ACV, Sales Cycles, and Sales Reps
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We decided to steer the direction of the Labs theme for this analysis and dedicate this one solely to sales. Specifically, we looked for three answers:

– What’s the average pipeline generated by a sales rep?

– What’s the relationship between deal size and sales cycles?

– What’s the relationship between deal size and sales reps?

This time, it’s not going to be a 10-page report, but it’s still going to reveal some interesting stuff.

TLDR

  • The average pipeline generated by SDRs in B2B SaaS is $384K per month.
  • The length of sales cycles explains only 27% of the variance in deal size, meaning that deal size alone doesn’t have a massive impact on the length of sales cycles.
  • Although it’s a weak one, there is indeed a relationship between the number of sales reps and the average deal size.

Definitions and Methodology:

Deal Size:

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

Pipeline:

Total Deal Created, both inbound and outbound.

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.

Sales Reps:

SDRs, BDRs, and MDRs.

Pipeline per Sales Rep:

Total pipeline generated divided by the number of sales reps (inbound and outbound)

Sample Size:

54 B2B SaaS companies from the US, UK, and Canada, between 50 to 10k+ employees, and between 10 to 400+ sales reps.

Analysis method:

I preferred regression analysis over other models to understand the relationship between a dependent variable and one or more independent variables. This approach was chosen for its ability to quantify the impact of these variables on each other, offering insights into how they interact within the sales process. By applying regression analysis, we aimed to uncover whether there’s a tangible link between expanding the sales force and achieving higher contract values or if other factors play a more significant role in influencing deal sizes.

Part I: Pipeline by Headcount

In 2021 research, The Bridge Group found out that the median pipeline generated per SDR was $3 million/year, which equals $250k/month (inbound and outbound together)- that research was not specifically done in B2B SaaS but it was at least providing a benchmark. Although thinking about the companies I worked for, I can clearly say that the average I saw was definitely higher.

Pipeline generated per SDR in 2021 was $250K/month, while in 2023 it grew to $384K/month

According to our data set, the median pipeline generated per SDR is $4.6 million/year, which equals $384K per month. This number is >50% more than what the previous research found out.

So what could’ve changed?

Firstly, we have to keep in mind a couple of things:

– The Bridge Group research was not specifically B2B SaaS; it was B2B in general.

– It was 2021 – right in the middle of COVID. But also, it was pre-”we need to be more efficient” era as most companies were still happy to burn the VC money.

– The median ACV was $55K; in our case, it’s $20.8K. Theoretically, we can assume that SDRs generate faster but smaller deals.

Part II: What’s the relationship between Deal Size and Sales Cycles?

Everyone believes that if your ACV is high, then you must have long sales cycles; or vice versa, if your ACV is smaller, then you must have short sales cycles.

Is this necessarily true? Especially the second part, should a $10K ACV product always have short sales cycles?

This is the part where regression analysis starts, and I’ll try to make this sound as less nerdy as possible.

R-squared (R²) value measures the proportion of the variance, and a higher value (closer to 1) suggests a stronger relationship between the variables.

For example, if it was above 70%, it’d have been considered a strong relationship. Between 50% to 70% would be moderate And between 30% to 50% would be weak.

To illustrate, imagine if we were looking at the relationship between the amount of ice cream sold and the number of sunny days. If our R-squared value was 75%, this would indicate a strong relationship, suggesting that sunnier days lead to more ice cream being sold. Conversely, if the R-squared value were only 30%, it would suggest that the number of sunny days is not a significant predictor of ice cream sales.

In our case, R-squared is 26.8%.

The length of sales cycles explains only 26.8% of the variance in deal size

This indicates that the length of sales cycles explains only 26.8% of the variance in deal size for our dataset, and suggests that deal size doesn’t have a massive impact on the length of sales cycles. With all fairness, this is surprising to me.

However, a caveat here is that we excluded one-off deals with above $500K ACV, we also excluded deals with less than $5K to not skew the average.

What’s more surprising is to see companies with above $100K ACVs closing deals in 60 days. What I think here is that product complexity plays a bigger role than deal size – if the product requires multiple teams or complex integration, then the sales cycle gets longer.

Part III: Do more sales reps mean more ACV?

I actually heard this question on the This Week in Startups Podcast by Jason Calacanis – the discussion was that as startups grow, they try to increase their average deal sizes because their investors expect hypergrowth, and the only way to hit their goals is to double or triple their deal sizes.

Then the discussion was about when companies grow their sales teams, does this necessarily mean that they are trying to move to enterprise, or are they just trying to increase their pipeline?

So, can we necessarily assume that as companies grow, they focus more on increasing the contract values? Data says not that much.

We found out that there’s a 36% variance in deal size that can be explained by the number of salespeople.

There is a 36% variance in deal size that can be explained by the number of sales people

Let’s go back:

Strong relationship: 70%

Moderate relationship: 50-70%

Weak relationship: 30% – 50%

This means that although it’s not strong or moderate, there’s indeed a relationship between the number of sales reps and the average deal size. The first R² value for sales cycles and ACVs was less than 30%, hence meant that there’s almost no correlation – however, here we are seeing one.

We still cannot say that as companies grow, they increase their prices; however, we can definitely say that as companies grow, they don’t not increase their prices.

For instance, in the sample set, we can see companies with 300 sales reps and an average of less than $10K ACV, but we also see companies with 10 sales reps and an average of $100K ACV.

Conclusion

Contrasting with The Bridge Group’s 2021 findings, our data showcases a median pipeline per SDR of $4.6 million/year, exceeding previous benchmarks by over 50%. This increase could be attributed to differences in market focus, the impact of the COVID-19 pandemic, and lower median ACVs facilitating faster, albeit smaller, deals.

The regression analysis further challenges conventional wisdom, showing that deal size explains only 26.8% of the variance in sales cycle lengths, indicating a weaker correlation than expected. This suggests that factors such as product complexity might play a more significant role in determining sales cycle durations than the deal size itself.

Moreover, the relationship between the number of sales reps and average deal size, while present, is weaker than anticipated, explaining only 36% of the variance. This observation suggests that while there’s a trend towards achieving higher ACVs with larger sales teams, it’s not a definitive outcome for all organizations.

This was our first sales-first report, I hope you find it insightful. Please feel free to reach out with any questions or requests for future reports.

WRITTEN BY
Canberk Beker
Head of Growth at HockeyStack
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