I believe the litmus test of a good B2B SaaS marketer is how they approach Google Ads. Do they understand the concept of revenue, or are they just trying to maximize leads? If the latter, one can easily decrease the cost per lead by 50% and double the number of leads on Google Ads by sacrificing the quality. For this reason, Google Ads can be a verydangerous platform in the wrong hands.
Whenever I speak with fellow marketing leaders and customers, I often hear comments like “Oh, this person in the company said we can improve our conversions by spending more on Google” or “Is Google getting more expensive and less profitable? We can’t convert properly anymore.” Google is indeed becoming more expensive, as are all other ad channels - because every day new companies start running ads and all these platforms operate on auction models. The crucial question we need to address is whether these channels are becoming less efficient and if they are still contributing to revenue.
In this report, we analyzed the Google Ads data from the past 27 months to determine what has been happening with Google Ads and whether it is actually a profitable channel.
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
Attribution Model: Position-based because it offers a balanced view on the importance of each touchpoint in a customer's journey towards a conversion. This method assigns more credit to the first and last interactions and distributes the remaining 20% among the middle interactions. Especially since this report was focused on Google Ads, our main goal was to uncover the data that’s not the last touch.
What we analyzed:
- More than $100M Google Ads spent in the last 27 months from over 50 B2B SaaS companies.
Sample Description:
-From $5M ARR to $650M ARR; average ACV from $10K to $100K
-At least $5K monthly Google Ads spend per company
-65% NAM, 30% UK, 5% Europe
-From January 4th, 2022 to March 31, 2024 for spend and MQL data, to April 22nd 2024 for pipeline and revenue data.
In the last 27 months, the average budget allocation for Google was 49.22%. When we examine this budget allocation year by year, we observe a negative trend. In 2022, 55% of the budget was allocated to Google Ads; in 2023, this decreased to 47.72%, and in the first quarter of 2024, the allocation further decreased to 39.85%.
Although we can definitely see a change in budget allocation, I don’t believe this shift necessarily indicates a general trend across all software companies. On the contrary, I think this shift is unique to our own dataset because as soon as our customers start using HockeyStack, they begin to measure Google Ads ROI more accurately; hence, they understand what is working to what extent and whether those last-touch Google conversions are genuinely attributable to Google. Note: I always try to remain neutral in these reports, and by no means do I want this to sound like a promotion for HockeyStack. If other companies who are also able to demonstrate the impact of Google Ads run this analysis on their own datasets, we might see similar results—hopefully.
Let's start with the straightforward ones. CPC and CPM will be the only cost metrics that I'll discuss for several reasons - this dataset includes companies with $10K ACV alongside those with $100K ACV; it includes companies in competitive categories, across different industries with diverse audiences. Hence, if we start discussing the average cost per lead or average cost per opportunity, the average figures might be misleading. Instead, I'll focus on the general shifts in other metrics, such as how much cost per opportunity is increasing or decreasing on average, and the patterns we observe. The primary reason I chose to highlight CPCs and CPMs is to provide a clearer understanding of the market landscape.
The average CPM over the last 27 months is $309. In 2022, the average was $215, which jumped to $373 in 2023; this means that CPMs became almost 75% more expensive. However, Q1-2024 data shows that the average CPM is now $230. This sharp decline from last year made me wonder—could this be seasonal? Do we see lower CPMs in some quarters compared to others? I revisited the data for Q1-2022 and Q1-2023, and it seems my hunch was valid.
In both of these quarters, the CPM was the cheapest of the year. For example, the average CPM for 2022 was $215, while in the first quarter of 2022, it was $185—about 14% cheaper than the average. In 2023, the average CPM was $373, but in the first quarter, it dropped to $264—nearly 30% cheaper than the average. If we apply this logic to the first quarter of 2024, we should expect an increase between 14% and 30% in CPMs for the rest of the year.
On the CPC side, things are a bit more normal—the average CPC over the last 27 months is $5.9. It was $4.9 in 2022 and jumped to $6.6 in 2024. Again, we’re seeing more than a 30% increase. There’s a slight decrease in the first quarter, where we are seeing an average of $6.2.
Once again, I checked the data from Q1-2022 and Q1-2023 to see if CPCs were cheaper in the first quarters compared to the rest of the year. In both cases, the dataset shows that CPCs were between 15% - 20% cheaper in the first quarters. This suggests that CPC in 2024 is likely to increase, and the average CPC will be on the same level as last year.
Now, let’s take a look at what’s happening on the MQL side. As mentioned in the previous paragraph, I won’t be discussing the cost per MQL but rather the changes in cost per MQLs.
What’s the difference? If I were to discuss the former, I’d need to mention an average cost; however, the latter involves analyzing the changes in cost per MQL on a company level in percentages, calculating the weighted average, and merging the data. For instance, if Company A had 2K MQLs and their cost per MQL increased by 20%, and Company B had 1K MQLs and their cost per MQL increased by 10%, then the average increase would be 17.5%. In simpler terms, I've ensured the data displays what we need to see.
From 2022 to 2023, the cost per MQL from Google Ads increased by 24.7% on average—unlike what we are seeing on the CPC and CPM fronts, where there has been no decrease in cost per MQL in the first quarters of 2022 and 2023. However, in the first quarter of 2024, we're observing an average of 18% decrease in cost per MQLs from Google.
This decrease in cost per MQL could be explained by companies cutting their Google Ads budgets and starting to focus on what works in Google Ads.
Another interesting piece of data we discovered is that the cost per MQL gets about 15% cheaper in Q2s over the last two years—if we observe the same decrease at the end of this quarter, then this could be an actual trend.
The average MQL:SQL conversion rate from Google Ads over the last 27 months is 19.53%.
We are observing that the MQL:SQL conversion rate has been improving over time. In the 2023 Revenue Benchmarks report, we found out that Google had an average MQL:SQL conversion rate of 21.7% and in the 2024 Q1 Benchmark Report, this rate increased to 25.79%.
Although the conversion rate got better in 2023; since the increase in cost per MQL was higher, the cost per SQL ended up being12.3% more expensive in 2023 compared to 2022. However, with the decrease in cost per MQLs and the increase in conversion rates in the first quarter of 2024, we are monitoring that the average cost per SQL is 31% lower than in 2023 and about 20% lower than in 2022.
This aligns with what we are seeing on the MQL:SQL conversion rate side - although in-platform metrics and cost per MQL are increasing, the increase in conversion rates is prevailing. I think we can assume that companies are becoming more laser-focused, prioritizing quality over quantity, this would also explain the differences in costs.
As companies hone their focus, costs increase. If this is the case, it should reflect on the pipeline ROI - specifically, how much pipeline is generated from each dollar spent on Google Ads? Let’s see.
The average pipeline ROI for Google Ads over the last 27 months is 8.17, meaning that for every $1 spent on Google, companies generated an $8.17 pipeline. In 2022, this pipeline ROI for Google Ads was 7.47; it jumped to 8.34 in 2023 - this is a massive jump! This supports our earlier hypothesis that MQLs are becoming more expensive because companies are focusing on what works, thereby reducing the cost per SQL and enabling the generation of a better pipeline for less. Looking at the first quarter of 2024, we see the same pattern, with the pipeline ROI increasing from 8.34 to 8.49, so it’s actually getting even better.
In the Q1 2024 Benchmark report, we found out that the average SQL:CW conversion rate for Google Ads was 16.30% (we didn't have this data in the 2023 benchmarks report, but upon analysis, we found that the 2023 average was 16.41% and the 2022 average was 15.2%).
Although we are seeing a slight decrease in the first quarter of 2024 compared to 2023, I don’t consider this a significant concern, given that we are in April and many companies are still working on closing deals from March. Irrespective of whether it shows a positive trend or not, I think the critical issue here is the SQL:CW conversion rate in general. While we are observing a positive trend in Google metrics, this is still behind our average. For instance, Linkedin opportunities had an average of 33% SQL:CW conversion rate in 2023, and 30.41% in the first quarter of 2024.
What does the change in revenue ROI look like? In other words, what is the actual dollar added for each dollar spent on Google Ads?
Our dataset shows that the average revenue ROI of Google Ads over the last 27 months is 1.31, which is not particularly strong. It essentially means that most companies are barely breaking even with their Google Ads investments. While the average revenue ROI was 1.13 in 2022, it increased to 1.36 in 2023, and further to 1.38 in the first quarter of 2024. However, this change is not significant enough to justify the budget allocated to Google Ads.
It's important to note that although we're observing better pipeline ROI, MQL:SQL, and SQL:CW conversion rates in 2023 compared to 2022, and in the first quarter of 2024 compared to 2023, these metrics do not seem to effectively translate into revenue. This shows that while companies might be closing more deals from Google Ads, the ACV per closed won deal is decreasing.
I think this report reveals some good news and some bad news. The good news is that the decrease in budget allocation and increase in conversion rates show that companies are becoming more laser-focused on Google Ads and are shifting away from a mindset of merely maximizing MQLs. Initially, the increase in cost per MQLs and in-platform metrics might appear concerning; however, when looking at this data alongside the pipeline ROI and conversion rates, it's clear that the rise in cost per MQL results from a more targeted approach to generating conversions.
However, we need to consider the complete picture. Growing companies isn't just about generating a pipeline; it's about generating revenue, efficient revenue—”you can't invoice the pipeline”. And from what we see, when it comes to actual revenue, Google Ads seems to be lagging behind. We keep talking about focusing on pipeline rather than leads, but even focusing on pipeline is not enough in some cases—and this is one of those cases. Google Ads might help you hit your pipeline goals, but it might not bring you very profitable revenue growth.
If you are our customer and would like to check out your own data, here’s the How-to guide.
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