Palo Alto – April 2, 2021 – It was mid-Autumn 2019, and I looked at the faces around the table at our board meeting to gauge their reaction to what I was about to say next — “It’s time to sell the company.”
My assertion actually surprised no one. While we had gotten our growth engine humming along over the past 2 years since I joined as CEO at LiftIgniter, an innovative venture backed AI company with solid market traction, rapid industry consolidation and the emergence of ‘good enough’ alternatives in larger marketing cloud platforms had pretty much made this move a foregone conclusion.
With 6 months of runway, I had some time— but not a whole lot of it. The good news was that I wasn’t going to have to start the sale process from scratch. I had already forged a strong working relationship with an investment bank, Woodside Capital Partners, and had also built some bridges to potential acquirers. And over the next several months, we spoke with around four dozen potential acquirers with deep dive meetings with close to ten of them. Several term sheets later, we had picked our path and made it to the finish line with a nice outcome.
LiftIgniter was the fourth company that I was running which I got to a successful exit. No two companies and no two acquisition processes are exactly the same. But I’ve learned a few things along the way, especially when it comes to selling an AI startup, that I think will be valuable to any AI startup CEO.
With that, here are some learnings to help you achieve a more successful exit:
1. If a tree falls in the woods
One of the key challenges that we often faced in acquiring new customers at LiftIgniter was how to explain why our model had the impact and results that it did. We faced similar hurdles when working with potential acquirers. Rarely does an AI system have a simple linear function of ‘you do A and then B happens.’ We were able to show acquisition targets how we instrumented apps and which inputs would go into our algorithm; however, it was not as straightforward to show how the model weightings would adjust for a given user path/flow.
My recommendation here is, if possible, to spend the time before you have discussions with acquisition targets to instrument your system with a sort of ‘debug mode’ where the weightings of the various model inputs can be visualized. If that is too complex to do, the next best thing is to have a parallel simulation that can show what the results would have been without your system involved (both in aggregate as well as flow-by-flow specific).
Lesson 1: Don’t underestimate the importance of model explainability and its impact.
2. Data scientists have feelings too
As with LiftIgniter, your startup likely has amazing tech. While our algorithm typically opened the door to strategic discussions, we soon learned that just focusing on the superiority of our algos was not getting acquisition deals to sufficiently advance. The reason: all of our targets had strong AI/Data Science experts who also were really smart. Going into the discussions and eventual negotiations with ‘we’re smarter than you’ is not a great way to win over the other side.
Rather, I found by focusing on how we applied our algorithms to very effectively solve specific use cases helped the discussions progress. By framing things in this manner, we weren’t threatening to their data scientists, we were showing them how we can apply our tech to specific use cases. The result of which helped their data scientists see how our tech and team could help their team do more.
Lesson 2: Focus on how your product and team can help them by framing the effectiveness of your algo.
3. If the shoe fits
Many companies underestimate the importance of this area. No matter how impressive your technology and even customer impact is, if the integration and ongoing maintenance of your solution appears to be hard, you will face high hurdles in your acquisition discussions. One of the areas often overlooked at AI startups is model operationalization, since much of the early focus is around getting the algorithm to work consistently well and to have impactful results. As an AI company matures, this operationalization becomes more important. From the standpoint of the team evaluating your technology to recommend whether their company should move forward with an acquisition of or not, they will likely place considerable weight on how much work may be required to integrate your solution (as they will be doing this integration).
In the case of LiftIgniter, we had prioritized early on the operationalization of our models. That not only helped greatly with the performance and efficiency of our solution, it also made the tech due diligence discussions go smoothly. While you may not have the time or resources to go back and operationalize all components of your solution, at a minimum, make sure to have a strong design for how you would operationalize/automate the key points of integration with the potential acquirers’ solutions. In general, companies who operationalize their AI components command higher valuations.
Lesson 3: Prioritize demonstrating the model operationalization/integration with targets.
4. Two heads are better than one
It is quite possible that you may have two pathways into a potential acquirer: the product management/line of business (LOB) team as well as the data science team. When selling LiftIgniter, we found that if we started with the LOB team, the data science team would often feel defensive and try to find holes in our solution. And if we started with the data science team, the LOB team would often push back that they had other priorities.
Our greatest success came from approaching both of these groups in parallel. This can be a difficult path to navigate well and having a banker to help with this can be a big plus.
Lesson 4: Consider dual tracking the entry point into the target.
5. No time like the present
As a startup CEO, you are constantly juggling priorities and having to make do with insufficient resources. So, spending time on developing relationships with strategic partners that most likely won’t lead to near term sales revenue may seem like a luxury not worth the time and effort. However, through my four Company sale experiences, I learned that building these relationships early on was key to a successful exit. It would have been a challenge for me or even our bankers to get the right level of attention at these key targets if we had not initiated and cultivated these relationships prior to our formal M&A process. So, start to build these relationships now—in the future they’ll pay off greatly.
Similarly, building strong working relationships early on with an M&A banker can be invaluable. At Woodside Capital, we typically are working closely with CEOs long before the active selling process begins. So, as you consider the future strategy of your company, keep us in mind. We can help you think through the strategy and timing of your plan to help ensure the best possible outcome.
Lesson 5: It’s never too early to line up potential targets and your support team.
And as for me, after my fourth time around the track, I felt like I was becoming well versed not just in running companies, but in getting them to positive exits. So, earlier this year, I decided to join the Woodside Capital team to focus especially on helping other AI companies do what I did when the time is right.
Jon advises AI and Cloud Infrastructure & Software companies on M&A and strategic financing transactions. He brings a rare combination of Silicon Valley entrepreneurial and executive experience, coupled with successfully spearheading the exits of multiple startups he was running. These include LiftIgniter, acquired by Maven.io, Badgeville, acquired by SAP/CallidusCloud, CloudUp Networks, acquired by CipherCloud, and XDN acquired by Fortinet. Prior to joining WCP, Jon was a successful serial CEO and entrepreneur in Silicon Valley during his 25+ year career. He has managed companies in AI, cloud infrastructure software, security, and marketing cloud SaaS products.
Early in his career he spent over 5 years working in Japan in various roles at Toshiba and in management consulting, and he later worked at Internet infrastructure bellwethers including 3Com and NetScreen/Juniper. Jon is a Phi Beta Kappa graduate of Duke University and the Stanford Graduate School of Business.
You may get in touch with Jon through email@example.com.
Woodside Capital Partners is the leading corporate finance advisory firm for tech companies in M&A and financings in the $30M-$500M segment. The firm has worked with the best entrepreneurs and investors since 2001, providing ultra-personalized service to select clients. Our team has global vision and reach, and has completed hundreds of successful engagements. We have deep industry knowledge and extensive domain experience in the following sectors: Autonomous Vehicles and ADAS, Computer Vision, Artificial Intelligence, Cloud/Enterprise Software, Cybersecurity, Digital Entertainment & Lifestyle, Health Tech, Internet of Things, Marketing Technology, Networking / Infrastructure, and Robotics. Woodside Capital Partners is a specialist in cross-border transactions, with extensive relationships among venture capitalists, private equity investors, and corporate executives from global 1000 companies. More about Woodside Capital Partners here.
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