So far we've published several reports that touched on some data points about G2—in the What Does It Take to Close report, we found that G2 touchpoints speed up deals; and in the State of Attribution and Revenue report, we found that review websites bring better ACVs. However, we haven't had a dedicated report to understand the G2 metrics until now.
G2 is a pretty interesting topic, and there are many strong opinions about it. Some argue that G2 has disrupted the traditional review industry, making it possible for companies to bypass $100K Forrester or Gartner reports. Others claim that B2B buyers shouldn't trust G2 because many reviews come from handpicked customers. Personally, I’m more inclined towards the former argument; I think G2 has made a real impact—it's changed the way companies approach reviews and significantly altered the traditional review game.
And yes, companies obviously prioritize their best customers to review them on G2, but let's be real—analyst firms used to do the same thing, and it often resulted in 400% ROI claims - at least we can also see the negative reviews on G2 while we can’t in those analyst reports.
I agree that, in an ideal world, we should rely on random samples of existing customers and communities for insights. But we need to be realistic. Are there communities for every software industry? Is every buyer a member of these communities? The reality is that when buyers search for "Company X reviews," G2 is often the top result. So, what happens when someone finds you through G2? That's what we set out to uncover in this report.
Before we get into the methodology, I want to clarify a few things. We analyzed all conversions that happened in Q1 2024 and filtered by companies that had at least one "G2" response in their self-reported attribution answers. We figured that if a company didn't receive a single G2 response in the last 90 days, they probably don't prioritize G2, or it's not relevant to their industry. By excluding companies without at least one G2 response, we ensured we were comparing apples to apples. This approach also eliminated companies without self-reported attribution forms from our dataset.
This goes without saying but we obviously removed all the invalid self-reported answers such as “djks”, “test”, etc.
MQL: High-intent demo, pricing page, contact us submissions. Basically, every hand-raiser on the website. Ebook form submissions, lead-gen stuff, webinar registrations were not counted as MQLs. In the first Labs report, some people got a bit heated with us for saying MQLs, but I won’t be changing this simply because I like the sound of it.
SQL: Pipeline created; SQO, Opportunity, etc. Every company has different definitions, but we unified this on the backend and used the SQO definition for when the pipeline is actually created.
Pipeline: Total deal value of SQLs
ACV: Total new business revenue added divided by the number of closed-won deals (also known as Average Contract Value, Deal Size, Selling Price, Deal Value)
CW: Closed won deals, net new business, new revenue.
Revenue: Total value of CWs
What we analyzed:
-Over 20K self-reported form answers from 51 B2B SaaS companies.
-From $10M ARR to $300M ARR; average ACV from $10K to $80K
-65% NAM, 30% UK, 5% Europe
-From January 1st, 2024 to March 31, 2024 for the MQL data, to April 28th 2024 for pipeline and revenue data.
In the State of Revenue report, we discovered that 1.8% of self-reported attribution answers were from review websites, specifically from G2 and Capterra. This report was based on 8.5K responses from 36 B2B SaaS companies between February 1st, 2023, and December 31st, 2023. Out of these, over 100 responses explicitly mentioned G2. Therefore, our 2023 data indicated that G2 mentions constituted about 1.2% of total submissions.
So the first thing we wanted to understand was the current state of G2; what is the total ratio of answers containing G2? Are we seeing an increase or decrease compared to last year's average?
With a significant increase in both the number of companies included and the total number of answers analyzed compared to the last report, we found out that 1.35% of the 20K form submissions included G2 which equals to 270 MQLs. This indicates that the total ratio of answers containing G2 has increased by around 11% compared to the previous year—not too bad.
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What about after the form submission?
When we look at the MQL to SQL conversion rates, we see that MQLs including G2 have an average 22% better conversion rate to SQL than those that don't include G2 (these include all form submissions with valid self-reported answers).
On the source level, two interesting data points:
- Answers containing Linkedin had an 8% better conversion rate than those containing G2.
- Answers containing Facebook had a 35% lower conversion rate than those containing G2.
Furthermore, when we look at the time between stages, we find that MQLs containing G2 become SQLs 17% faster. This means that if your MQL to SQL duration is typically 10 days, those containing G2 will likely convert to an SQL in 8.3 days. I don't think we need to overthink this—people who found you through G2 have likely already read your reviews and compared you to your competitors, so they move faster.
On the source level, two additional interesting data points:
- Answers containing Google had a 14% faster MQL to SQL duration.
- Answers containing Reddit had a 24% slower MQL to SQL duration.
When we analyzed the 2023 data, our dataset showed that although review sites made up only 1.8% of conversions, these conversions accounted for 2.66% of revenue, indicating that the ACV and the close rate of these deals were better. However, we didn’t analyze this phenomenon at the ACV level in that report.
Hence, for this report, we decided to start with the ACV. On the ACV front, we’re observing a slightly different picture compared to the top of funnel metrics—according to the Q1 2024 data, G2 deals actually have a 14.5% lower ACV than non-G2 deals.
One of my assumptions is that this may be related to why we’re seeing faster sales cycles. When your audience finds you through G2, they most likely find your competitors as well and learn about both you and your competitors. If they then come to your website and become an opportunity, it means they are already familiar with your product and move faster; but this might also mean that they are aware of your potential weaknesses or those of your competitors, thus leveraging this during sales calls to obtain more discounts. However, a caveat here: this assumption may oversimplify the sales process because your audience might also check your G2 once they become an opportunity. It’s not like they can only check your G2 profile before submitting the demo form.
Another assumption could be related to company sizes—perhaps prospects who find you through G2 fall under the SMB and mid market segments, whereas enterprise prospects find you through other channels. This assumption is an easy one to prove or refute though.
So, we decided to analyze the company size split between conversions with G2 and those without G2.
TLDR: There’s no correlation. Upon looking at the G2 and non-G2 conversions, we’re seeing both massive enterprises and SMBs find products through G2; similarly for the non-G2 conversions, there are massive companies as well as SMBs. We don’t have any data indicating that conversions coming from G2 are only SMBs or mid market.
Unlike the ACV part, we’re seeing very positive metrics when it comes to conversion rates and sales cycles. According to Q1-2024 data, opportunities with G2 have a 21% better close rate compared to those without G2.
This is interesting because, in the Q1-2024 benchmark report, we found that the SQL:CW conversion rate in Q1-2024 decreased by 43% compared to Q4-2023. This decrease in conversion rate occurred across all types of deals—from outbound to inbound, and from organic to paid—and this was primarily because Q4 is the quarter where the close rate is always the highest. However, deals with G2 experienced the smallest decrease in their conversion rates in the first quarter of 2024.
We’re seeing a similar trend in the sales cycles. Our dataset shows that in the first quarter of 2024, deals that include G2 close 26.5% faster compared to those without G2. This means that if your average deal cycle is 100 days, the average deal cycle for G2 deals would be approximately 73 days—nearly a month less.
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