Why Is It Essential to Analyze G2 Reviews for Software Companies?

G2 reviews are one of the main places where software buyers check what a product is like to live with. Founded in 2012 as G2 Crowd, G2 describes itself as a B2B software and services review marketplace, and it hosts more than 3 million verified reviews across over 2,000 categories. In early 2026, G2 agreed to acquire Capterra, Software Advice and GetApp from Gartner, and Capterra now describes itself as part of the G2 ecosystem.

For a software company, these reviews are read at the shortlist stage. A buyer comparing four security tools looks at the ratings, the Grid placement and then the reviews written by people in a similar role at a similar company.

Most software teams read their G2 reviews to track their rating. Analyzed the way a research team would analyze interviews, the same reviews show how the product works for each role and company size, how it compares with its rivals, and where the category is moving. This article explains how that analysis works and how teams that already hold this feedback can turn it into enterprise-grade market research.

Who Is Speaking in a G2 Review

What sets G2 reviews apart as data is that each one is tied to a known professional profile. G2 places reviewers in company-size segments: Small Business (50 or fewer employees), Mid-Market (51 to 1,000) and Enterprise (1,001 or more). Even when a reviewer chooses to stay anonymous, the review still shows their job category and industry. G2's review pages can be filtered by company size, role and industry, and G2 publishes separate Grids for each segment.

A G2 review therefore answers a question most feedback cannot: who is saying this. A complaint about setup reads differently when it comes from an IT administrator at a 5,000-person bank than from the founder of a twelve-person agency. The product is the same; the experience is not.

How reviews are collected matters for analysis too. Reviewers sign in with LinkedIn, a business email or a personal email that G2 can validate, answer questions about their use case, and pass an automatic filter and a manual moderation step before the review is published. A reviewer can upload a screenshot of the product in use, which adds a Current User label. G2 sometimes offers a nominal incentive, capped at $100, as thanks for an approved review; eligibility never depends on whether the review is positive or negative, and these reviews are labeled Incentivized. Reviews from business partners and guest users carry their own labels and don't count toward scores. For research, the practical step is to keep these labels as columns, so that groups of reviews can be compared rather than blended.

Why a Category Average Says Little About Your Buyers

A rating of 4.5 averages people who use the product in very different ways. A small business may love a tool for its simplicity while an enterprise customer gives the same score despite struggling with administration. The average hides both stories.

Fixed categories do not fix this. An analysis that sorts every review into Ease of Use, Support, Value and Features will put "SSO setup took our team weeks" and "the dashboard is easy to read" under related headings, though they come from different people and call for different decisions. Kimola finds the themes in the feedback itself, without predefined labels, and names each one in the terms customers use. Each theme also gets its own sentiment, so a review that praises reporting but criticizes the contract terms counts as positive for one and negative for the other.

Ranking by volume adds another distortion. When themes are ordered by how often they appear, the obvious topics always come first. Kimola scores themes with deterministic methods that weigh the share of positive or negative mentions against the number of comments. A theme with 5 comments, all positive, looks perfect at 100%, but 5 comments are thin evidence. A theme with 50 comments, 43 of them positive (86%), is stronger, and Kimola ranks it higher.

Research Layers That Separate Roles and Segments

Themes and sentiment show what reviewers discuss. Dimensions show the context around it. A Dimension is a research question Kimola asks of every review. The answers are grouped into clusters built from the data, with counts, shares and quotes in customers' own words, and each cluster can be crossed with the columns in the dataset and with other Dimensions. For G2 reviews, the most revealing questions are about who is having which experience.

Personas

Personas asks who is experiencing this. G2 already gives a job title; Personas adds behavior. Kimola builds personas from patterns in what customers write, not from demographics such as age, location or job title. Two marketing managers with the same title can use a product in completely different ways, and the review text shows how.

Take a marketing automation platform. One persona writes about reporting flexibility, attribution and what they can show leadership at the end of the quarter. Another writes about how fast the editor loads, whether the templates save time, and how many clicks it takes to launch a campaign. Both may hold a marketing title. The first judges the product by its outputs; the second by its daily friction.

An HR and payroll tool shows a similar split across roles. The HR specialist who runs payroll every two weeks writes about accuracy, compliance updates and how errors are corrected. The employee who only uses it to request time off writes about the mobile app and whether approvals arrive quickly. Both shape the rating, but they want different things.

Pain Points

Pain Points asks what goes wrong or creates difficulty. Kimola presents the answers in a structured, prioritized form with real quotes, and each pain point can be broken down by persona with its own sentiment.

A cybersecurity product might find that its most negative cluster is not about missed threats but about alert volume: security analysts describe spending their shifts sorting alerts that turn out to be harmless. A developer tool might find that its pain points sit around documentation gaps and build times rather than the features its team spends most of its time on. What matters is which roles carry the pain: a problem that hurts the daily user but not the buyer can stay invisible until renewal.

Experience Stages

Experience Stages shows where in the customer journey an experience happens. Kimola's customer journey framework covers Discovery, Purchase Experience, Usage & Support, and Loyalty & Advocacy.

G2 reviews often come from customers who have used a product for a year or more, which makes the later stages visible. A customer support platform might see warm reviews about the sales process and onboarding, followed by a sharp drop when reviewers reach renewal: a price increase, a minimum seat count, or a contract that cannot be adjusted when the team shrinks. An HR and payroll tool might find its weakest moment in the first payroll run after go-live. The stage tells a company whether the fix belongs to product, customer success or sales.

Social Context

Social Context reveals whether the experience is lived alone or together with others. For business software, it usually separates tools used by one person from tools a team depends on.

A code editor extension used by an individual developer is judged on personal speed and focus. A platform used by a 200-person engineering organization is judged on permissions, shared settings and audit trails. A project management tool used across a department is judged on visibility between teams. If a company reads only the averages, it can miss that its collaboration features decide the outcome for its largest accounts.

Co-Occurrences

Co-Occurrences shows what customers mention together. In software reviews, this often means the other tools in the stack. A sales engagement tool might find that its reviews mention one CRM far more than any other, and that the reviews mentioning a particular data warehouse are noticeably more negative. That is direct evidence about which integrations to prioritize.

Substitutions

Substitutions asks what customers used before, switched from or compare the product against. On G2, this layer is most useful for mapping a category. Analyzed across five competing products, it shows who wins customers from whom, and why.

Reading the Layers Together

The layers become most useful when they are combined. Imagine a security platform whose small business reviews praise how quickly it was set up. In its enterprise reviews, the Dimensions might show a different picture: the IT administrator persona, rolling the product out across several teams at once, reports a cluster of pain points around single sign-on and role-based permissions, concentrated in the first months after purchase. The company now knows where, for whom and why the same product turns hard.

Signals Across Products, Roles and Company Sizes

A Signal is a statistically tested finding. Kimola crosses every theme and Dimension with the columns in the dataset and with time, then tests whether something is mentioned more, or more negatively, in one group than in the rest, or more often in recent periods than before. Only statistically significant findings become Signals, and they are ranked so the ones that deserve attention come first.

G2 reviews come with exactly the columns that make this possible: company segment, role, industry and date. Suppose complaints about setup complexity sit in the middle of the overall ranking for a cybersecurity product. Crossed with company segment, they may stand out among enterprise reviewers; crossed with role, they may concentrate among IT administrators. Kimola calls this kind of finding a Difference: a theme or Dimension that stands out in one group compared with the rest.

Add a product name column, and the same method works across a category. If a company analyzes its own reviews alongside those of four competitors, Signals can show that a pain point shared by the whole category is far more common for one rival, or that one product is consistently more praised in a specific industry. With a date column, Signals also show Trends, such as pricing complaints rising in recent periods after a change in packaging.

A Signal does not come from frequency alone. It appears when a pattern is noticeably more or less common in one group than in its comparison group, or when it changes over time. Each Signal also carries a consistency label that shows whether the pattern holds across subgroups or varies. For a closer look at how small businesses choose and switch software, see our article on analyzing Capterra reviews.

From Competitive Findings to Action Plans

An Action Plan is a set of up to 12 actions that Kimola writes from a Report. Kimola forms hypotheses from the findings, tests each one against the reviews with counts and sample comments, and drops any action the evidence does not support. Every figure and quote in the remaining actions is checked against the feedback, and the actions are scored and ranked, each with a confidence level.

Public G2 reviews make competitive Action Plans possible. When creating a plan, a team chooses where it stands (its own offering, a rival, or the market as a whole) and a goal such as Competitive Strategy. Built from a category's reviews, a plan can point to the segment a rival serves poorly, or the integration buyers keep asking for. Each action card explains why, who, when and where, the risk, and how to see that it worked.

How to Analyze G2 Reviews in Kimola

Software companies that already hold their review data have the strongest starting point. If your team already collects G2 reviews, for your own product and your competitors', for example in a spreadsheet or through your review management tools, you can upload them as a file in .xls, .xlsx, .csv or .tsv format, up to 30 MB. Choose the column that holds the review text and, if you have one, a date column.

Keep the other columns as well: product name, reviewer role, company segment, industry, rating and the review labels. Kimola reads each column's name and values and uses it to enrich the Report, as a filter, a comparison axis and an input for Signals. Review text alone still shows themes and Dimensions; the extra columns reveal how they differ between segments, roles and competitors. One file with five products and a product name column becomes a category study rather than five separate reports.

G2 is not supported as a link. To collect reviews directly from G2 pages, for your own product or a competitor's, use Kimola's browser extension, the Airset Generator. It saves the reviews on a page into a dataset called an Airset, from which a Report can be created. Our guide on how to scrape and analyze G2 reviews walks through the steps.

For larger organizations, the same analysis can serve every team. A custom Model classifies feedback with the organization's own themes, so product, marketing and customer success read G2 reviews in a shared language. Spaces organize feedback by product line, brand or customer segment, and enterprise accounts are set up with a dedicated Account Manager.

Trying It With Your Own Reviews

Every new Kimola account starts on the free Starter package, with no credit card required, so you can test this approach on your own G2 reviews or a competitor's before choosing a plan. A Report of about 100 reviews is usually ready in two to three minutes. Create a free account to start with your own data, or request a demo to discuss a category-wide or enterprise setup with our team.