SaaS Valuation and Early Exits: An Interview with David Rowat of Strategic Exits
Steven Forth is CEO of Ibbaka. See his Skill Profile on Ibbaka Talio.
One reason SaaS companies come to Ibbaka for help with their pricing is that they want to use pricing as a lever to improve valuation. Price can have a big impact on the key metrics that drive the value of a SaaS company, Average Contract Value (ACV), Annual Recurring Revenue (ARR), and Net Recurring Revenue (NRR).
One of our fellow travelers in this work is David Rowat from Strategic Exits Partners. David and his partners at Strategic Exits help companies execute successful exits and are well known for encouraging early exits, something we are already starting to see in the generative AI space (see As AI Costs Soar, Some Startups Consider Selling on The Information).
Ibbaka spoke with David in early February to get his insights into how SaaS company valuation is evolving and about the impact of AI on valuation.
Ibbaka: Can you share with us your background and how you got involved in thinking about valuations and helping companies to exit?
David Rowat: I've been in the tech industry for over 30 years and I've worked for 75 different companies as full-time, part-time, project work, as a fractional CFO, all kinds of things. It started for me when I was still in engineering school. I could see that Canadian technology was as good as any technology in the world. What was lacking was good technology management. I thought that maybe I could do something about that. I enrolled at the Harvard Business School to learn more about business management. I then returned to Vancouver to continue my career in the tech industry and to ski at Whistler.
The photo was taken at the 120 km mark, and felt like it.
My current gig is with Strategic Exits Partners, the company that was founded as Strategic Exits in the early 2000s by Basil Peters. When Basil retired in 2022, my partner, Len Zapalowski, and I continued the company as Strategic Exits Partners. In the mid-2000s, Basil discovered something that everybody else in the tech world had overlooked. Back then, the industry mantra was “build your company, build it big,” then do an Initial Public Offering and retire happy. Great theory, but was we all know, fewer than 10 – 20% of tech companies make it that far.
Basil invented the concept of the early exits. He noted that the majority of tech exits were not by an IPO but through a private sale of the company and the average sale price was about $15 million, which is enough to satisfy most founders. Next, he surmised that many of the 80-90% of companies that don’t succeed were forced into growing the company past their management capabilities. Many could have taken an early exit and earned a life-altering win, instead of becoming exhausted and unhappy after 10 years of hard work amounting to a learning experience.
The early exit strategy, while earning a “win” for the founders, also avoids the 10 years of agony trying to build your business to a great valuation. The early exit strategy was considered heresy at the time, because the “Go Big” strategy was so ingrained. But the early exit offers a pragmatic alternative to many companies and founders who would be happy with a smaller win, with greater certainty, and saving a decade of their lives.
The early exit has become very popular now. It's part of the mainstream. I don't think you'll see anybody arguing that early exits aren't a viable way to sell and recruit the investment in your company.
I was sitting around one day trying to retire in 2017, and failed miserably because Basil picked up the phone and said he needed help. He had lots of exits going on and asked me to come and join Strategic Exits. We embarked on a long campaign to re-brand Strategic Exits Partners as a world-wide M&A advisory firm with Basil, Len and I as partners. We started to rebrand ourselves by researching and writing on more trends in technology M&A. If you go to our blog you will see dozens of articles that the three of us have written, on early exits, fully-remote companies, SaaS valuations, robotics, AI, and more.
Basil retired in early 2022. Len Zapalowski and I have carried on the company as Strategic Exits Partners. We have worked on exits of tech companies around the world.
Ibbaka: What is it that makes you excited about this work?
David Rowat: An exit is a game-changer. For many people, the exit is the biggest financial transaction they will ever do. Bigger than selling their house, bigger than getting married. It literally changes people's lives. There is a great deal of satisfaction in making that kind of difference in a founder’s life.
There's something that Strategic Exits Partners does that nobody else does. There are thousands of M&A advisors out there, and they all have the same message - “Come to us. We'll sell your company and make you lots of money.”
But we do more than that. We often work with an early-stage companies for a period of months (and sometimes years) to take the kernel of what they created and develop a business and development strategy and an exit plan so that they're better prepared when we launch the exit. We invest a lot of time helping the companies get ready for exits, more so than anybody else does, to the best of our knowledge.
I mentioned earlier that part of our strategy is to distinguish ourselves from other M&A advisors by becoming thought-leaders in various trends in the tech world. For example, I researched the how fully- remote companies could become so successful so quickly and I developed a formula to prove that they also make more money for their founders than the traditional bricks and mortar companies do. We've also continued Basil's research on early exits and developed some new theories around that. I've also taken a particular interest in how to value SaaS companies.
Ibbaka: What does a company need to look like to exit today?
David Rowat: Today we're in a downturn. The number of financing and M&A deals has plummeted and valuations, particularly among unicorns, are a fraction of what they were in 2021. But this is not the first time that we have seen rampant euphoria drive tech company valuations through the roof, only to see them crash later. It happened in the dot.com meltdown of 2000, the global financial crisis of 2008, and now the latest tech bubble burst in 2022. History is repeating itself. It is incomprehensible to me that anyone is surprised that the bubble burst. I guess boundless optimism blinds us to history’s lessons.
We haven't come out of the crash yet. There's one sub-sector in generative AI that's doing extremely well. Many people are saying that genAI will lead tech us down to the valley of redemption. I hope they are right about that. I am doing some research on that that I hope to publish in a couple of weeks.
In these down markets, it is difficult to get sold today because the interest level isn't there in the way it was in 2021. You have to have something very exciting to talk about to get anybody to pay any attention right now. Nobody's in the mood for investing, and so that means that you're going to have to bring not just a business idea on the back of an envelope like you could in 2021, but you need to show what you've got, why it's important, why it's better than anybody else's, why you're going to be able to defend it against other competitors that are coming, that you've got a huge market and that you've got the right team in place.
That hasn't changed much in 20 or 30 years. I mean, that's the same you've always had to do but now the evaluation of the case is a lot harder than it used to be.
Investors are looking for reasons to say ‘no’ as opposed to saying ‘yes’. That means you have a lot more homework to do to get ready before you go and see an investor. Many of the companies that we're talking to, including some of our clients, are looking for angel funding right now to tide them through this rough spot so they can get to a point where there's a better market for mergers and acquisitions.
Ibbaka: How do early-stage companies get valued? And how early is ‘early stage’?
David Rowat: Sometimes it can be pre-revenue, sometimes pre-product. With Basil we won the ‘Angel Deal of the Year’ for selling a company, Genesis Robotics, that was pre-product. It had technology, it had patents, but it didn't have a product and it didn't have any revenues.
Part of the early exit strategy is to not invest significantly in developing a commercial presence and growing your business. It's a calculated strategy to build technology that gets some customer traction to prove that people will buy it and sell it in a couple of different sectors to show that it has a wide appeal. You do this before investing heavily in the infrastructure you need to “Go Big”.
We call this ‘the early exits decision point’ when you have to decide whether to raise venture capital and to “Go Big” or to stay small and survive on founders’ and angel’s money until you can get to an early exit. The idea is that if you haven't made that decision to raise financings to grow big, you're making it a decision to stay small, lean, and mean so that you can exit before you dilute your equity position by raising funds for expansion.
Ibbaka: Are there metrics that you guide people to focus on or is that very dependent on each situation?
David Rowat: Every opportunity is situation-dependent and at an early-stage so much of the future is unpredictable. So, in terms of metrics, there are more qualitative than quantitative ones at this point. For example, if a customer gives a positive evaluation of your prototype, or Minimum Viable Product, that’s an important metric. You have created something.
If you can sell it more than once, then you've got commercial traction. The actual numbers aren't particularly important; it’s the fact that a customer will buy and use our product.
One of the curious aspects of selling companies at an early stage is to make sure that you don’t sell a lot of product if you intend to pursue an early exit. Once you generate a revenue stream, an investor may want to value you based on a multiple of your small revenue. That's the last thing you want. You want them to value you based on the technology that you've created, and the value it will create for them as the acquirer. The acquirer can leverage that across a company that's a hundred or a thousand times bigger, which means that your company is worth way more to them than your small revenue stream might indicate.
Ibbaka: You’ve been doing some work around net revenue retention (NRR). Can you unfold your thinking on that and its impact on valuation?
David Rowat: I’ve been working on how you value SaaS companies for a little while. I probably read 200 articles on the subject and all of them were depressingly the same. There is very little in the way of hard numbers and actual calculations and a lot of qualitative saying ‘bigger is better.’
The very simplistic formula of’ annual recurring revenues times a multiple’ is a grab bag for everything that influences the growth of the company, which to me wasn't very satisfying because a lot of it was qualitative, and a lot was based on how public SaaS companies were evaluated. The SaaS Capital Index has been incredibly volatile since the early 2000s. The index was as low as 2, then climbed to over 17 during the bubble. Crazy. Then the crash happened and after the index plunged, it has been thankfully staying more or less steady at around six- or seven-times revenue. Now that the multiples have stabilized, I think it is safe to start estimating SaaS company valuations again.
An article that I came across by SaaS Capital, and I have to take the opportunity here to thank SaaS Capital for the incredible amount of work they have done on SaaS valuation. They are clearly the world leaders. They've done a lot of deep research and applied this to their own business and they've been generous enough to publish a lot of their research so people like me can pick it up and carry it on.
I found an article where they had done regression analysis on 47 financings in SaaS companies they had done themselves. Regression analysis basically tracks the variability out of a mass of data to try and see what the essential variables are and they determine that the SaaS multiple, the growth rate in revenues, and the net retention rate were the three most important variables. I Incorporated those into my work and added to that several other features, which I thought were important that they hadn't included. I spoke to them about that and they said ‘Yeah, we used to have six, but now we have three because adding on an extra one is incrementally better but It starts to confuse the issue a little bit.’
The good thing about net revenue retention is you identify your recurring revenue customers at the beginning of a year and follow that particular cohort to the end of the year. You're looking at the same customers at the beginning of the end to see what's happened. Now if they've fallen off that's bad. Your retention rate goes down. But here's the thing that churn doesn't do - which net revenue retention does do, it allows for the fact that your customers could end up buying more licenses over the course of the year and that's in that positive. The retention in retention rate provides for a greater number of eventualities that can be numerically quantified. That's why SaaS Capital found that it was better to include that term in the formula.
They said that their formula which gives you a baseline evaluation multiple explains about 50% of the variability. This starts to become valuable and useful. If you can get a valuation quantified within a 30% range you're doing pretty well because previous to this everybody was just guessing I think.
Ibbaka: I'd like to get your thoughts on how you go about valuing an AI-first company. Is it the same as valuing another early-stage company or are there different perspectives that you bring to this?
David Rowat: It's completely new and I would suggest that the valuation of AI companies today is where SaaS companies were about 10 years ago. There was a lot of data but not a lot of information.
I should probably note that investments in AI other than generative AI have also fallen off. AI has been around since the 1960s, so there's been a lot of companies that have come and added to the world's knowledge and there's been enough rounds of financing that you can probably start to develop some kind of sense of AI valuations.
Completely separate from that is generative AI which as you noted has just taken off like a rocket over the last year to year and a half. There have been, I think, 135 generative AI companies formed in the last year or two, which is a staggering number. But this has all the aspects of the latest cool new thing.
I believe there have been 22 unicorns developed in that period of time and there are some charts that show a few numbers of what people paid. OpenAI is the biggest gorilla in the market. Its valuation is way up there. Looking through my various resources, I don't see a lot of information on the valuations of generative AI. It's more about how much money has gone in, and how long they have been at it. Who are their partners? There's a lot of time being spent on who's partnering with whom.
It's the wild west out there. What I intend to do is to look through some of the sources we've got where there is information on investments and exits. We can generally find that through Crunchbase. I suspect there won't be very many of them, but that will give me some way of developing metrics.
I should turn this over to machine learning and let that do the work.
Ibbaka: Do you see AI impacting the valuations of more conventional SaaS companies?
David Rowat: What I'm hearing a little is that new companies that are going out for financing need to have an AI story. Are they intending to bring in data analytics? Are they intending to bring that into their business or not? And if not, why not? It's pretty valuable.
Yes, there needs to be an AI story and in fact, we have a client that was in the retail business and they were at the point of wanting to exit because it was time. They had grown older and wanted nothing more than to go and play in a rock and roll band. We said let's talk about your data analytics and here are some things that you might think about doing. They looked at that and said they were going to wait to work on leveraging the data they had collected to build value.
They could use data analytics and machine learning to determine certain trends that would be important for product positioning and discounting. So there's one example, and I think you're going to see lots more of it. It reminds me of 20 years ago when you had to have either an "e" at the front of your name or a ".com" at the end of your name if you were going to get financing because that was the beginning of the internet. People are getting pretty excited about generative AI—it's an incredibly comprehensive technology that has not existed before, which enables people to do things they only ever imagined before, and now you can do it basically at home on your laptop. So, it's an exciting new time.
Whether it's going to continue, I think this one's really got legs because of the immediate commercial applications. I contrast that with some Multiverse, which was a big deal recently. I've got a little chart somewhere that shows the number of times that Multiverse was mentioned in a quarterly earnings call, and it used to be up here, and slowly it's slid all the way down here. It's one that just never took off, and frankly, I don't think it will.
Ibbaka: Do you think that there is perhaps a set of companies out there that have been gathering data over the years and have had no way to monetize that data, but now because of the changes new opportunities have opened for them?
David Rowat: I think so. I can't point at anything specifically to tell me that that's the case but I got to believe that that's true. We've been hearing about data analysts for the better part of a decade and I believe that the companies are bringing this kind of analytics into their business processes. They can better deploy their assets for greater gain.
Next time I’ll remember to do up my chin strap.
I thought if I'd retired, I'd be able to ski more but I found out that you can only ski so many times in a week before you can't do it anymore. So, I am still at work.
In the summertime, I ride my bike probably four to five times a week. I belonged to a riding club and typically on a Saturday, we start at the Planetarium and head out to Horseshoe Bay or ride to Deep Cove or Tsawwassen. Those are probably 60 to 100 kilometer rides. I also ride with a group of Tech Veterans once a week and we will log 50 – 80 km. During the summer the week I’m probably riding four days a week. So, between skiing and riding and working out in the gym to stay fit, that keeps me healthy. I will keep it up as long as I can.