Every marketer deep down knows that they don’t just contribute to inbound revenue; their activities also influence outbound.
But whenever their success is measured, boards and CFOs mostly look at one of these three things:
– What’s the spend and total leads generated (medieval)
– What’s the spend:inbound pipeline ROI
– What’s the spend:inbound revenue ROI
Because there has been no simple way to prove the marketing influence on outbound sales at scale. It’s possible to track call recordings and label outbound deals as “marketing influenced” if the buyer says stuff like “I’ve been seeing your ads everywhere” or “I saw you at this event” but, this is firstly not scalable and we’d have to rely on SDRs to ask something like “have you heard about us before?” — a question that differs subtly from “how did you hear about us?”. Secondly, SDRs will push back because they will argue that they were the ones who created the deal.
What’s the alternative? I’ve been thinking about this a lot. I think I first discussed this with my CMO back at Deepcrawl. I was always told that marketing was influencing outbound deals, but we just didn’t know how to uncover this dark social side without making lots of assumptions like “okay, they must have downloaded an ebook three months ago, and another person in this company visited the website a month ago, and an SDR reached out to someone else from that company last week and booked a meeting” – obviously, this approach wouldn’t make sense at all. In this report, we tried to uncover some parts of the marketing influence on outbound deals.
I want to highlight that this report is not perfect either; we cannot simply uncover the entire marketing influence in the outbound deals, but we can definitely shed some light on it. What we did in this report was to look at the outbound deals and analyze company-level impression data where the deal had at least 5 impressions per employee in the last 30 days before having the first meeting. This means we split the deals by the number of employees because using a flat rate of 50 impressions for every company, for example, wouldn’t accurately represent companies like Amazon with 50k employees. Instead, we ensured that for each employee, there were at least 5 impressions in the last 30 days.
Why 5? Because in our What Does It Take to Close report, we found out that the average number of impressions to generate an opportunity was over 1800. The companies we looked at in that report mainly had 200-500 employees. To get a benchmark, we did a simple calculation: we divided 1800 (the average number of impressions needed for an opportunity) by 375 (the midpoint of the 200-500 employee range), which gave us about 4.8. So, we rounded up and decided on 5 as our benchmark for impressions per employee.
TLDR
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.
SQO: Pipeline created; SQL, 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.
Sample Size: More than 50 B2B companies, all B2B SaaS.
Sample Description: From $5M ARR to $1B ARR; average ACV from $5K to $120K. From January 1, 2023, to February 29, 2024.
Impressions: Linkedin ads impressions.
Engagement: Linkedin ads engagement.
Outbound: Deals created from a sales touchpoint.
Inbound: Deals not created from a sales touchpoint.
Deal created: Once the deal amount field is filled.
I was speaking about ABM at the Pavilion webinar last week and just before the end, I got a question, “Is outbound dead?” Back at Cognism, SDRs were constantly generating a strong pipeline from outbound, so I hadn’t really given it much thought; it was definitely working. But that question made me realize how little I had considered it outside of Cognism, about how other companies generate their pipeline and revenue. Has it already been revolutionized?
To find out what’s happening with outbound, I started with this juicy question: Is outbound dead? Can B2B SaaS companies really generate a pipeline with outbound? What percent of their revenue comes from outbound? Are these outbound deals more qualified than inbound ones?
On the pipeline level, it seems like outbound is not quite dead. Outbound deals make up 64% of the pipeline, while inbound makes up 36%.
Although, outbound might be dead on the revenue level…
On the revenue level, inbound deals make up 53% of the revenue, while outbound deals make up 47%.
So let’s take a step back.
64% of the outbound pipeline equals 47% of the outbound revenue, meaning that the efficiency score is 0.73.
36% of the inbound pipeline equals 53% of the inbound revenue, meaning that the efficiency score is 1.47.
This shows that although inbound brings less pipeline, it definitely brings more revenue. To be exact, inbound is 2x better than outbound on the revenue side.
In the 2023 benchmarks report, we found out that the average SQO:revenue conversion rate was 22.85% – but we didn’t have the inbound and outbound split up until now. According to our data, we’re seeing that inbound deals have an average conversion rate of 28.07% while outbound deals have an average of 16.96%. This means that inbound deals have almost a 1.7x better conversion rate.
Let’s have a look at the sales cycles now. My spidey senses tell me that inbound deals should close faster since the buyer would be more familiar with the company.
Again, in the 2023 Benchmarks report, we found that the average sales cycle for B2B SaaS was 69 days, but we didn’t have an outbound and inbound split there. Now, when we split by source, our dataset shows that once the deal is created, outbound deals close in 82 days on average; while for inbound deals, this is just 54 days.
This means that inbound deals actually close 28 days faster. It’s important to highlight that, for a fair comparison, we ignored the pre-deal/MQL part here hence for both inbound and outbound deals, we’re looking at the data since the deal is created, not the MQL.
This data aligns with our findings in the Benchmark report, we’re seeing more inbound deals on the revenue side, and these deals have shorter sales cycles hence although outbound deals close in 82 days, the inbound deals lower the average to 69 days.
Have you ever found yourself looking at the outbound pipeline, spotting a familiar company, and thinking, “I’ve been seeing this company everywhere in the demographics reports”? I sure have. Lots of times.
Our data reveals that 26.8% of outbound deals had at least 5 impressions per employee in the last 30 days. So, if a company had 100 employees, that’s 500 impressions; for a company with 1k employees, it would be 5k impressions. Essentially, nearly 27% of outbound deals were influenced by Linkedin.
Additionally, 18.7% of outbound deals had at least 2 engagements per employee over the last 30 days. This means almost 1 in 5 outbound deals engaged with LinkedIn Ads before the first sales call. Obviously, we don’t know if there’s a direct correlation between employees engaging with the content and the people in the buying committee, but what we are sure about is, there’s definitely a pattern.
If we remove the minimum engagement filters, things get even more interesting. 88% of the outbound deals actually had at least 50 impressions in the last 30 days, and 69% of the outbound deals actually had at least 10 engagements with an ad in the last 30 days. However, I don’t think this is a reliable metric, and I won’t be using this.
Let’s have a look at the sales cycles now. The question is if ad impressions speed up the outbound deals. So, if an outbound deal had seen ads or engaged with ads before becoming an opportunity, does this mean they will move faster?
The short answer is yes.
Our dataset shows that the average sales cycle of those 26.8% of outbound deals that were exposed to LinkedIn ads is 76 days; and the average sales cycle of those 18.7% of outbound deals that engaged with LinkedIn Ads is 74 days.
As highlighted in the first part, the average sales cycle of the outbound deals was 82 days. This means that outbound deals that were not exposed to ads before have an average sales cycle of 84 days, it is a clear indication that seeing Linkedin Ads actually decreases the outbound sales cycles by almost a week.
On the conversion side, the average outbound conversion rate was 16.96% – but when we look at the outbound deals that were exposed to Linkedin ads, we see a conversion rate of 21.09%; and when we look at the outbound deals that engaged with Linkedin ads, we see a conversion rate of 22.50%. This shows that seeing Linkedin Ads not only decreases the outbound sales cycles but also increases the conversion rates.
What happens if an outbound deal that hasn’t seen any ads before becoming a deal starts seeing ads after the deal is created? Do open-deal campaigns for outbound deals work?
According to our dataset, the ratio of outbound deals that started seeing ads after becoming a deal is 10.02%.
Taking a step back, in the previous part, we found out that on average, 27% of the outbound deals were influenced by marketing. This leaves us with 73% of outbound deals without any ad influence. Hence, when we say 10.02%, this means that 10% of those 73% of outbound deals started to see ads after they became an SQO. This corresponds to 7.3% of all outbound deals.
Also, I’d like to point out that we are not sure if these outbound deals started seeing ads intentionally or not. What I mean is, maybe these deals began to see ads because they fell under the remarketing campaigns after visiting the website, or they were actually intentionally targeted by the open opportunity campaigns; we don’t know this.
In the previous part, we found that the average sales cycle for outbound deals was 82 days, and for outbound deals without any ad impressions, it was 84 days. However, if these outbound deals with no prior ad impressions start seeing ads after becoming a deal, the sales cycle decreases to 78 days.
This means that even if an outbound deal without any ad impressions before starts seeing ads after becoming a deal, this accelerates the sales cycle by 6 days. Moreover, if an outbound deal that was exposed to Linkedin ads before becoming a deal continues to be exposed to Linkedin ads at the same level, this reduces their average sales cycle from 76 days to 72.5 days. This equals to almost two weeks!
Unfortunately, we are not seeing anything particularly interesting on the conversion rate side. The SQO:CW conversion rate difference between outbound deals that weren’t exposed to Linkedin Ads versus those that were exposed to Linkedin ads after becoming a deal is just 2%. The latter group converts better, but not at a statistically significant level.
I believe most marketers were aware that their activities were influencing outbound deals, I’m pleased that we now have some data to back up this hypothesis. This report shows that on average 27% of outbound deals are heavily influenced by marketing activities; and these deals close faster and they have better conversion rates.
But regardless, this report shows that even if an outbound deal has had no ad impressions before, by targeting them after becoming a deal, you can still speed up the sales cycle; open opportunity campaigns are real! And B2B SaaS companies definitely need to focus more on these campaigns.
I believe the most important takeaway here is that this report demonstrates that marketing and sales should collaborate when targeting accounts; silos between teams not only create friction but also decrease conversion rates and lengthen sales cycles. Sales teams should pay attention to accounts targeted by marketing, and marketing teams should monitor accounts targeted by sales. This report highlights the significance of allbound activities over splitting the funnel. Efficient growth will not come from department-level efforts but from an allbound approach.
Emir talks about our journey of building The Flow and its revenue influence.
.svg)
.png)
.png)
