With all honesty, email marketing hadn’t been a focus for me for years until early this year. When I used to do lead-gen, we would send nurture emails and use email marketing as a form of “intent warmer.” We would either score leads by their email interactions or set a time delay before sending them over to sales. Then at Cognism, email marketing didn’t fall under my responsibility, so my exposure to it was limited. However, I knew for a fact that we weren’t tracking the end-to-end email impact on HockeyStack during my tenure at Cognism. I reckon email marketing is one of those things you forget to use if you don’t see massive success from it. It’s like running a display ad—it’s been a long time since I experimented with it, and it wouldn’t be my first choice in my role.
The idea of publishing this very report about email marketing came from our success in email marketing strategy, which we began after seeing the numbers in the “What Does it Take to Close” report. In that report, we discovered that email marketing was still very effective and significantly accelerated sales cycles. Without seeing that data, working on an email marketing strategy wouldn’t have been in my top five priorities. But the data was there, and we had to test it.
Based on the findings in the “What Does it Take to Close” report, we built our email marketing strategy in late February. The image below shows how email marketing is now contributing to our pipeline—it’s directly from HockeyStack’s own HockeyStack account.
This reverse funnel shows that email marketing has already become one of our major revenue drivers within the last three to six months. Hence, I thought we could dedicate this week’s report to email marketing and analyze how emails actually influence the buyer journey and revenue.
- 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.
- Meeting Booked/MB: If a meeting is booked but has not happened yet.
- 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.
- Sample Size:
- 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.
- Date Range: From January 1, 2023 to March 31, 2024.
- Sample Description:
According to Hubspot’s Research in 2023, the average email open rate was 46%-50% and the CTR was 2.6%-3%. We need to take these numbers with a grain of salt as we have no idea about the sample size, industry, or even if those companies were B2B or B2C or blended. Maybe that’s why they didn’t use an average but gave a ballpark figure.
Our numbers are a bit different from Hubspot’s. The dataset shows that the average open rate is 21% which is more than half of what Hubspot had. When we split this open rate by inbound and outbound, we see that inbound emails have an open rate of 25% whereas outbound emails have an open rate of 15%.
I believe there are a few things we need to cover here:
- Both inbound and outbound open rates are flawed due to security and privacy regulations and tools that automatically send the “opened” data to CRMs.
- It makes sense that outbound has a lower open rate as outbound emails are often sent to people who don't necessarily show intent.
- Another reason for outbound having a lower open rate could be the email deliverability issue. It’s a vicious circle: outbound emails getting bounced or deleted immediately, this impacts deliverability, hence these emails get caught in spam filters more often.
So we need to look at the data with all of the above in mind. According to this data, inbound emails have a 1.6x better open rate than outbound.
CTR is generally a more reliable metric as it measures actual user interaction with the content. Although sometimes automated systems can click on links to check for malicious content, falsely inflating CTR, it’s not as common as open rate inflations. Therefore, CTR is typically a more accurate reflection of user engagement compared to open rates.
On a blended view, we’re seeing an average of 3.1% CTR; this is actually almost identical to what Hubspot shared.
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While inbound emails have an average CTR of 4.1%, outbound emails have an average of just 1.67%. This shows that inbound emails have almost a 2.5x better CTR than outbound emails. Is this surprising? Not really.
The first thing I want to start with is the email splits in the buyer journey.
According to our dataset, 67% of emails were sent to people who haven’t visited the website in the last 90 days. This could include people who have never visited the website or those who last visited it 91 days ago or more. We used 90 days as a benchmark because it equals a quarter, and it is safe to assume that if there is no website visit in the last 90 days, there’s most likely no intent.
Next, 27% of emails were sent to people who’ve been on the website but haven’t booked a meeting or submitted a demo/pricing form. These people visited the website in the last 90 days, and they might have checked one page and left, downloaded an ebook, or visited high-intent pages, but they or their companies haven’t booked a demo.
So, 94% of the emails were sent before any pipeline qualification, leaving only 6% for the rest of the buyer journey. One might argue that since the number of deals or meetings booked is significantly lower in later stages, that 6% is just a representation of that low number; however, I don’t agree with that. Let’s do a simple math:
Let's break down the assumptions and calculations:
1. Assume every email sent was for lead generation, not demand generation or high conversion rate campaigns.
2. Lead gen conversion rate: 4.93%
3. Number of inbound emails: 8M
- 8M emails * 4.93% = 394K SQLs
4. Number of outbound emails: 13.5M
- Outbound email conversion rate: 2.1%
- 13.5M emails * 2.1% = 283.5K SQLs
5. Total SQLs: 394K (inbound) + 283.5K (outbound) = 677.5K SQLs
6. 6% of emails correspond to 1.3M emails
Given this, each SQL would receive approximately 1.9 emails (1.3M emails / 677.5K SQLs) even if we assume the inbound conversion rate is the lowest.
This calculation indicates that there is almost no lifecycle or open-opportunity email marketing strategy used by companies. Instead, it appears that most email marketing efforts are focused on top of the funnel with minimal follow-up or nurturing through the lifecycle stages. This highlights a potential area for improvement, as implementing a more robust lifecycle email marketing strategy could enhance engagement and conversions throughout the buyer journey.
*Please note that we have excluded emails sent by sales after a deal is created. Instead, we’re looking at the email marketing strategy once the deal is created. But it seems like most marketing teams just abandon the prospect as soon as sales qualifies them.
In our State of Revenue report, our dataset showed that email marketing contributes 0.43% of the MQLs on the first touch, 1.08% on the last touch, and 1.16% according to the self-reported attribution answers. So, with all these three data points together, email marketing contributed more or less 1.1% of the total MQLs. On the revenue side, 1.1% of these MQLs contributed to 2.24% of the revenue.
Now, we’ve increased the sample size almost by 5x in this report, and we’re still seeing a similar contribution. Across over 150 companies, email marketing is contributing 1.3% of the total MQLs. As we were able to replicate this data in a different report with a slightly different timeframe and 5x more companies, I think it’s safe to say that in B2B SaaS, email marketing contributes to an average of 1.1%-1.3% of the total MQLs.
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Needless to say, this was for inbound emails - we’ll look at the outbound data in the next part when we analyze the revenue section.
In this section, we’ll be looking at a couple of things, starting with the MQL:SQL conversion rate of email marketing. According to our dataset, email marketing MQLs have an 11.3% better conversion rate compared to the blended average.
Furthermore, we’re seeing a 2x better conversion rate than Facebook, a 1.6x better conversion rate than Bing, and a 1.2x better conversion rate than Google Ads; however, compared to Linkedin Ads, the conversion rate is 19% lower.
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On the ACV side, the figures are almost identical. Email marketing MQLs have only a 1.2% lower deal size and their sales cycle is 7% faster than the inbound average. We’re seeing similar results in outbound email deals, with the deal size and sales cycle difference in outbound deals created by outbound emails being only 1.5% different than the outbound average in general. Therefore, it’s very safe to say that email marketing or outbound emails have extremely average time to close and deal sizes.
On the close rate side, the close rate of outbound email deals is identical to other outbound deals. However, on the inbound side, we’re seeing that email marketing deals have a 37% better conversion rate compared to the inbound close rate average.
So, even if the ACV seems identical, the higher close rate means email marketing contributes to revenue more effectively than the average inbound MQLs. This data also aligns with our findings in the State of Revenue report, where 1.16% of email marketing MQLs correspond to 2.24% of the revenue.
This high close rate be because of several factors:
-Familiarity: Getting emails consistently from a company can establish trust (or hate if the emails are unauthentic and CONSTANT). However, if the email audience has been engaging with the emails, they are likely already familiar with the product, and this might lead to better conversion rates.
- Better targeting and segmentation: If done right, email marketing could actually allow for better targeting and segmentation, and theoretically, companies can send highly segmented messages which could increase the effectiveness.
So, in a nutshell, we’re not seeing any groundbreaking data when it comes to deal sizes and time-to-close when we look at the email marketing MQLs or outbound deals created by emails. This situation made me look at the other side of the data, the ignored part—the 6% part—in hopes of finding some interesting insights.
When we look at the emails sent after the deal is created (covering all deals, whether created by emails or not, and including both inbound and outbound), we finally see something interesting.
If marketing sends more than 8 emails between the deal creation and closure, the close rate increases by 47%. If marketing sends between 4-8 emails, the close rate increases by 29%. However, the close rate doesn’t change if marketing sends no emails or fewer than 4 emails once the deal is created.
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For inbound deals, sending marketing emails seems to be 8% more effective than for outbound deals. I believe this is because inbound deals are more likely already familiar with marketing efforts; because if they are inbound, they probably came through marketing channels. On the outbound side, familiarity with marketing is lower, which might cause the 8% lower effectiveness. However, regardless of whether the deals are inbound or outbound, email marketing indeed helps close deals.
Another interesting thing is that, if marketing sends more than 5 emails after a deal is created, we see an average of 13% faster sales cycles, while the deal size doesn’t show any significant change.
I think the most critical insight from this report is the lack of lifecycle/ open-opportunity email marketing strategies used by most companies. It’s really interesting that 94% of the emails were sent before any pipeline qualification. The report shows that most email marketing efforts are concentrated at the top of the funnel, with minimal follow-up or nurturing through later stages of the buyer journey. Implementing a robust lifecycle email marketing strategy could greatly help the customer journey since our data indicates that sending emails after a deal is created significantly boosts close rates and speeds up the sales cycles.
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