All HockeyStack Labs reports are done using anonymized HockeyStack customer data. We did not partner with anyone on the creation of this report and it was not sponsored by a vendor.

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Every company I worked or consulted for wanted to do ABM at some point; it’s always that shiny thing every marketer wants to try.

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In a way, ABM is sometimes seen as a shortcut to level up the enterprise game. While this expectation is fair, ABM needs to be a part of the GTM motion, not the entire GTM strategy, especially if there’s no other plan to improve the enterprise pipeline.

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When starting an ABM motion, almost everyone agrees that it will take time and be more expensive than a non-ABM approach. However, about four weeks in, people often begin questioning if ABM is working, when you’ll be generating pipeline, or if it’s right for your company. Then the following few weeks/months are often spent with marketers trying to convince other stakeholders to be patient. 

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Hence, we decided to publish this report to help other marketers manage expectations if/when they run ABM campaigns.

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TLDR data:

• Cost per Win: More expensive in ABM.
• Time to Close: ABM deals take significantly longer to close.
• Budget Allocation: ABM campaigns get surprisingly low budgets.
• Effectiveness: Despite these factors, ABM seems to be delivering good results.

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Methodology

MQL: Direct form submissions such as demo, pricing page, and contact us. This doesn’t include any ebook downloads, webinar registrations or anything like that.

SQL: Pipeline created. Every company has different definitions, but we unified this on the backend and used the SQL definition for when the pipeline is actually created.

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. 

ABM Data: Campaigns where the name or URL contains “ABM” or a specific company name, or 1:1, 1:few, or 1:many. Platforms include 6Sense, Terminus, Rollworks, Demandbase; channels include LinkedIn, Display, Email, YouTube, Google, Facebook.

Sample Size: 44 B2B SaaS companies.

Sample Description: 

- From January 1st 2023 to March 31st 2024

- From $20M ARR to $2B ARR; average ACV from $18K to $140K. 

- 84% NAM, 16% UK

-At least 5% of the budget was spent on ABM for at least one quarter.

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What we analyzed:

-$10M ABM spend, more than $100M total ad spend

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Part I: Spend & Top of the Funnel

According to our dataset, companies allocate only 11.05% of their budget for ABM campaigns. 

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This indicates that most of these companies treat ABM as a side project, not as their entire GTM motion. Considering the sales cycles and the need for cash flow, I can understand the rationale behind this budget allocation.

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When we look at the channel split in this 11.05% spend, our data shows that:

- 54% of the ABM budget is spent on LinkedIn

- 41% is spent on the Display Network

- 4% is spent on YouTube

- 1% of the ABM budget is spent on Facebook

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I can understand the YouTube part—this is actually something I’ve been recommending to our customers as well. If you direct your ABM traffic to specific landing pages (and you should) and have enough visitors, you can technically retarget these page visitors on YouTube.

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This works best, especially if you have specific landing pages for ABM campaigns such as hockeystackcom/linkedinads/productvalue/abm/salesforce. Then you can retarget the visitors of the pages that contain “abm” on YouTube.

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What strikes me the most is the Facebook spend. Even though it’s just 1%, it’s still very surprising that some companies use Facebook for ABM. My first assumption is that this could be a retargeting play, similar to the one I mentioned about YouTube in the previous paragraph, where these companies might be using Facebook to retarget people who visited their ABM pages. 

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Another assumption, though more unlikely, is that these companies might be using Facebook for 1:many or 1:few campaigns. Maybe they are using Metadata or Primer to match the audiences of their target account lists on Facebook.

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When we look at the other 88.95% part, the way these companies spend their budget across channels is very much identical to what we found out in the Q1-24 Recap report; LinkedIn is getting most of the budget, followed by Google, Facebook, YouTube, and Reddit. The share of Bing keeps decreasing more and more every month, suggesting that more companies are losing their faith in Microsoft Copilot.

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As expected, CTR in the ABM campaigns is 4.56x better than in the non-ABM campaigns; aligned with that, CPC in the ABM campaigns is also 4.38x higher than in the non-ABM campaigns. In a way, high CTR levels up itself with expensive CPC. I don’t think anyone would question why we’re seeing higher costs in ABM campaigns.

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You might remember from the previous Labs reports that I refrain from talking about cost per MQL as it’s dependent on many variables and a high cost per MQL might not necessarily be a bad thing if it brings better pipeline. I won’t be talking about the cost per MQL in this report either, but I’ll just highlight the difference between the cost per MQL from ABM campaigns and the cost per MQL from non-ABM campaigns, and the split of MQLs.

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As mentioned earlier, companies allocate around 11% of their budget for ABM campaigns. Out of 11% budget allocation, ABM MQLs make up 3.09% of the total MQLs. Whereas non-ABM campaigns get 88.95% of the budget, and they make up 96.91% of the total MQLs. This means that the cost per MQL in the ABM campaigns is 3.89x higher than in non-ABM campaigns.campaigns. 

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Part II: Pipeline

It was expected that ABM campaigns would be more expensive on the top-of-the-funnel side, but we need to look at pipeline and revenue metrics all together to see the complete picture. In an ideal world, we should see much better return on investment in the later stages.

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Let’s start with the MQL:SQL conversion rate; according to our dataset, MQLs generated from ABM campaigns have a 1.7x better conversion rate. This conversion rate also means that although ABM only makes up 3.09% of the total MQLs, on the SQL side, ABM makes up 5.15% of the SQLs, whereas non-ABM makes up 94.85% of the SQLs.

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There’s certainly an improvement on the SQL side, and the conversion rate is significantly better. However, these metrics still don’t justify the spend because the cost per MQL is also significantly higher. This means that even if the conversion rate is better, the cost per SQL from ABM campaigns is still 2.29x higher. 

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But is this necessarily a bad thing?

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It depends on the angle. If you’re measured by the number of MQLs or the number of SQLs, then yes, it’s a terrible thing. However, if you’re measured by pipeline, it might not be a terrible thing. Let’s see what the pipeline shows

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Deal size is where we finally begin to see what ABM is about; the average deal size of SQLs generated from the ABM campaigns is 3.98x higher. This means that although the cost per MQL and cost per SQL are significantly higher in ABM campaigns, the spend:pipeline ROI is actually 1.74x better. ABM campaigns indeed bring much larger deals. 

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Let’s work on an example:

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Company A spends $1M

- 11% of this budget goes to ABM: $110K

- 89% of this budget goes to non-ABM: $890K

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Let’s say the cost per MQL was $100 in non-ABM.

Since cost per MQL in ABM is 3.89x, it will be $389. 

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- So, there will be 284 MQLs generated in ABM

- And 8695 MQLs generated in non-ABM

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Now let’s assume that the MQL:SQL conversion rate was 10% in non-ABM.

Since the MQL:SQL conversion rate in ABM is 1.7x, it will be 17%

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- This means that there will be 48 SQLs from the ABM campaign,

- And 890 SQLs from the non-ABM campaign.

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Now let’s assume that the deal size was $10K.

Since the average deal size in ABM was 3.98x, it will be $39.8K.

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- With 48 SQLs and a $39.8K deal size, ABM campaigns will bring a $1.92M pipeline

- With 890 SQLs and a $10K deal size, non-ABM campaigns will bring an $8.89M pipeline

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Since the spent in ABM was $110K; $1.92M pipeline divided by $110K equals 17.39.

And since the spent for non-ABM was $890K; $8.98M pipeline divided by $890K equals to 10.

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Meaning that ABM campaigns have a 1.74x better spend:pipeline ROI. 

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Part III: Revenue

Revenue is where we should see the impact of ABM the most, as this is where expectations for ABM are highest.

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On the SQL:CW conversion rate side, we’re seeing that ABM SQLs have a 28% better close rate; moreover, the average deal size for each closed won deal is 4.08x higher. 

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This is interesting, and you might ask how the ACV difference jumped from 3.98x to 4.08x, which is a fair question.

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The answer lies in the sales process; it seems the average discount ratio in ABM deals is 5.4% while it’s 7.8% in non-ABM deals. 

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This means that ABM deals not only have much larger deal sizes but also that the discounts these deals get are 44% less than non-ABM deals. Based on these numbers, the spend:revenue ROI of ABM campaigns is 2.22x better than non-ABM campaigns. 

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So if we continue with the example from the previous part:

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We found out that there will be 48 SQLs and a $1.92M pipeline from the ABM campaign,

and 890 SQLs and an $8.98M pipeline from the non-ABM campaign.

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Let’s assume the SQL:CW close rate is 10%

Since ABM campaigns have a 1.28x better close rate, it will be 12.80%.

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- This means that there will be 6 closed won deals from ABM campaigns

- and 89 closed won deals from non-ABM campaigns.

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We assumed that the deal size was $10K for non-ABM and $39.8K for ABM.

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Now that we know there will be a 5.4% discount for the ABM ones, and a 7.8% discount for the non-ABM ones, 

-The average deal size will be $37.65K for ABM, 

-And $9.22K for non-ABM.

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$37.65K x 6 closed won = $225.9K revenue

$9.22K x 89 closed won = $820.1K revenue

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-Since the spend in ABM was $110K, $225.9K divided by $110K equals 2.05.

-And since the spend in non-ABM was $890K, $820.1K divided by $890K equals 0.92.

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Hence we are seeing a 2.22x higher spend:revenue ROI in ABM campaigns.

However, if we look from another angle, this also shows that:

- $110K spend, 6 closed won deals = $17.87K cost per win for ABM.

- $890K spend, 89 closed won deals = $10K cost per win for non-ABM.

This means that if you look at the cost per MQL, cost per SQL, and cost per win data, ABM campaigns indeed seem expensive. But if we look at this data from pipeline and revenue levels, we see that ABM campaigns have a 1.74x better pipeline ROI and a 2.22x better spend:revenue ROI.

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The last thing I’d like to highlight here is deal cycles. These metrics above might look all shiny and beautiful, but it’s very important to keep in mind that ABM is a long-term play. When we look at the average time to close, our dataset shows that ABM deals take 3.1x more time to close. This means that having an ABM motion can be profitable, but it can also damage your cash flow and accelerate your burn rate if not planned correctly.

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Conclusion

ABM campaigns, though more costly upfront, show substantial advantages in terms of pipeline and revenue ROI. Despite the higher costs per MQL, SQL, and Won; ABM campaigns deliver significantly larger deal sizes and better long-term returns. As highlighted, our data shows that ABM campaigns have a 1.74x better pipeline ROI and a 2.22x better spend:revenue ROI compared to non-ABM campaigns. However, it's crucial to consider that ABM deals take 3.1x longer to close, this requires careful planning. 

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