insights however would require totally different information assortment.
Pattern restrictions have an effect on generalizability: We excluded classes with fewer than 100 critiques, fewer than 10 citations, or excessive outlier values. Whereas this improves statistical properties, it limits our means to generalize to small classes, rising markets, or merchandise with atypical evaluate patterns. The pruning maintained the median, suggesting central tendency is preserved, however tail conduct stays unexamined.
Single platform evaluation: This research focuses completely on G2. Different evaluate platforms (like Capterra, TrustRadius, and so forth.) and knowledge sources (like Reddit and business blogs) additionally affect AI mannequin outputs. G2’s dominance in software program classes could not lengthen to different verticals, and multi-platform results stay unquantified.
Mannequin specification assumptions: We use log transformations to deal with skewness and assume linear relationships on the remodeled scale. Various practical varieties (like polynomial and interplay phrases) or modeling approaches (similar to generalized linear fashions and quantile regression) might reveal non-linearities or heterogeneous results throughout the distribution.
Measurement concerns: Citations and SoV depend upon Profound’s monitoring methodology and question choice. Completely different monitoring instruments, question units, or AI fashions could produce totally different quotation patterns. Overview counts depend upon G2’s verification course of, which can introduce choice results.

These limitations counsel our estimates must be interpreted as suggestive associations quite than causal results. The connection between critiques and AI citations is statistically detectable however operates inside a fancy system of a number of affect elements.