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Though search funds have historically acquired companies within countless different industries, software has been among the most popular and sought-after industries among searchers for many years now. This makes sense, in part due to the overlap between the software business model and the characteristics that search funds typically seek out in acquisition targets (recurring revenue profiles, asset-light operations, capital-light growth opportunities, sticky products with high switching costs, and so on).
For this reason, much has been written about software within the search fund ecosystem, and many software studies (similar to – but almost certainly better than – the one that I’ve presented below) have been compiled and presented by my peers over recent years (a 2022 study conducted by Endurance Search Partners and Applied Equity is particularly good, as is a 2021 study conducted by TTCER Partners and Banyan Software). I’m hopeful that the study that I present here is complementary to the efforts of these and other investors who have already been so generous with their time, efforts and insights.
I chose to conduct my own survey for two primary reasons:
(1) To better understand the complexion of the “typical” software acquisition within the search fund ecosystem; and
(2) To see whether or not the the complexion of the “typical” software acquisition has changed over time
Over the past few years, I’ve grown to suspect that the typical SaaS acquisition consummated after 2019 has changed quite materially from the typical SaaS acquisition consummated prior to 2019. More specifically, I’ve observed that many of the post-2019 acquisitions seemed to resemble growth equity transactions more than they did traditional buyout transactions (something I elaborate on further in another blog post, Evaluating 5 Very Different Approaches to Acquiring a Software Company). Prior to conducting this study, this opinion was based entirely on anecdotal information.
Now that I have pressure-tested this thesis (to the best of my limited abilities), I feel reasonably safe in concluding that, yes, the profile of the typical SaaS acquisition has indeed changed over the past few years. The remainder of this blog post will walk you through how and why I arrived at this conclusion.
The Data Set
Statisticians the world over will be tremendously disappointed to learn that my observations below are based on the responses of 41 CEOs who have acquired SaaS companies through the search fund model. Though they will (rightly) argue that my sample size is too small to produce statistically significant results, I would still suggest that, at the very least, the results should provide both searchers and investors with food for thought.
The year in which each target company was acquired is detailed in the chart to the right. As can be seen, we were able to achieve reasonably good diversity by vintage year, with 18 of the 41 transactions taking place in 2019 or earlier, and 23 of the 41 transactions taking place after 2019.
For each variable listed below, we present a summary of our survey responses, and where applicable, discuss if and how certain variables have changed across the two cohorts studied. In Section 1, we analyze several metrics at the time of closing. In Section 2, we analyze various post-closing considerations.
Section 1: Purchase Metrics
All Acquisitions: Across the entire data set, searchers were most likely to acquire their companies for a multiple between 3x – 4x ARR (29% of total). The second most likely range was 2x – 3x ARR (24% of total).
Comparing the Cohorts: Almost all of the of the 3x – 4x acquisitions took place after 2019. Prior to 2019, deals were much more likely to trade within the 2x – 3x ARR range. Indeed, prior to 2019, 56% of searchers acquired their business for a multiple of 3x ARR or lower. After 2019, only 30% of searchers paid 3x or lower.
Multiple of Total Revenue
All Acquisitions: Across the entire data set, on a total revenue basis (including all sources of revenue, both recurring and non-recurring in nature), the top of the bell curve occurs between 2x – 3x total revenue.
Comparing the Cohorts: Prior to 2019, 44% of searchers acquired their business for a total revenue multiple of 2x or lower. After 2019, only 17% of searchers paid 2x or lower.
Conclusions: Expressed as a multiple of both ARR and total revenue, it appears as if search funds have indeed been paying more robust valuations for SaaS companies since 2019. It is worth noting however that: A) This multiple expansion is consistent with broader market multiples; & B) As we’ll see in more detail below, the profile of the average company acquired after 2019 is quite different from the profile of the average company purchased prior to 2019.
All Acquisitions: Across the entire data set, the most frequent response to our question of the EBITDA acquisition multiple was “not a meaningful number”, which suggests that the multiple was so high that it did not convey any meaningful insights. However:
Comparing the Cohorts: There appears to be a reasonably large disparity between the two cohorts: Prior to 2019, 55% of transactions took place at an EBITDA multiple of 8x or less. After 2019, only 30% of transactions fit that description. What’s more, after 2019, ~40% of all transactions took place at an EBITDA multiple that “was not meaningful”. That same figure for the pre-2019 cohort was only 11%.
Conclusion: The basis for valuation has shifted from predominantly EBITDA-based (in 2019 and prior years) to predominantly revenue-based in 2020 and beyond. Some of our analysis below digs deeper into why and how this shift might have occurred.
All Acquisitions: Across the entire data set, I was actually somewhat surprised to see that the top of the bell curve appeared between 10-30% EBITDA margins (24% of companies purchased were generating EBITDA margins between 10-20%, and a further 24% of companies purchased were generating EBITDA margins between 20-30%). This profitability profile is actually much higher than I would have initially suspected.
Comparing the Cohorts: The data across the two cohorts appears to be somewhat mixed:
- On one hand, search funds after 2019 were much more likely to purchase companies that were either barely profitable or not profitable at all: In the “2019 or prior” cohort, only 6% of targets reported EBITDA margins of 0% or less. After 2019 however, 22% of targets reported EBITDA margins of 0% or less.
- On the other hand however, for those companies that were profitable post-2019 targets appeared to be more profitable than their pre-2019 counterparts
ARR Growth Rate
All Acquisitions: Across the entire data set, target companies were most likely to be growing ARR at a rate between 10% – 20% annually (29% of total responses), followed closely by 40%+ annual growth (24% of total responses).
Comparing the Cohorts: After 2019, target companies appear to be growing more rapidly: In the “2019 or prior” cohort, only ~28% of all companies purchased were growing ARR at 30% or more. After 2019, 43% of all companies purchased were growing ARR at 30% or more.
- After 2019, within the “rule of 40” equation, more weight has seemingly been ascribed to growth than to profitability;
- The increase in the growth rate of target companies may, at least in part, explain the higher valuations that have been paid since 2019.
“Rule of 40”
Definition: EBITDA margin + % revenue growth rate
All Acquisitions: Across the entire data set, 46% of all companies acquired were 40% or more on the “rule of 40” scale, representing (by a large margin) the most frequent response
Comparing the Cohorts: Though 40%+ was the most frequent answer across both cohorts, companies purchased after 2019 were slightly more likely to be 40% or more on the rule of 40 scale (52% of total responses) than those that were purchased prior to 2019 (39% of total responses). As we saw above however, this appears to be largely attributable to the revenue component of the rule-of-40 equation, not the profitability component.
Size of EBITDA
All Acquisitions: Across the entire data set, both cohorts exhibited a wide range of EBITDA values, with some target companies as small as $0-200K, and others as large as $2M+
Comparing the Cohorts: Consistent with our findings above, it appears that a greater proportion of sub-$1M EBITDA businesses were purchased after 2019:
- In the “2019 or earlier” cohort, only 39% of businesses had $1M of EBITDA or less.
- After 2019, 59% of businesses had $1M of EBITDA or less.
Size of ARR
All Acquisitions: Across the entire data set, the greatest number of companies were purchased while generating $2 – $3M in total ARR.
Comparing the Cohorts: It appears as if target companies have been getting larger since 2019:
- In the “2019 or prior” cohort, only 39% of all companies purchased were generating more than $3M in ARR at closing.
- After 2019, a full 70% of all companies purchased were generating more than $3M in ARR at closing.
Conclusion: The increase in the size of target companies (as well as the higher growth rates) may, at least in part, explain the higher valuations that have been paid since 2019.
Recurring Revenue as a % of Total Revenue
All Acquisitions: Across the entire data set, having at least 60% of total revenue coming from truly recurring sources appears to be table stakes (61% of all companies made this claim).
Comparing the Cohorts: In the “2019 or prior” cohort, only 17% of all acquisitions generated at least 80% of their revenue from recurring sources.
After 2019, however, a full 47% of all acquisitions generated at least 80% of their revenue from recurring sources. Searchers thus appear to be increasingly focused on buying businesses with a higher proportion of recurring revenue relative to total (this trend may also loosely coincide with the broader market’s gradual shift from perpetual-use pricing models to subscription-based pricing models)
Conclusion: Alongside larger revenue bases and faster growth rates, the higher proportion of total revenue coming from recurring sources may, at least in part, explain the higher valuations that have been paid since 2019.
All Acquisitions: Unlike many of the variables that we’ve presented thus far, the two cohorts appears to be more similar than different with respect to gross margin: In both cohorts, one-third of all companies acquired had a gross margin of at least 80%. Further, in both cohorts, ~70% of all companies acquired had a gross margin of at least 60%.
Comparing the Cohorts: The major difference between the two cohorts revolves around the percentage of companies purchased that were “only” generating a gross margin between 40-60%: 33% of all purchases in 2019 or earlier fit this description, but only 17% in the post-2019 cohort did. It’s difficult to accurately attribute this to anything in particular, as there is more variability than most people would think with respect to how software companies even calculate gross margin, specifically within the lower-middle market.
Gross Logo Retention Rate
Definition: “For every customer who was a recurring revenue customer last year, what % were still recurring revenue customers this year?”
All Acquisitions: Across the entire data set, gross logo retention rates above 90% appear to be more or less “table stakes” (71% of all companies boasted a GLR rate above 90%). As I’ve said in the past, I believe GLR to be among the most informative metrics for any software business, and in a hypothetical world where I could only see a single metric to evaluate the health of a given software business, gross logo retention would likely be my choice.
Comparing the Cohorts: There does appear to be a difference in retention rates between the two cohorts:
- Prior to 2019, 89% of all companies acquired had a gross logo retention rate of at least 90%.
- After 2019, only 56% of all companies acquired had a gross logo retention rate of at least 90%.
Net Revenue Retention Rate
Definition: “What percentage of my recurring revenue dollars from last year are still recurring revenue dollars this year, including the dollar value of customer upsells and downgrades?”
All Acquisitions: Across the entire data set, net revenue retention rates of at least 95% appear to be more or less table stakes (88% of all companies had NRR of 95% or more).
Comparing the Cohorts: Though the two cohorts appear to be reasonably similar, there does appear to be a slight bias towards acquiring higher NRR businesses after 2019: 35% of all companies acquired after 2019 boasted NRR of 105%+, while only 17% of companies acquired in 2019 or earlier can say the same
Conclusions: It is interesting to note that pre-2019 targets featured better GLR rates but worse NRR rates relative to their post-2019 peers. One possible conclusion is that post-2019 targets tend to grow or maintain their recurring revenues through more upsells (or fewer downgrades) from existing customers, as opposed to maintaining a higher a retention rate of the absolute number of customers served.
Definition: % of total revenue represented by the company’s largest customer at the time of acquisition
All Acquisitions: Across the entire data set, target companies were most likely to have negligible levels of customer concentration, with 0%-5% of total revenue coming from their largest single customer (~29% of all responses). Concentration isn’t unheard of however, as the second most frequent response (at ~17% of all responses) was that the company’s largest customer accounted for 15% – 20% of total revenue
Comparing the Cohorts: The data across the cohorts is somewhat mixed: On one hand, pre-2019 targets were less likely to mild have concentration than post-2019 targets, however they were also more likely to have meaningful concentration (defined as 35% of sales or more) than their post-2019 peers.
% of Transaction Funded with Debt
All Acquisitions: Across the entire data set, transactions were most likely to be funded with a minimal amount of debt (defined at 10% or less of total capitalization). This represented approximately one-third of all responses, and was largely consistent with my expectations
Comparing the Cohorts: I was however quite surprised to see that transactions that took place after 2019 were actually more likely to use more leverage than their pre-2019 counterparts:
- In the “2019 or earlier” cohort, 50% of all transactions used 20% leverage or more.
- In the post-2019 cohort, 61% of all acquisitions fit that same description.
- Given that the post-2019 cohort seems to be targeting higher-growth, lower-profitability businesses, I would have expected the post-2019 cohort to be considerably less leveraged than the pre-2019 cohort.
Total Number of Customers
All Acquisitions: Across the entire data set, companies were most likely to have at least 300 customers at the time of acquisition (37% of all responses)
Comparing the Cohorts: Companies purchased after 2019 do indeed seem to be getting larger as measured by the size of their customer bases (this is consistent with our analysis above, which suggests that post-2019 acquisitions were, on average, generating more ARR than their pre-2019 counterparts):
- In the “2019 or earlier” cohort, only 33% of companies had 150 or more customers at the time of acquisition.
- For the post-2019 cohort, 52% of all companies had 150 or more customers.
Conclusion: This consideration may, at least in part, have contributed to the higher valuations that have been paid after 2019 (under the assumption that a greater number of customers produce more ARR). It is also consistent with our observation above that post-2019 acquisitions are less likely to have meaningful customer concentration relative to their pre-2019 counterparts.
Definition: “What is the lifetime value of an “average” customer, expressed as a multiple of the cost to acquire that same “average” customer? (higher = better)
All Acquisitions: Across the entire data set, I was not particularly surprised to see that 63% of all respondents said that they either didn’t know the company’s LTV/CAC ratio, or couldn’t measure it with any real degree of precision. Though LTV/CAC is generally considered to be a “SaaS 101” type of metric, accurately measuring it in small, privately held companies with low information hygiene is often much more difficult than most might suspect.
Comparing the Cohorts: Though “I don’t know” was indeed the most frequent answer within both cohorts, for those companies where LTV/CAC could be calculated, the post-2019 cohort did seem to demonstrate better unit economics relative to the pre-2019 cohort: 35% of all post-2019 companies had LTV/CAC ratios of 5x or more, while only 11% of pre-2019 acquisitions could make the same claim.
Conclusion: Again, more favorable unit economics could have played a material role in the higher valuations that have been paid since 2019.
Cash Funded to Balance Sheet at Closing
All Acquisitions: Across the entire data set, by far the most frequent response (at ~34% of all responses) was “beyond day-to-day working capital requirements, we funded an additional $900K or more” to the balance sheet as part of closing. The frequency with which this response was provided was somewhat surprising to me, as the majority of search fund acquisitions outside of software typically require only a modest amount of cash to the funded to the balance sheet at close beyond day-to-day working capital requirements.
Comparing the Cohorts: By now it shouldn’t surprise you to hear that transactions consummated after 2019 were more likely to require a material amount of cash funded to the balance sheet relative to those consummated prior to 2019:
- 39% of all post-2019 deals funded $900K or more to the balance sheet, while only 28% of pre-2019 deals required the same.
- 39% of all pre-2019 deals funded nothing to the balance sheet beyond day-to-day working capital requirements, while only 22% of post-2019 deals could claim the same.
Section 2: Post-Close Considerations
State of the Product, Code Base, & Technical Debt:
Idea: Anecdotally, I’ve observed that many new SaaS CEOs comment on how the product that they inherited is in worse shape than they had originally suspected. I wanted to test to see whether this was indeed true, and whether it differed materially between the two cohorts.
Comparing the Cohorts: For those companies purchased in 2019 or earlier, a full 72% of CEOs reported that the state of the product, code base, and technical debt was in worse shape than they had suspected. Only 30% of post-2019 CEOs shared that same sentiment. Indeed, in the post-2019 cohort, CEOs were much more likely to be reasonably satisfied with the state of their products, with ~70% of respondents reporting that the product was “about the same as I had thought” or “better than I thought”.
Conclusions: Though the magnitude of the delta between the two cohorts was higher than I would have thought, I wasn’t necessarily surprised to see that post-2019 targets tended to have better quality products/code bases than their pre-2019 counterparts. This could be due to a combination of a) Better pre-purchase product due diligence performed by more experienced third-party advisors; b) Fewer legacy on-premise businesses with significant technical debt available for purchase; and/or c) the propensity of software companies to update their tech stacks periodically over time (sometimes in a very meaningful way)
Since Purchase, EBITDA Margins Have:
All Acquisitions: Across the entire data set, 63% of companies saw their EBITDA margins decline post-acquisition, while only 15% reported them increasing.
Comparing the Cohorts: Based on our “state of the product” observations above, we shouldn’t be surprised to see that the pre-2019 companies were more likely to see their margins decline after purchase than were their post-2019 peers:
- In the pre-2019 cohort, only 28% of companies saw their margins stay flat or increase post-closing.
- In the post-2019 cohort, 43% of companies saw their margins either stay flat or increase.
Conclusions: I have long suspected that search funds don’t model in enough margin contraction post-close in their base cases. Margin contraction in the early years can be common, and is often attributable to a combination of: a) Product-led investments, often of a “defensive” sort (see next section below); b) Sales & Marketing investments (again, see next section below); c) Historical under-investment in tools, people, and processes by the previous company owner; and/or d) replacing the work that had previously been done by the selling shareholder (that can often necessitate 2-4 incremental hires, as the new incoming owner doesn’t have the decades of knowledge and experience that their predecessors often did).
Where the Bulk of Opex has Been Spent
Idea: For those companies who have seen margins decline since the purchase, I was curious to better understand where the opex spend was primarily going.
All Acquisitions: Across the entire data set (only those companies who have seen EBITDA margins decline since purchase), two responses presented themselves with equal frequency: i) Product (including engineers, QA, infrastructure, product management, etc.) (~36% of total responses); & b) Sales & Marketing (also ~36% of total responses)
Comparing the Cohorts: Based on our analysis thus far, we shouldn’t be surprised to see that the pre-2019 acquisitions were more likely to direct their opex to fixing product-related problems than their post-2019 counterparts: In the “2019 or earlier” cohort, 44% of CEOs said that their margin declines were mostly attributable to product considerations. In the post-2019 cohort, only 29% of CEOs reported something similar.
Rate of Revenue Growth: Current vs. At Purchase
Idea: Because of the importance of revenue growth to the “typical” SaaS investment thesis, I wanted to better understand how the growth of the companies now, under new leadership, compare to the rate of growth at the time of purchase, under the previous leadership group.
All Acquisitions: The cohorts appear to be very similar in this domain:
- 55% of pre-2019 purchases recorded a higher LTM growth rate than the company was producing at the time of purchase
- 52% of post-2019 companies reported the same.
- Across the entire data set, only 12% of companies reported a lower revenue growth rate in comparison to when the company was purchased.
The Most and Least Foundational Components of Equity Value Creation
Idea: Although growing the value of a company is an incredibly complex and multi-variable exercise, at the risk of oversimplifying I wanted to better understand the primary components of each CEO’s plan to create equity value, ranked from most to least important
All Acquisitions: Across the entire data set, revenue growth was by far the most important value creation lever, with 80% of all respondents classifying it as the most important component of their value creation plan. Similarly, across both cohorts, leverage was classified as the least important component to the value creation plan. Based on our analysis above, this is unsurprising.
Comparing the Cohorts: Unsurprisingly, the post-2019 acquisitions do seem to have a bit more of a growth orientation relative to their pre-2019 peers: 87% of post-2019 companies ranked revenue growth as their most important consideration, while only 72% of pre-2019 companies made the same claim.
Interestingly, nobody, in either cohort, classified profitability growth or margin expansion as the most foundational component of their plan to create equity value. This is one variable in which the SaaS investment thesis appears to differ materially from the more typical non-SaaS investment thesis within the search fund ecosystem
Summary of Results
In the introduction to this blog post, I mentioned that the profile of the typical SaaS acquisition had indeed changed since 2019 based on the results that I was able to compile from this survey. Yes, searchers do appear to be paying higher multiples for SaaS businesses of late, however they also appear to be acquiring materially different types of companies, on average (larger, faster growing, a higher preponderance of recurring revenue, etc.).
A summary of our survey results can be found in the table below. Contained within each cell is the most frequently provided answer for each category and each cohort:
|All Acquisitions||2019 or Earlier||After 2019|
|Multiple of ARR||3-4x||2-3x||3-4x|
|Multiple of Total Revenue||2-3x||1-2x||2-3x|
|Multiple of EBITDA||nmf||4-6x or 6-8x (tie)||nmf|
|Size of ARR||$2-3M||$2-3M||$4-5M|
|LTM ARR Growth||10-20%||10-20%||40%+|
|Recurring Rev. as % of Total||80-100%||40-60% or 60-80% (tie)||80-100%|
|EBITDA Margin||10-20% or 20-30% (tie)||10-20%||20-30%|
|Size of EBITDA||$2M+||$2M+||$0-200K or $400-600K or $2M+ (tie)|
|Gross Logo Retention||90-95%||90-95%||95%+|
|Net Revenue Retention||100-105%||95-100%||100-105%|
|Rule of 40||40%+||40%+||40%+|
|Largest Customer % of Sales||0-5%||0-5% or 10-15% (tie)||0-5%|
|% of Transaction Funded w/ Debt||0-10%||0-10%||0-10% or 20-30% (tie)|
|# of Customers||300+||300+||300+|
|Cash funded to B/S at Closing||$900K+||$900K+||$900K+|
|State of Product||Worse than suspected||Worse than suspected||Same as suspected|
|Direction of Margins Post-Purchase||Down||Down||Down|
|Bulk of Opex Spend||Product & Sales||Product & Sales||Product & Sales|
|Current Growth Rate v. Growth Rate at Purchase||Higher||Higher||Higher|
|Most Important Value Creation Lever||Revenue Growth||Revenue Growth||Revenue Growth|
|Least Important Value Creation Lever||Leverage||Leverage||Leverage|
Appendix: Comments from SaaS CEOs
At the conclusion of the survey, I asked participating CEOs if they had anything important left to say to prospective SaaS acquirers. After reviewing their responses, I was struck by how many of their responses revolved around the product, which is often glossed over in due diligence, especially by non-technical acquirers. To provide you with a better sense of what these SaaS CEOs want prospective acquirers to know, below I have copied/pasted all of their responses specific to product:
- Pay for good tech diligence!
- Dig into customer support metrics to see how bad your product really is, then go in with eyes wide open!
- Understand product/market fit cold
- Make sure it’s mission critical, focus on churn
- Do not disregard implementations during due-diligence!!
- Focus on a company with a strong product, large TAM and truly recurring revenue
- Make sure you buy a company that is either 1) in a growing market, or 2) has a superior product
- Spend the cash early on for 1-2 highly experienced leaders in the places you need support the most – if you’re in need of product management, don’t hire a senior product manager, hire a Director/VP of Product – it will pay off in multiple spades
- Focus on a company with a strong product, large TAM and truly recurring revenue
Thanks to our Sponsors
This episode is brought to you by Avidbank. Avidbank is one of the most experienced search fund lenders in North America, having funded over 40 separate transactions since 2014, for a total of over $300M. They are deeply familiar with the search fund model, and understand the nuances of the fundraising process, dealing with sellers, communicating with your equity investors, LOI reviews, and everything else in between. Reach out to Anthony Rodriguez (firstname.lastname@example.org) or Conor Tidgwell (email@example.com) to learn more.
This episode is brought to you by Symphony, a company that partners with software businesses across the entire software development life cycle, including technical due diligence, team augmentation, outsourced development, technical consulting, and more. Symphony not only performs technical due diligence engagements for search funds, Private Equity firms, and strategic acquirers, but they also partner with those buyers on an ongoing basis on all things product (outsourced development, team augmentation, new product prototyping, UI refreshes, QA professionalization, and so on). Symphony is offering a full 15% off of any of their services for readers & listeners of In the Trenches. Just go to the Contact form on their website and tell them that you’re a listener of the podcast.