Summary:

• This report analyzes data from 150 B2B SaaS companies ($8 million to $2 billion ARR) to determine how many impressions and touchpoints are actually required to generate a closed-won opportunity.
• It takes significantly more exposure to close a B2B SaaS deal compared to two years ago, with companies now averaging 2,879 impressions and 266 touchpoints. This represents a 20% increase in touchpoints and 9.5% increase in impressions since 2023.
• Deal size dramatically affects the number of impressions and touchpoints required to close. If opportunities reach or exceed $100K in ACV, impressions skyrocket to nearly 5,500 and touchpoints to 417. This is nearly 2X the average for impressions and 1.5X for touchpoints.
• B2B buyers require repeated exposure throughout increasingly long and complex journeys. Single-touch attribution oversimplifies channel and campaign performance and fails to capture more nuanced pipeline insights at different stages. It’s critical for marketers to deploy multi-touch attribution models to effectively track, measure, and optimize interactions across channels.

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I don’t know how to start; this is one of the most exciting Labs reports. It answers most of our questions and validates many of our hypotheses. We first decided to delve into this topic with the What Does It Take to Close report, where we uncovered how many impressions and touchpoints it takes to generate MQLs, SQLs, and closed-won deals. The results were incredibly surprising, and that report ended up being our most read, with more than fifty thousand unique visitors – it’s been shared many times and even presented at some conferences.

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The reason we wrote that report was because it was, in a way, an easy topic—easy in the sense that everyone in B2B SaaS agrees the buyer journey is complex. The hard part was showing just how complex it is, and we managed to do that. After that report, we received numerous messages and emails asking us to dive deeper into the data and show how these metrics change by company size and deal size. Although we agreed it was crucial, we wanted to ensure we had enough data for each company size and segment of deals. We also waited some time so we could compare our new average data with the previous year’s to see if the numbers had changed in 2024. From what we see, yes—numbers changed in 2024. In a good way or a bad way? You'll have to read the report(s) to find out.

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In the first part of the first chapter, we’ll dive into this change—what our numbers show on average and how these numbers have changed compared to last year. In the What Does It Take to Close report, we used 2023 data and included 50 companies. In this report, we’ve included three times as many companies—150 in total—and the first eight months of 2024. However, we didn’t include MQLs generated after the end of the second quarter to see a clear picture (otherwise, we could’ve seen MQLs generated last week decreasing the conversion rates because they wouldn’t have been SQLed yet).

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The second part of the first chapter will focus on how impressions and touchpoints change by deal size. Then we have the second chapter; in the first part of the second chapter, we will focus on how impressions and touchpoints change by company size (number of employees). And the second part of the second chapter will cover a bonus topic where we’ll show the average deal sizes and conversion rates by company size and deal size—and we’ll also focus on the relationship between company size and deal size. We really, really enjoyed analyzing this data and writing this report. We hope you find it useful as well.

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Methodology:

MQL: Hand-raisers (demo requests, pricing page visitors, contact us submissions, b. Basically every high intent hand-raiser on the website) Ebook form submissions, lead-gen stuff, and webinar registrations were not counted as MQLs. 

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.

Sales Cycle: Starts from the date of deal created, not from MQL created. The reason for this is that since we included both inbound and outbound, and outbound doesn’t start from the MQL level, it would have skewed the average.

Deal Size / ACV: Total new business revenue added divided by the number of closed-won deals (also known as Average Contract Value, Selling Price, Deal Value)

CW: Closed won deals, net new business, new revenue. 

Revenue: Total new revenue added 

Touchpoints: All interactions between stages, such as organic website visits, ad clicks, email opens, webinar registrations.

Linkedin Impressions: Any impression regardless of engagement or clicks or not; on the company level (last 60 days)

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Sample Description:

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Sample Size: 150 B2B SaaS companies, minimum $15k monthly ad spend

Region: 90% North America, 10% UK

Average Deal Size: From $5K to $200K

ARR: From $8M to $2B

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Date Range: 

• From January 1st, 2024 to August 30th, 2024 for the entire dataset. 
• From January 1st, 2024 to June 30th, 2024 for MQLs.

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Company Size Segments: 

• 11-50
• 51-200
• 201-500
• 501-1K
• 1K-5K

Each company size segment has a similar number of companies. 

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Deal Size Segments:

• Up to $10K
• $10K-$20K
• $20K-$50K
• $50K-$100K
• Above $100K

Each deal size segment has a statistically significant sample size, ensuring that no segment skews the average.

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Part I: Change in Impressions and Touchpoints H1-2024 vs. 2023 average

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A caveat before we begin: These metrics are averages. They don’t mean that if you generate X amount of impressions and Y amount of touchpoints, you’ll definitely close a deal. These metrics are guidelines—one can easily reach this amount of impressions and touchpoints by running broad campaigns, but this won’t bring revenue. Instead, it would increase the average number of impressions and touchpoints to close a deal in our next year’s report. So please use these metrics as benchmarks, but don’t assume that if you reach them without a plan or strategy, you’ll close deals.

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In the previous report, we found that a B2B SaaS company needs to have 723 Linkedin impressions from their audience in the last 60 days before that audience actually visits the website, and an average of 54 touchpoints to generate an MQL.

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Disclaimer: Due to Linkedin’s privacy regulations, this data represents company-level impression data rather than contact-level data.

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From MQL to SQL, the average was an additional 87 touchpoints and 1,068 Linkedin impressions. Our hypothesis was that once the MQL is qualified and progresses, more stakeholders get involved in the process, making the buying journey more complex, hence the increase in numbers. So, it doesn’t necessarily mean you have to get more touchpoints and serve more ads to your point of contact; rather, it means that with more people getting involved, the number of touchpoints organically increases.

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We’ve seen this trend continue from the SQL to closed-won phase, where the average was an additional 81 touchpoints and 836 impressions. This part was a bit surprising because, considering the time between SQL and CW was longer than between MQL and SQL, our expectation was to see a higher number of touchpoints than in the MQL-to-SQL phase as more stakeholders (like finance and procurement teams) get involved. For that data, our assumption was that once the pipeline is created, the main stakeholders begin to wait; maybe the main point of contact still keeps coming back to the website, looking at the product, but not the rest of the buying committee. Instead, the other teams—finance, operations, procurement—get involved. So, even though from MQL to SQL it takes less time, there aremore touchpoints as the potential product users visit the website and are retargeted on LinkedIn. But once the deal is created, it takes more time to close, but there are fewer touchpoints (finance and procurement teams don’t visit the website as much and mostly aren’t retargeted due to their titles).

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Okay now, 2024 - what changed? 

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Our data shows that the average number of impressions there are for a website visit is now higher than it was—we’re seeing a 23% increase, meaning that now an average B2B SaaS company needs 894 impressions instead of 723.

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I keep repeating myself in some reports, but I feel like I have to—this increase in impressions could indicate that people on Linkedin are becoming more immune to ads; therefore, their threshold is getting higher. What’s the solution? Testing a different platform than Linkedin? I don’t think so, because we know the audience is already here. I think the solution is experimenting with different types of campaigns. Most companies are using single images and conversation ads, and the end-user recognizes it’s an ad as soon as they see it. But what if companies shift their focus to different formats? Formats their audience isn’t familiar with? That’s our two cents. Because if the problem was solely Linkedin, we wouldn’t be seeing considerable increases in the number of touchpoints we found at each stage—spoiler alert—but we are.

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Compared to 2023, the average number of touchpoints there are to generate an MQL increased by 31%—now it takes 71 touchpoints for a B2B SaaS company to generate an MQL, compared to 54 last year.

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Once an MQL is created, we’re seeing a slight decrease in the average number of impressions there are until SQL. According to our previous data, it would take an average of 1,068 impressions from MQL to SQL; the current data is 5% lower, now we’re seeing an average of 1,019 impressions. However, this decrease isn’t reflected in the touchpoints, where we’re seeing a 10% increase—it used to take an additional 87 touchpoints to generate an SQL once an MQL was created in 2023; now it takes 96. That’s something we might need to think about—I think if the audience needs more convincing, realistically, we should have seen higher impressions and touchpoints at the same time. But if we’re seeing a decrease in impressions and an increase in touchpoints, this could actually be about a change in touchpoint channel allocation. Let’s have a look at it.

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The leading touchpoint in the MQL-to-SQL phase in 2023 was the website with 36.8%, followed by Linkedin with 14.7% and email with 12.6%. Looking at the 2024 data, we’re seeing that the first two remained unchanged; however, the split for email has decreased to 7.1%, and the split for display increased from 1.6% to 4%. I’ve highlighted the importance of email marketing in the post-MQL phase in that report, and it seems like with the decrease in email and increase in display, we ended up seeing an increase in the number of touchpoints there are in the MQL-to-SQL phase.

However, this is just my theory; I couldn’t find any other change.

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Lastly, based on 2023 data, an average B2B SaaS company there are an additional 81 touchpoints and 836 impressions in the SQL-to-CW phase. Similar to what we’ve seen in previous data points, we’re seeing an increase here as well.

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On average, it takes 965 impressions from SQL to closed-won now—a 15% increase compared to last year; and on the touchpoints side, we’re looking at 99—a 22% increase from 81 last year.

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To summarize:

• Impressions until the first website visit increased from 723 to 894 (23% increase)

• Touchpoints until MQL increased from 54 to 71 (31% increase)

• Impressions between MQL-to-SQL decreased from 1,068 to 1,019 (5% decrease)

• Touchpoints between MQL-to-SQL increased from 87 to 96 (10% increase)

• Impressions between SQL-to-CW increased from 836 to 965 (15% increase)

• Touchpoints between SQL-to-CW increased from 81 to 99 (22% increase)

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‍This means that while a B2B SaaS company had 2,627 impressions and 222 touchpoints to close a deal in 2023, they now need 2,879 impressions and 266 touchpoints in 2024—which equals a 9.5% increase in the number of impressions, and a 19.8% increase in the number of touchpoints.

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Part II: Change in Impressions and Touchpoints by Deal Sizes

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Now, we know the average number of impressions and touchpoints for B2B SaaS, but it’s true that these metrics will change depending on the circumstances—we can’t possibly write down and analyze every scenario, but we can at least analyze the most important factors, which for us (and for most people) are deal sizes and company sizes.

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Let’s start with deal sizes—we segmented deal sizes into five categories: up to $10K, $10K-$20K, $20K-$50K, $50K-$100K, and $100K and above, ensuring that each segment has a similar sample size. What we found didn’t surprise us—on the contrary, it was one of those moments where I said, “I KNEW IT” as soon as I saw it.

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In a nutshell, we’re seeing that as deal size gets higher, the number of touchpoints and impressions there are also increases. Companies with smaller deal sizes need fewer impressions and touchpoints. As expected, companies that fall under the ‘up to $10K’ deal size segment have the fewest impressions and touchpoints.

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For example, the number of touchpoints for a company with an average deal size of more than $100K is 2.65 times higher than for a company with an average deal size of up to $10K; similarly, impressions for an MQL are also 3.95 times higher between these two segments.

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As highlighted in the previous part, the average number of impressions there are for the first website visit is 894, and the average number of touchpoints there are to generate an MQL is 71. For companies with small deal sizes, we’re seeing a massive difference.

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Companies with up to $10K deal sizes need 357 impressions for the first website visit and 47 touchpoints to generate an MQL (40% and 31% less than the average, respectively).

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This trend continues in the MQL-to-SQL and SQL-to-CW stages as well; companies with up to $10K deal sizes need 52% fewer impressions and 41% fewer touchpoints than our average.

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When it comes to companies with average deal sizes between $10K and $20K, we observe that these companies need 29% more impressions and 28% more touchpoints to close a deal compared to companies with deal sizes below $10K.

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But when we compare this data with our overall average, we see that companies with average deal sizes between $10K and $20K need 38.5% fewer impressions and 24% fewer touchpoints than the B2B SaaS average. So, if we take a step back, what we’re seeing is that although the buyer journey is complex, if your deal size is below $20K, then this journey is considerably less complex.

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Moving to the upper market, things start to change quickly. Companies with average deal sizes between $20K and $50K need 44% more impressions and 30% more touchpoints than companies with $10K-$20K deal sizes. This is quite a large increase, and what we see is that these numbers are now closer to our overall average. While the number of impressions for companies with $20K-$50K deal sizes to close a deal is 10% lower than our average, the number of touchpoints is just 1% lower.

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We begin to see the real complexity of the buyer journey when we go further, to the $50K-$100K segment. For this segment, the number of impressions to close a deal is actually 5% higher than the average, while the number of touchpoints is 16% higher.

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This means that although we need an average of 2,879 impressions and 266 touchpoints to close a deal, for the $50K-$100K segment, we need 3,035 impressions and 309 touchpoints.

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And as expected, when we finally look at the $100K+ deal segment, things get crazy. We need almost 2x as many impressions and 1.5x more touchpoints than the average—this means that companies with deal sizes over $100K need almost 5,500 impressions and 417 touchpoints to close a deal.

What’s more interesting is the difference in the SQL-to-CW stage here; once the deal size is above $100K, these companies need an average of 2,081 impressions in the SQL-to-CW stage, while for companies with $50K-$100K deal sizes, this is half that amount; for companies with $10K-$20K deal sizes, this is 4x lower.

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When it comes to the number of touchpoints in the same stage, companies with deal sizes over $100K need 1.4x more touchpoints than companies with $50K-$100K deal sizes, and almost 2.5x more touchpoints than companies with $10K-$20K deal sizes. This huge difference in the SQL-to-CW stage could indicate the involvement of other teams in high-value contracts.

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Part III: Change in Impressions and Touchpoints by Company Sizes

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While reviewing this data, I decided to pause and take a few days to digest it and try to find explanations—but I’m still unsure about the implications of some of these findings. To put things in the simplest terms, what I discovered is that as company size increases, the need for touchpoints and impressions also increases. This made sense when we were discussing deal sizes, but it doesn’t really add up when we’re talking about different company sizes. We can’t just say that as companies get bigger, their contract prices increase, making the buyer journey more complex.

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No, we have plenty of enterprise companies with low deal sizes and plenty of smaller companies with high deal sizes in this dataset, so that’s not the right answer.

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On the contrary, I would assume that as companies grow, more people know them, their brands become more well-known, and they spend less time convincing their audience. Hence, the number of impressions and touchpoints for these companies should be lower compared to smaller companies with similar deal sizes. I’ve seen this exact narrative presented in boardroom slides time and again—that we can’t calculate the impact of brand investment, but we can certainly assume that with brand investment, our long-term costs will go down, and we’d generate opportunities more easily—which I completely agree with. But this data doesn’t support that. If we were only seeing an increase in touchpoints, but not in impressions, we could have assumed that maybe those touchpoints are coming organically through the power of the brand. Then we could check the split between channels in those touchpoints to validate the hypothesis. However, since we’re seeing increases in both impressions and touchpoints, we can’t really make that assumption.

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Before speculating any further, I'm going to have to check the average deal size for each company size segment and cross-reference to see if we actually have higher deal sizes in bigger companies. If we do, then we have our answer; if not, we're in serious trouble...

This is the end of the first chapter. We'll be publishing our findings about company sizes and their relationship to deal sizes and conversion rates next week. Follow us on LinkedIn to get notified when we release the report!

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All HockeyStack Labs reports are done using anonymized HockeyStack customer data and cannot be used for any commercial use without written consent from HockeyStack. We did not partner with anyone on the creation of this report and it was not sponsored by a vendor. Reach out to emir@hockeystack.com with any questions.

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