One of the few things everyone in the B2B SaaS can agree on is that the buyer journey is not linear and it requires multiple touchpoints to close a deal. In the ideal world, where there is no war, hunger, and unlimited runway for each startup, one can assume that if they have the product-market fit and a proper ICP definition, they will get a conversion at first sight.
But there’s no ideal world, resources are limited, runways are short, and conversion at first visit is nothing more unusual than love at first sight.
For this exact reason, we decided to see what’s actually happening throughout the funnel, what does it take to generate demos, opportunities, and revenue? In short, what does it take to close a deal?
We analyzed the data of the last 13 months from January 1st 2023, to January 31st 2024; to see a clear picture, MQLs generated after Dec 31st 2023 were not included.
This report has four parts. The first part focuses on the MQL level, analyzing the user journey up until they become an MQL, both on the website and Linkedin impression levels. In the second part, we analyze the user & company journeys from the moment they become an MQL to SQL. The third part is about what happens from SQL to closed-won. During this analysis, we also identified some interesting patterns, such as what makes deals close faster or slower, and what channels and strategies can be leveraged at each step.
This time we’ve put the methodology right up front to cut through any confusion about what we mean by our terms. I strongly recommend giving it a look before diving into the rest.
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 previous 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. Every company has different definitions, but we unified this on the backend and used the SQL definition for when the pipeline is actually created.
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.
Sample Size: Over 1.5M contacts from more than 50 companies, all B2B SaaS.
Sample Description: From $5M ARR to $1B ARR; average ACV from $5K to $120K.
Caveats: We have excluded irrelevant data and small sample sizes. For example, if a $200K deal was created from one DuckDuckGo search in one touch, we didn’t include that as it was not repeated and it was rather a glitch in the matrix.
As mentioned at the beginning, we didn’t just analyze the journey with website visits; we leveraged HockeyStack’s unfair advantage and included Linkedin impression level data as well. This means we started analyzing the journey from the first impression on Linkedin in the last 60 days to the first website visit.
What we found is that a B2B SaaS company needs to have 723 Linkedin impressions from their audience before they actually visit the website. This doesn’t mean 723 impressions per user but 723 impressions on a company level before someone from that company visits the website.
(Disclaimer: These figures are averages from all deals. In most cases, 10k+ companies need over 5k impressions in the last 60 days until the first website visit, but this is due to the sheer volume of employees.)
There’s a sort of correlation between the ACV and the number of impressions. It’s not a very clear correlation, but if the ACV is >$100K, then the total number of impressions required increases from 723 to 950. However, there’s no pattern when the ACV is under $100K.
The industry is full of sales leaders and CEOs who expect that if someone needs your product, they will convert right away, isn’t it? I’ve been there; I believe all of us have been there… But now we have the data showing otherwise.
After analyzing more than 1.5m contact journeys, an average B2B SaaS company needs 54 touchpoints to generate an MQL after the first website visit.
When you’re running an ABM campaign, if you see someone from your target company visiting the website for the first time, don’t get overexcited like I do; they will probably have to come back a bit more…
For B2B SaaS companies with an ACV of less than $20K, the average website touchpoints required is 31. Between $20K-$60K ACV, it’s 48. Above $60k ACV, it’s 75. There’s a clear pattern between the number of touchpoints and ACV at the top of the funnel.
What constitutes these touchpoints? Can we identify any patterns?
Yes, we can.
Up until an MQL is created, we’re seeing the following split among 54 touchpoints:
– 33.6% Website
– 21.2% Linkedin Ads
– 20.9% Google
– 7.0% Organic Social
– 5.3% Display
– 3.8% Email
– 2.9% Youtube
– 2.4% Facebook
– 1.8% Bing
– 1.1% Reddit
To reiterate, this is the average split we’re seeing, hence this doesn’t mean that this is the recommended split by any means.
This meant that we needed to dig further – were there any touchpoints that would reduce the total average, or were there any that would increase it?
Surprisingly, email marketing takes first place. What we’re seeing is, once a prospect visits your website, email marketing actually reduces the number of touchpoints needed by an average of 7.5. So, with a proper email marketing strategy, one might actually reduce the touchpoints required from 54 to 46.5 – which on average equals shortening the conversion cycle by 7 to 10 days. (And here you can see the email marketing template of ActiveCampaign and how they track their success and which metrics they use.)
Another surprising finding is Youtube. It seems like website visitors who get targeted on Youtube also convert faster, on average Youtube remarketing campaigns decrease the average touchpoints by 7.1. However, I like to highlight the remarketing part – we’re only seeing this in the remarketing campaigns, not for cold audience campaigns.
Yet, this data shows that if you set up an email marketing strategy for your website visitors (with HockeyStack, you can deanonymise your website visitor data at the contact level) and build a remarketing campaign on Youtube for your website audience, this could decrease the average touchpoints from 54 to 40.
It’s worth noting that we have excluded Youtube and Reddit from the 2023 Recap report since the total spend in 2023 on Youtube was low. However, as of Q4-23, we’ve started to see a significant increase in Youtube campaigns from over ten clients.
Let’s look at the other side of the river. What about the touchpoints that actually cause an increase in the number of touchpoints required for conversion?
Well, it’s Youtube again. Although on the remarketing side, it actually helps us to shorten the deal cycle, if it’s used incorrectly, it apparently might increase the cycle length by an additional 4 touchpoints.
Another interesting finding here is the Google Broad match campaigns. If anytime at their journey, regardless of the first touch or the 50th touch, if the touchpoint contains a Google broad match campaign, it actually increases the sales cycle by five touchpoints. This is showing us that it’s not only about bringing people to your website, but it’s also about bringing the right people to your website and to the right content.
Let’s use HockeyStack as an example, we can use a keyword like GA4 alternative for B2B SaaS enterprise with a phrase match – however if we use the broad match, we’ll probably get someone who’s searching for enterprise marketing software. Although this person might have already been considering Hockeystack and been on the website, if this person clicks on this ad, actually this doesn’t help with the journey at all! (Sample size here is above 1k so it’s definitely not a one-off thing, but it’s a pattern).
Similar to the broad match, we have another one from Google. Display ads… This data I suppose will put another nail to its coffin, apparently display ads actually increase the touchpoints required until MQL by 7.2…
Do you remember the times when B2B marketers were talking about Tiktok and claiming that it’ll be the next big thing in B2B? Seems like Tiktok ads also actually increase the touchpoints required to convert by 7.7…
So if your strategy contains display ads, Tiktok, and broad match; you might be needing 75 touchpoints to convert someone instead of 54.
So from the first impression to website visit, it takes around 700 LinkedIn impressions. Then from the first website visit to MQL, it takes around 50 touchpoints.
They submitted the form, then what? Do you go home and say ‘this was a good day’ or do you keep thinking about optimizing the post-MQL journey?
I hope it’s the latter because once the form is submitted and MQL created, it needs 1068 more LinkedIn impressions and 87 more touchpoints to become an SQL – which is actually higher than what’s needed from the first impression to MQL.
But when you think about it, it makes sense because as said in the beginning, our definition of SQL is when the pipeline is created and in most cases, this happens at least on the second call where multiple stakeholders get involved in the process whilst during the MQL process there’s generally one stakeholder – as the number of stakeholders increases, the number of touchpoints increases accordingly.
We’re also seeing more LinkedIn impressions from MQL to SQL journey than the first-touch to MQL journey because most likely the additional stakeholders are being retargeted on LinkedIn after they visit the website.
Here what I advise our clients is to split their retargeting layers because the intent in the remarketing audiences are different; remarketing audiences should be used to increase new conversions, but in most cases, they are also being used for the existing opportunities as these opportunities are not excluded – I am definitely not against targeting the open opportunities, but instead of generic retargeting campaigns, running an open opportunities campaign for the companies who are already in the pipeline actually speed up the deal process.
So what we can say is that it’s not necessarily required to have more touchpoints and impressions to generate a pipeline but since during this process, more stakeholders get involved, the buying journey gets more complicated hence the numbers increase.
What constitutes the MQL-to-SQL touchpoints?
From MQL to SQL journey, we’re seeing the following split among 87 touchpoints:
– 36.8% Website
– 14.7% LinkedIn
– 12.6% Email
– 11.4% Google
– 10.1% Organic Social
– 7.5% Facebook
– 4.1% YouTube
– 1.6% Display
– 1.2% Bing
With this data in mind, what’s the situation when it comes to channel effectiveness?
Although we haven’t seen this on the first-touch-to-MQL side, on the MQL-to-SQL side, we’re seeing that Reddit and Facebook actually decrease the number of touchpoints required. It’s likely because of the retargeting campaigns. On average, Reddit campaigns decrease the average number of touchpoints required by 5.8, and Facebook decreases it by 4.7. Although we saw that YouTube retargeting campaigns help to decrease the number of touchpoints from the first touch to MQL, we don’t see this on the SQL level.
Another interesting thing here is G2 – during the MQL to SQL stage, if at least two unique users (likely stakeholders) visit the website through your G2 profile, this decreases the number of touchpoints required by 10 – which in a way decreases the average time from MQL to SQL by a week.
On the other hand, what we’re seeing on the things that increase the number of touchpoints required are pretty aligned with what we are seeing on the MQL side. On average, Google Broad match campaigns increase the touchpoints required by 3.8 and display ads increase it by 5.8 – which means that broad match + display ads strategy could increase your sales cycle for a week. (The calculation method here is based on the average sales length being 69 days, according to the Marketing & Revenue Benchmarks 2023 Report.)
As our self-reported attribution report showed, the search is definitely not dying – however, broad match and display ads are dying, they literally do no good, on the contrary, they harm your sales process.
From the first impression to MQL, it takes 700 Linkedin impressions and 54 touchpoints.
From MQL to SQL, it takes 1068 Linkedin impressions and 87 touchpoints.
Once the SQL is created, we’re seeing that it takes an average of 81 touchpoints and 836 Linkedin impressions until the deal is closed.
This part was a bit surprising for me because considering that the average sales cycle is 69 days (see the recap report), and the time between SQL-to-CW is longer than the time between MQL-to-SQL, my expectation was to see a higher number of touchpoints as more stakeholders are involved and in most cases, the finance and procurement teams get involved after the proposal. Here, my assumption is that once the pipeline is created, the stakeholders begin to wait; maybe the main point of contact still keeps coming back to the website, looks at the product, but not the rest of the buying committee. Instead, the other teams – finance, operations, procurement – get involved. So, actually, even though from MQL to SQL it takes less time, it actually requires more touchpoints as the potential product users visit the website and are being retargeted on LinkedIn (with the job function filters). But once the deal is created, it takes more time to close the deal but requires fewer touchpoints (finance and procurement teams don’t visit the website that much, and don’t get retargeted due to their titles).
Here’s the interesting part, in the CW part, we can finally see a good pattern between the number of touchpoints and ACV. As ACV gets higher, the number of touchpoints gets higher. We’re seeing an average of 51 touchpoints from SQL to CW when the ACV is up to $25K, then this number goes up to 85 when the ACV is between $25k to $80k, and it goes up to 120 when the ACV is above $80k. Probably because for more expensive contracts, more people need to get involved in this process hence the increase in touchpoints.
What constitutes SQL-to-CW touchpoints?
-27.7% Website
-19.6% Google
-17.3% Linkedin
-11.3% Facebook
-7.6% Email
-5.3% Display
-4.7% Organic Social
-4.1% Facebook
-2.4% Bing
On the channel side, what we observe aligns with our findings on the pipeline side. Firstly, we’re seeing that if four or more stakeholders visit the website after checking G2, this reduces the number of touchpoints by 15 and, on average, speeds up the sales cycle by over a week.
When companies are exposed to Facebook ads once they are qualified, this decreases the number of touchpoints required by 4.6; similarly, if they are exposed to Reddit Ads, this also decreases the number of touchpoints required by 3.6. Similar to the MQL side, we continue to see the power of email marketing here—if at least three people in the buying committee receive marketing emails, this actually reduces the number of touchpoints required by 4.19.
So, having a multi-channel remarketing strategy for open opportunities that includes email marketing, Facebook, and Reddit might actually reduce the number of touchpoints required by 12, which would be equivalent to decreasing the sales cycle by almost a week. And on top of this, having a strong G2 profile could actually decrease your sales cycle by two weeks.
From the marketing standpoint, I know it’s pretty hard to only target the open opportunities. On Linkedin, you can use Hubspot dynamic audiences or if you’re using Salesforce, through a third party platform like Metadata or Zapier. But for Facebook and Reddit, it might not be that easy. Even if you manage to find personal emails and upload CSVs, this approach lacks scalability since the file needs frequent updates—potentially every few days. I suggest experimenting with an alternative strategy that I’ve been testing.
Create a specific page for these opportunities, such as hockeystack.com/what-we-offer, and make sure that this page isn’t accessible from your homepage. Then implement this into the AE playbook either for during or after the qualification call. The AE will send the link and ask people to click on it to see everything they discussed in the call summarized. The thing here, though, is if your AE sends this after the call, not everyone clicks on that link; but if the AE does this during the call and says, “Can you please check if the link is working for you?”, then most people will click on it to check.
By creating a remarketing audience from the visitors of this specific link, you effectively create an open-opps audience, which can then be utilized across any marketing channel.
This approach can also be initiated before the AE call, but there’s a risk. If the AE doesn’t qualify the deal, you might end up retargeting unqualified prospects as well.
Analyzing the 1.5M data points was a massive undertaking and took longer than anticipated, but it provided a reliable sample size for making educated assumptions. We analyzed MQLs generated from January 1st, 2023, to December 31st, 2023, and SQLs & revenue created until January 31st, 2024, from the MQLs generated after January 1st, 2023. This analysis reinforces the idea that demand creation is crucial. B2B SaaS companies need to focus on maximizing their reach and penetrating their target audience, as it takes more than 700 impressions per company to even visit your website.
Seeing YouTube in the MQL touchpoints, but not in the SQL and revenue touchpoints, is intriguing. My educated guess is that this is because most of our clients began investing in YouTube in Q4-2023. Given the length of sales cycles, we might need to wait a bit longer to see YouTube’s impact on the lower funnel, but I think the first results are actually promising.
Another key observation was the power of email marketing and nurturing. The crucial takeaway from this analysis is the necessity for companies to implement an end-to-end multi-channel strategy that includes various ad channels, as well as email marketing and customer marketing (as we’re seeing the impact of G2).
In summary, this analysis confirms the complexity of B2B buyer journeys. It’s not just about the number of touchpoints to close a deal; the nature of these touchpoints significantly varies at different stages, and some touchpoints apparently do more harm than any good.
As a next step, we aim to understand the relationship between touchpoints and the buying committee. For instance, how do touchpoints shift when finance and procurement teams become involved in deals? Are there channels that expedite deal closure after these teams join the process? We will explore these questions in the second part.
I hope you find this report insightful. Please feel free to reach out with any questions or requests for future reports.
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