Why G2 and Capterra ratings disagree — and how to read them
The same product routinely carries different scores on different review platforms. The reasons are structural, and once you know them the numbers become useful again — just not for the thing most people use them for.
The disagreement is not an error
Look up any established B2B product on two review platforms and you will usually find two different averages. People treat this as a sign that one platform is wrong, or that reviews are worthless. Neither follows.
Two averages differ when they are computed over two different populations. That is not a flaw in either number; it is what an average is. The useful question is not "which score is right" but "who is in each sample, and does that resemble me".
I want to be clear about the limits of this piece up front: I have not audited either platform's moderation, verification or weighting, and I am not going to characterise either company's practices. What follows is reasoning about how review populations form in general, which is enough to read the numbers more carefully.
Where review populations come from
Reviews are not a random sample of users. They are a sample of users who were asked, at a moment when they were willing to answer. Three things shape that sample, and all three vary by platform.
First, solicitation. Most B2B reviews exist because someone asked for them — a vendor campaign, an in-app prompt, an incentive. A vendor that runs a campaign on one platform and not another will have systematically different volumes and, plausibly, different sentiment, because the population of "customers who responded to our campaign" is not the population of "our customers".
Second, timing. A review written during onboarding measures a different thing from one written in year three. Onboarding reviews capture first impressions and setup friction; long-tenure reviews capture whether the thing held up. A platform whose reviews skew recent is measuring a different phase of the relationship.
Third, who each platform attracts. Different platforms have different audiences by company size, region and buying role, and a tool that suits a fifty-person company and frustrates a five-person one will score differently depending on which is over-represented.
Why volume matters more than the average
A 4.9 from 30 reviews and a 4.4 from 12,000 are not comparable quantities, and treating them as one is the single most common error in reading these scores.
Small samples are volatile: a handful of enthusiastic early adopters, or one campaign, moves the number a long way. Large samples are stable but slow — they include years of history, so a product that has genuinely improved will carry its old reviews for a long time, and a product that has decayed will coast on them.
This is why this site holds products with fewer than 500 reviews below better-reviewed ones in any list ordered by rating, whatever the average. It is a blunt rule and it is better than the alternative, which is letting a launch-week 5.0 sit above an established 4.4 and implying something false to anyone scanning the page.
The practical version: read the count first, then the average, and if the count is small treat the average as an anecdote rather than a measurement.
What the numbers are actually good for
Aggregate scores are poor at ranking products and good at three other things.
They are good at flagging outliers. A product sitting at 3.1 across a thousand reviews when its category averages 4.4 is telling you something real. Most B2B software clusters between about 4.2 and 4.7, which is itself informative: within that band, differences are mostly noise, and a 4.6 is not meaningfully better than a 4.4.
They are good as an index into the written reviews, which is where the actual information is. Filter to reviewers whose company size and industry resemble yours, sort by most recent, and read the three-star reviews. Five-star reviews tell you what the product does; one-star reviews are often about billing or support incidents; three-star reviews are where people describe the trade-off they actually made.
And they are good for spotting consistent complaints. If four unrelated reviewers over eighteen months mention the same limitation, that is a finding. A single mention is a person having a bad week.
How this site handles it
Since this is a comparison site, the fair thing is to state our own position. We do not average platform scores into a house rating. A single blended number out of five looks authoritative and, given everything above, means very little — the populations are not comparable, so the average of the averages is not a quantity.
Each platform's score is shown separately with its name attached, or marked unverified where we have no source for it. And one specific mislabelling is worth naming because this site made it: ProductHunt reports upvotes, not reviews. Upvotes measure launch-day attention, not satisfaction after six months of use, and displaying them under a "Reviews" column — as this site did until recently — implies a kind of evidence that does not exist.
If you only take four things
- Two platforms disagree because they average over different populations. Ask who is in the sample, not which number is right.
- Read the review count before the average. Below a few hundred reviews, treat the average as an anecdote.
- Most B2B software sits between roughly 4.2 and 4.7. Inside that band, differences are mostly noise; outside it, pay attention.
- The three-star reviews are where the real trade-offs are described. Filter to companies your size and sort by recent.
What this piece does not establish
- This piece reasons about how review populations form in general. It is not an audit of any platform's verification, moderation or weighting, and it does not characterise any company's practices.
- The 4.2–4.7 clustering is a general observation about B2B software ratings, not a figure derived from a dataset we have published.
- Ratings shown elsewhere on this site are third-party figures, attributed where sourced and marked unverified where not. See the methodology page for the current state.
Who wrote this
How figures on this site are sourced and labelled is set out in the methodology. If something here is wrong, send a correction.