AI visibility platforms, like Radix or Promptwatch, have discovered G2 to be probably 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 best affect for software-related queries” with 22.4%.
Moreover, PromptWatch discovered G2 to be probably 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 information means that G2 has a significant affect on software program searches on LLMs (e.g., ChatGPT, Perplexity, Gemini, Claude, and many others.). As an unbiased researcher, I needed 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 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 whole obtainable variety of citations
What the info revealed
Classes with extra G2 Critiques get extra AI citations and the next SoV. When ChatGPT, Perplexity, or Claude must 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 information reveals 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 the next SoV
I additionally discovered a small however dependable relationship between G2 Critiques 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 components outdoors this evaluation: model authority, content material high quality, mannequin coaching knowledge, natural search visibility, and cross-web mentions. Critiques clarify lower than 2% of the variance, which implies they are a small piece of a bigger puzzle.
However why G2 particularly?
AI fashions face a verification drawback. They want scalable, structured alerts to evaluate software program high quality. G2 gives 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 best natural visitors in software program classes, G2 presents sign density that different platforms cannot match.
A ten% improve in critiques correlating with a 2% improve in citations sounds modest. However contemplate the baseline: most classes obtain restricted AI citations. A 2% elevate on a low base could also be virtually negligible. Nevertheless, 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 rely, however your place relative to opponents in your class. A class with 500 critiques the place you maintain 200 positions has a distinct affect 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% bounce 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 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.
After 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 “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 trade treats G2 as a buyer success field to examine. The information suggests it is turn out to be a distribution channel — not the one one, however a measurable one.
What actions you possibly can take primarily based on these analysis insights
One of the best ways to use the info 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 info to your G2 Profile.
- Drive extra critiques to your G2 Profile, resembling 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 strategy:
We took 500 random G2 classes and assessed:
- Accredited 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 underneath 10
- Visibility rating is 0 %
- Accredited critiques within the final 12 months are under 100 permitted critiques
- Critiques had 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 embrace the next:
- Cross-sectional design limits causal inference: This evaluation examines associations at a single time limit (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 components resembling model power or market positioning. Time-series or panel knowledge could be required to determine 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 components outdoors 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 stage: We analyze classes quite than particular person merchandise, which obscures within-category heterogeneity. Classes with an identical evaluate counts however completely different distributions throughout merchandise might exhibit completely different AI quotation patterns. Product-level evaluation would supply extra granular insights however would require completely different knowledge 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 capacity 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 solely on G2. Different evaluate platforms (like Capterra, TrustRadius, and many others.) and data sources (like Reddit and trade blogs) additionally affect AI mannequin outputs. G2’s dominance in software program classes might not lengthen 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. Different practical types (like polynomial and interplay phrases) or modeling approaches (resembling generalized linear fashions and quantile regression) might reveal non-linearities or heterogeneous results throughout the distribution.
- Measurement concerns: Citations and SoV rely upon Profound’s monitoring methodology and question choice. Totally different monitoring instruments, question units, or AI fashions might produce completely different quotation patterns. Assessment counts rely upon G2’s verification course of, which can introduce choice results.
These limitations recommend our estimates needs to 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 components.
