B2B SaaS companies spent more than 45% of their paid media budget on Linkedin Ads in 2023; the current data shows that this will increase to 50% in 2024, which means that Linkedin will exceed Google Ads and become the most budget-allocated channel. That being said, there are still lots of opinions about Linkedin Ads being expensive and why Google Ads should be used to generate conversions. I’m not going to write about why Linkedin is the most suitable channel for B2B for the sake of SEO. If you’re reading this, you must already have an idea.
As Linkedin is getting more and more budget, we decided to publish an entire benchmark report to find out how spend changes month-over-month and quarter-over-quarter, how this change impacts the in-platform metrics, MQLs, pipeline, and revenue.
This dataset includes more than 70 B2B SaaS companies and a total Linkedin Ads spend of $28M from January 1, 2023, to December 31, 2023. We’re writing this report in mid-March, where now I can confidently say that December deals have become SQOs and closed, as most of the sales cycles are completed, so we shouldn’t have any problems with the cohorted numbers.
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 70 B2B companies, all B2B SaaS.
Sample Description: From $5M ARR to $1B ARR; average ACV from $5K to $120K. From January 1, 2023, to December 31, 2023, for spend and MQL data, until February 29, 2024, for pipeline and revenue data.
Attribution Model: Position-Based; we did use the linear model with the impression-level tracking in some instances, those instances are highlighted in the report.
Companies allocated the most budget to Linkedin in Q4. More than 30% of the budget was spent in Q4 – I think this comes as no surprise, as it is almost an industry standard now that teams ramp up their spend by the end of the year to maximize their revenue before the end of the year.
Q4 was followed by Q1 where we see an average of 27.47% budget allocation; again, I don’t think this is a surprise.
As soon as the start of the new year, teams want to maximize their top of funnel conversions so that their sales teams can close the deals throughout the first and second quarters. So in Q4, the intent for higher spend is to maximize the revenue before the end of the financial year, I call it the Holidaymania. In Q1, we have two types of intents for high spend. The first one is to maximize the revenue before the end of the financial year as most of the UK companies end their financial year at the end of March; and the second intent is to fill the the top of the funnel conversions and give enough conversions to sales people for the rest of the quarter.
Q4 was followed by Q1, where we see an average of 27.47% budget allocation; again, I don’t think this is a surprise. As soon as the start of the new year, teams want to maximize their top-of-funnel conversions so that their sales can close the deals throughout the first and second quarters. So, in Q4, the intent for higher spend is to maximize the revenue before the end of the financial year. In Q1, we have two types of intents for high spend. The first one is to maximize the revenue before the end of the financial year, as some companies (especially in the UK) end their financial year at the end of March; and the second intent is to fill the top of the funnel with conversions and give enough conversions to salespeople for the rest of the quarter.
For me, the surprising part was to see that Q3 had a bigger budget allocation than Q2. I know that most teams increase their spend in September, but I also know that July and August are mostly dead months; I’d expect to see Q2 get more budget than Q3, as I generally see significantly more pipeline created in Q2 than Q3. For this reason, I wanted to get a more granular approach and split this spend data by months.
Looking at the spend on the monthly view, we get a better understanding. Ironically, the most spend happened in February (although it’s 3 days shorter than March), followed by November, December, and January. I believe the main reason to maximize spend in February is to maximize the number of deals for March, before the end of Q1. We can support this hypothesis by looking at the March spend data where it’s only 4.58%, and it’s the month with the lowest budget allocation.
Let’s take a step back, almost 25% of the total Linkedin ads budget was spent on the first two months of the year, January and February, most likely to maximize the deals and revenue for Q1. In March, we’re seeing the lowest spend, most likely because most teams are aware that they can’t really influence the Q1 pipeline anymore and maybe start planning for the next quarter. I want to highlight that, this is not something I recommend or I do; this is what this data shows us. For me, demand creation is dynamic; you constantly work on creating demand as the demand you create in March can be captured in Q2.
Another interesting thing is that we don’t actually see a huge increase in spend in April when Q2 starts; the budget allocated for April is slightly higher than the budget allocated for March. We’re witnessing the jump in May; the spend increases by over 50% in May compared to April. We’re seeing a similar pattern in Q3 where spend in July decreases, then it increases in August, and skyrockets in September.
Although October is the beginning of the quarter, similar to what we’re observing for January, the spend doesn’t decrease; most likely because teams are aware that the possibility to close is very high in Q4 and Q1. Therefore, they try to maximize their resources.
I find it a bit surprising that December is the third highest month in terms of budget allocation since it’s basically a half-month considering Christmas and the new year. I mostly recommend decreasing the budgets significantly after December 20th because most people are either on holiday or mentally checked out; hence, for me, December is generally the month where we have the least spend. But according to our dataset, we’re seeing a different picture.
For the sake of simplicity, I’m not going to split this data and check the spend day by day in December to see if the trend is to maximize the spend until the Holidays; however, I don’t think this is the case. I think it’s rather to spend the remaining yearly budget before the end of the year – why? Because if you don’t spend your budget, then finance gives you less budget next year since they will think you don’t need the budget as you didn’t spend all of it. I suppose this is the main reason why: teams spend more in December because they have to.
Now that we have visibility on the in-platform metrics, let’s have a look at how the audience responds to these ads throughout the year.
The first question is, is there a relationship between high spend and CPC? We all know that ad platforms work with the auction model, hence if more companies are spending more money, the costs will increase. Therefore, we should be able to see higher CPCs in Q1 and Q4, and lower CPCs in Q2 and Q3 – right?
Not quite. We don’t really see a strong relationship between the increase in spend and high CPCs. In Q1, where the spend is the second highest, we are seeing the lowest CPC.
We’re seeing the highest CPC in Q3 with an average of $15.72, which means that CPC increases by almost 1.5x in Q3 compared to Q1. I would normally think that CPCs are cheaper in Q1 because there’s less competition, but we’re seeing that companies actually spend on Linkedin in Q1, it’s definitely not because of less competition. Maybe we need to take into account other factors, like the audience’s intent. Maybe at the beginning of the year, audiences are more likely to click on ads than the rest of the year – could this be the case?
We need to look into the CTR metrics to come to any conclusions.
So, can we say that people on Linkedin are more likely to click on ads in Q1, which decreases the CPCs although the spend is high? The answer is no.
Our dataset shows that although Q1 has the lowest CPC and the second-highest spend, the CTR in Q1 is the lowest with an average of 0.82%. We’re seeing the best CTR in Q3 with an average of 0.96%, and September seems to be the best month when it comes to CTR with an average of 1.05%.
As highlighted in the first part, B2B SaaS companies spend the most on Linkedin in Q4 and Q1, but how does this influence MQLs, pipeline, and revenue?
On the MQL side, we’re observing that 27% spend in Q1 brings 27% of the MQLs, so basically, companies are getting what they are paying for.
We’re seeing the most cost effective MQLs in Q2, where companies spend 18% of their budget and generate 30% of the MQLs. Maybe this is the reason why we’re seeing a low investment in Q2 although Q2 is known to be a profitable quarter – most marketing teams are still measured by the number of MQLs, hence in Q2, maybe they hit their MQL goals with less investment, therefore, they don’t feel the need to spend more. This is my assumption at this point.
In Q3, we’re seeing a similar trend as we saw in Q1. Companies spend 22% of their budget in Q3 and generate 22% of their MQLs, so again, they are getting what they are paying for.
However, the inefficiency starts in Q4; although the most budget is spent in Q4, 31% spend brings only 20% of the MQLs. Hence Q4 is the quarter where the spend is the most but also where the number of MQLs is the least. I think we can talk about the impact of auctions here, as more companies spend their budget, the cost per MQL gets more and more expensive.
Our dataset shows that, in terms of the cost per MQL, Q2 is the best quarter, followed by Q3, Q1, and Q4. With actual numbers, the ratio of spend to MQL in these quarters is the following:
Q1: 99.3%
Q2: 161.9%
Q3: 100.8%
Q4: 63.8%
But we all can agree, hopefully, that looking at the number of MQLs alone is not enough. What do we see on the pipeline level?
In the Benchmarks report, we found that the MQL:SQO cycle was shorter in Q1 and Q2, where MQLs generated in these quarters become SQOs within the next 30 days, and mostly within the same quarter. However, in Q3, we were seeing that MQL:SQO duration gets around 1.5x longer.
Which showed that:
– MQLs generated in Q2 become SQOs in Q2.
– MQLs generated in Q3 become SQOs in Q4.
– MQLs generated in Q4 become SQOs in Q1.
– MQLs generated in Q1 also become SQOs in Q1.
This was the general inbound data – but we’re not seeing a massive difference when we look at the Linkedin data. Linkedin conversion data aligns with the overall inbound data except for one thing. We’re seeing that the MQL:SQO cycle is longer in Q2, not as long as in Q3 and Q4 but also not as short as Q1. Hence, MQLs generated in Q2 become SQOs in Q2 AND Q3.
On average, in Q1, MQL:SQO duration is 24 days. MQL:SQO duration in Q2 is 45 days. MQL:SQO durations in Q3 and Q4 are 62 and 68 days. In the recap report, we found out that the average sales cycle was 69 days – this indicates that non-Linkedin conversions move between stages much faster.
This also means that when we look at the Q1 MQLs, we need to look at the Q1 pipeline; when we look at the Q2 MQLs, we need to look at the Q2 and Q3 pipeline; when we look at the Q3 MQLs, we need to look at the Q4 pipeline; and when we look at the Q4 MQLs, we need to look at the Q1 pipeline.
We’re seeing the best pipeline in Q3 where 32% of the total Linkedin pipeline was created. Considering that the most MQLs were generated in Q2 with 30%, we can clearly see that these MQLs become SQOs in the next quarter. Hence, the spend in Q2 seems to be the most efficient spend; we’re seeing an 18% budget allocation bringing 30% of the MQLs in the same quarter, and 32% of the pipeline in the next quarter. What’s even more interesting is to see the revenue data. In terms of revenue, as everyone might expect, Q4 is the best quarter with an average of 35% revenue. I think it’s safe to assume that the pipeline generated in Q3 shows its impact in Q4.
This is interesting because in the recap report, what we found was that the worst quarter for pipeline was Q3 and the best quarter for pipeline was Q1 – for Linkedin, we can’t say this. On the contrary, the best quarter for pipeline is Q3, then Q4, and then Q1.
Let’s keep analyzing the pipeline side. While the best quarter for pipeline is Q3, the worst quarter for pipeline seems to be Q2 where we are seeing a total of 20% created. However, I think we need to consider other implications here. In the previous paragraph, I mentioned that MQLs generated in Q1 become SQOs in the same quarter; while MQLs generated in Q2 become SQOs in the same quarter and in the following quarter. Hence what we’re seeing is actually not a low pipeline, but a pipeline that hasn’t been created yet.
So if we take cohort into account; in Q3, 22% of the spend brings 22% of the MQLs, and these MQLs become pipeline in Q4 and make up a total of 24% of the total pipeline. Whereas in Q2, 18% of the spend brings 30% of the MQLs, and these MQLs become pipeline in Q3.
In terms of monthly pipeline data, we’re seeing the best pipeline in July with 11.7% – this is very interesting for me, considering that it’s the summer season and right after Q2. July is followed by February with 10.39% and September with 10.2%. The worst month when it comes to pipeline seems to be January with only 5.8% of the pipeline generated – so this data shows that July had 2x better pipeline than January.
In terms of spend:pipeline ROI, the average ROI for Linkedin Ads is 1.84. We’re seeing the best ROI in July with 3.25, followed by June with 2.53 and March with 2.44. The worst ROI was in January with 0.78. However, it’s important to highlight that this ROI is calculated with the position-based model where 40% of the credit is given to the first interaction, recognizing the importance of attracting a potential customer. Another 40% is attributed to the last interaction, acknowledging the final decision-making step. The remaining 20% is evenly distributed among the middle interactions, covering the nurturing phase where the customer is considering their options.
What happens if we include the impression-level data? So what happens if we don’t just track clicks, but we track the impressions? Then we see the true impact of Linkedin. Once we remove the dependency on clicks and start looking from the impression level, we’re seeing that the average ROI for Linkedin Ads is 6.01, meaning that for every $1 invested in Linkedin, companies create $6 worth of pipeline – so although those conversions were originally seen as organic, direct, or PPC and didn’t come directly from Linkedin Ads – they were influenced by Linkedin Ads in the last thirty days.
Although this wasn’t planned initially, I reckon this is such a good use case to show the importance of impression-level targeting – if we use Linkedin to track direct conversions, it will probably show lower ROI than Google Ads; but once we reveal the impression-level data, we get to see the true value of it.
In the Benchmarks report, the dataset showed that the best quarter for revenue was Q1 followed by Q2, and Q4. Similar to what we’re seeing on the pipeline level, the Linkedin dataset doesn’t align completely with the total revenue data. When it comes to revenue from Linkedin Ads, Q4 seems to be the best month, followed by Q1, while Q2 is the worst month.
36% of the total Linkedin Ads revenue was added in Q4, followed by 26% in Q1, 21% in Q3, and 18% in Q2.
When we take a step back and think about it, it kind of makes sense. Linkedin isn’t a capture demand channel; it’s rather a create demand one, therefore it takes longer to generate pipeline and revenue – we can also prove this with the length of sales cycles.
Another interesting thing arises when we look at this data on the monthly view; while on the total revenue side, October was one of the worst months; for Linkedin Ads, it’s the best month where 16% of the total revenue was added. It’s followed by January with 15%, and December with 13%. October and December make up a total of 30% of the total revenue – it’s crazy. On the other hand, we’re seeing the worst revenue in April with just 1.6%.
When we look at the Pipeline:Revenue conversion rate, our dataset shows that the average is 34.8%, so more than 1 out of 3 SQOs created by Linkedin Ads actually become revenue.
In terms of the spend:revenue ROI, the average ROI of Linkedin Ads is 0.71 – but again, it’s important to highlight that we’re tracking clicks here with the position-based attribution. Once we apply the impression-level tracking here, the ROI goes up to 2.46 – so for each $1 spent on Linkedin, companies add $2.46 in net revenue. Another important thing to note here is the Pipeline:Revenue conversion rate. When we look at the clicks, this rate was 34.8%, but when we look at the impression level, this conversion rate goes up to 41%. This proves that not only do Linkedin Ads work, but the impressions actually positively impact the conversion rates even though there are no clicks.
For me, it was really interesting to see that most of the Linkedin budget is being spent in December, while April and May get only small percentages. But also, what we are observing is that December seems to be the worst month for top-of-the-funnel conversions, and my recommendation is to distribute the budget more evenly across the months rather than ramping up in December.
Another interesting insight was the lack of a strong relationship between high spend and high CPCs, indicating that CPCs are not necessarily about high spend after all. However, according to our dataset, there’s definitely a correlation between the cost per MQLs and high spend on Linkedin.
Linkedin is definitely a long-term game; we might be seeing longer sales cycles, but we’re also seeing stronger ROIs for pipeline and revenue, as well as better conversion rates from SQO to revenue. This report once again shows us that to understand the actual impact of LinkedIn, we need to move from click tracking to impression tracking.
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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