October 12, 2021 – On today’s podcast we welcome special guest AdTheorent CEO Jim Lawson. AdTheorent is a programmatic digital advertising leader using advanced machine learning technology and solutions to deliver real-world value for advertisers and marketers.
On the show, Jim discusses:
- His journey from practicing law to becoming an entrepreneur and CEO
- How AdTheorent’s machine learning advertising technology platform works
- Why the two most prevalent ad targeting methods, cookie-based retargeting and segment-based audiences, are less effective
- Insights into the merger with MCAP Acquisition Corporation
- And more
Welcome investors to the Absolute Return Podcast. Your source for stock market analysis, global macro musings and hedge fund investment strategies. Your hosts Julian Klymochko and Michael Kesslering aim to bring you the knowledge and analysis you need to become a more intelligent and wealthier investor. This episode is brought to you by Accelerate financial technologies. Accelerate, because performance matters. Find out more at www.Accelerateshares.Com.
Julian Klymochko: Excited to have Jim from AdTheorent on the show today. Jim, I looked at your career history, your trajectory, found it very interesting. It seems like a lot of CEOs that we come across these days started out as lawyers. So, you practice law for nearly a decade prior to making the jump to startups, technology, leadership, things of that nature. What made, or what took you off that so-called partner track, take the leap of faith to entrepreneurship and tech startups?
Jim Lawson: Well, thank you, Julian and Michael. It’s good to be here. That’s a great question. I will fight the temptation to make a lawyer joke.
Julian Klymochko: Feel free.
Jim Lawson: After 10 years, I had finally paid off my school debt and I really wanted to work on creating something tangible as a lawyer, representing clients on all kinds of different individual matters. I was often jealous of my clients. I really wanted the opportunity in my career to build something and grow something. And I had a feeling that my self-motivated style, my disdain for office politics would be better suited for a new venture as opposed to an older corporate bureaucracy type endeavor. I love the thrill of helping to grow a business, and the AdTheorent team is really a world class group.
Julian Klymochko: That makes a lot of sense. And I assume you had a much higher risk appetite than your typical lawyer, but none less. On that career track, no doubt that you would learn a lot of useful skills and get the resources needed to pursue really ultimately were probably your dreams on the startup side. So, what specifically did you learn as a lawyer that came in handy as a CEO, entrepreneur and on where you’ve been in your post lawyer career?
Jim Lawson: Oh yeah, so many things. At the end of the day, legal work and business work, each center around problem solving. Practicing law and billing 2000 hours per year for 10 years. It actually makes me sad to do that math.
Julian Klymochko: Laugh.
Jim Lawson: Taught me a lot about working hard, how to do a hundred things at once while attempting to remain calm, how to deal difficult people, how to think clearly and write and communicate clearly. And perhaps most important. I worked closely with some world class people who did things the right way, and I learned from them. I also saw countless mistakes made by people in businesses that I’ve learned from and used in my business career.
Julian Klymochko: And it’s a lot easier to learn when someone else is making the mistakes rather than you.
Jim Lawson: That’s, right. Exactly.
Julian Klymochko: So, I wanted to get into AdTheorent business. Can you start off describing how the company’s machine learning advertising technology platform works?
Jim Lawson: Absolutely. At a very high level, programmatic digital advertising focuses on targeting digital ads to the right media opportunities, so that advertisers benefit. AdTheorent is a very unique programmatic media buying platform because we use machine learning and data science as the core method of ad targeting and campaign optimization, all at the impression level. And this is contrasted with cookie based and other ID based or user profile-based targeting, which really focuses on an individual’s identity and retargeting that individual identity across the internet.
Julian Klymochko: Right, and I get the sense that cookie-based retargeting or segment-based retargeting is somewhat negative. Could you describe that? And like, I’ve heard that perhaps there maybe pressure on cookies in terms of their use from a privacy perspective, are those specific technologies going to face pressure in the future?
Jim Lawson: Absolutely. So, we, as a machine learning statistic driven targeting organization are foundationally different than cookie based and profile ID based targeting solutions in that we target and optimize performance based on non-individualized statistics, which inform our predictive models. And this is very different than the prevalent historic methods of cookie based and audience-based targeting, which very much rely on targeting available user IDs. As a result, we believe we have a major strategic advantage because being privacy forward is becoming more relevant and more important among other things, initiatives led by Google and Apple are making individual IDs harder to access and harder to use for ad targeting. So, we believe those realities as well as a movement in the industry generally. And the focus put on privacy and being privacy forward by CMOs and leading businesses. Give us a very significant strategic advantage all on top of the fact that MLB predictive advertising performs better. So, it performs better and it’s more privacy forward. It’s kind of a win-win for everybody.
Michael Kesslering: Can you dig into some of that performance and how it performs better? As well as really kind of like, I guess, going over, why outside of the privacy aspect, why a company would choose AdTheorent over some of the other competitors?
Jim Lawson: Yeah, Michael, that’s a great question. Advertisers choose AdTheorent because we drive return on ad spend, it’s that simple. Our machine learning platform identifies the ad impressions with the highest likelihood of converting on a client’s desired action, whether that’s an online action or a physical world action, such store visitation. AdTheorent predicted targeting is not reliant upon third party data licenses, cookies, device IDs, or any of the new unified or individualized IDs being discussed in the market now. We can and do use them to the extent available, but we are not dependent on them to target. Instead by ingesting statistical and not on individualized data attributes and each bid request, our models can inform our real time media buying decisions in a way that’s not tied to an individual. So, for example, if our customer’s KPI or performance objective is an online insurance quote application, our platform will identify in real time, data correlations, which exist in the historic insurance quote conversion activity. So, this could be device type, excuse me, operating system, one or more keywords in the URL, keywords in the page content, geographic data, time of day, you name it. There’s many, many data attributes that the machine learning models can detect, and they can determine connections between data attributes and conversions. And then we utilize that for campaign optimization.
Michael Kesslering: So how are you deciding upon the data inputs that are going into your machine learning model, I guess at kind of like the top of the funnel?
Jim Lawson: Absolutely, so as a DSP in the programmatic ecosystem, we receive a lot of data from supply side partners and publishers, and that data called a bid request. That’s essentially the request or the delivery to us of an opportunity to bid on an ad opportunity. And those bid requests are data strings. As I mentioned earlier, some of the data are individualized IDs that some DSPs use for targeting and all they’re looking for are those IDs, and they just target those IDs. In our world, we ingest the other signals and then we let the models tell us when attributes 7, 27, 73 and 56 are present. There is a greater likelihood of a conversion event that’s predictably scored. And then that drives our bidding activity, including the pricing that we’re willing to pay for a given impression.
Julian Klymochko: So, the technology is fairly complex with respect to the machine learning data science. I’m hearing a lot about signals and models. I was wondering if you could really help clarify for our audience, you know, can you give a real-world example of AdTheorent, you know, within an advertiser’s business and how you guys did it?
Jim Lawson: Yeah, absolutely. It is quite complex. I mean, starting back in 2012 was when we created our business. At the time, mobile was going to be a big deal. Mobile advertising was going to be a big thing and without cookies to use for targeting, you know, how do you target effectively? How can you efficiently target? So that was really the premise that our business was built on. And then over the years, we became truly omnichannel and going towards desktop and all the other screens, including video and CTV. So, the way that we have grown our business has been very much showing up at client and saying, what are you trying to do with your advertising budgets? What is the goal?
What is your business goal? It starts there. Any machine learning algorithm needs to have a definition of success as its starting point. If you define success, then you can capture success and you can identify what data variables are present when that success occurs. And then from there, you can predict future behavior and you can make that past data actionable in the future, and that’s what we do. An example, like a credit card sign up, or in some cases driving a restaurant consumer to a physical location. And as long as you have a feedback loop, whether that feedback loop is an online feedback loop or some other offline measurement and attribution feedback loop to give you the data on performance and conversion activity, the models can update and learn from historic conversion activity, and the result is the customer wins. The customer gets more return on ad spend. They spend less per conversion using us than they do with other platforms because we’re so locked into performance.
Julian Klymochko: That makes a lot of sense. Now, the theme that I constantly hear from you, Jim, is that of privacy. So that’s becoming increasingly more and more important and clearly, you know, a key tenant of what AdTheorent does. Now, I was running that focus on privacy. Is that mostly being driven by the platform, sort of trying to get rid of these ID based retargeting data points available, or is it something that you’re hearing from your customers as well? The advertisers
Jim Lawson: It’s absolutely both. Our platform is a privacy by design platform in the sense that the data that we primarily rely upon for targeting is not individualized and is not user profile based. So, because of that, we are inherently privacy forward, because we are not seeking to build user profiles. We’re not in the business of creating user profiles. We are in the business of identifying ad impressions that are going to result in business conversions. And we don’t care who the people are that do that. We just want to do that efficiently for our customers.
Julian Klymochko: Transitioning to another topic. I wanted to talk about AdTheorent recent performance. Specifically, during 2020, COVID. Super tough environment, for the advertising industry in general. However, you guys did grow revenue in 2020, despite a very challenging year for the industry. How did you do it?
Jim Lawson: Well, thank you for that question. We grew modestly in 2020, but we consider 2020 to be a success story across the board for our business. First, we kept our team together, no layoffs, consistent advancement of our tech and product roadmap. We did not pursue a payroll protection program, loan or grant because we had access to capital and we believe that money was intended for bakeries and barber shops and other great small businesses throughout the U.S. We had a lot of deep vertical expertise and we focused on COVID resilient verticals during the darkest days of 2020 that’s banking, financial services and insurance, pharmaceuticals, government, education, nonprofit. Business and life went on, I mean there was an economic shutdown across a number of vertical sectors, hospitality, dining, retail, auto. There were definitely a retreat in spend from some of those verticals, but our business is broad and diverse and we’re able to focus on the verticals where there was spend and frankly in government and nonprofit, there was incremental and new opportunities, same with pharma. So, we just focused on where the opportunities existed and we continued to build our business and plan for the future. And we emerged from the COVID economic shut down a better business in many ways. We operated at north of 30% EBITDA margins in 2020. And we have been and project that we’ll continue to be a rule of 50 company when you consider both EBITDA, margins and revenue growing.
Julian Klymochko: So, I believe you are owned by H.I.G Capital at the time. So, I assume they’re very supportive.
Jim Lawson: Absolutely, H.I.G has been a great partner of our, since December of 2016, when we joined forces with them, they’ve been a great partner and they’re very excited about the business and the next chapter for the business on the public markets.
Julian Klymochko: Okay, and I did want to get into this next chapter in the public markets, of course recently announcing merger with MCAP Acquisition Corporation, 775 million enterprise value is what you’re going public at. How did this deal come together?
Jim Lawson: Yeah, so we are becoming a public company through a SPAC process. We are not a typical SPAC target in the sense that unlike some of the other companies that have utilized the SPAC process to go public based on, you know, mostly future forecasts and projections, we have a strong track record of historic financial performance, our fundamentals, again, our sound. We have a very disciplined operation, in 2021 we’ll generate north of 160 million in revenue per our most recent guidance over 106 million in revenue, less tack, which is our gap revenue minus the traffic acquisition cost. And as I mentioned, over 30 million in EBITDA that represents significant year over year growth, both in revenue and EBITDA margins. So, we think, you know, as a SPAC, we stand out from the crowd, but it was a very, very unique opportunity for us to continue to work with Monroe Capital who was our lender from the H.I.G transaction. And they’re sponsoring this SPAC. So, we thought a really great opportunity to keep our partnership with Monroe going forward onto this next chapter.
Julian Klymochko: Yeah, I was wondering what made Monroe Capital, the ideal SPAC sponsor to partner with, like, did you guys consider others? Did you have like a very tight process where I only spoke to this one? How did that work?
Jim Lawson: We had a thorough process, and we had a number of different options which included going public both in a traditional sense, and through the SPAC route. At the end of the day, we chose to partner with Monroe. We wanted to continue that partnership. They, again, they had financed the H.I.G transaction in December of 16. They knew our business. We had a shared understanding as to the unique value of our business and what we believed it could be with greater investment. Monroe also had a track record of success with other SPACs, and that was important to us. Again, knowing that we were going to be a different animal in this SPAC marketplace, we very much appreciated Monroe experience there and wanted to keep that strong partnership going forward.
Michael Kesslering: So, moving from private equity ownership to a public company, obviously there’s quite a bit of changes there. And so how does things change for you as a company, but also for you in terms of your role a as the CEO as well?
Jim Lawson: It’s a great question. As far as the company goes, generating cash flow has always been a part of our corporate DNA, we’re proud of that. For all the reasons discussed, we believe now it is the time to make a bigger investment, to capture a greater share of the market. We will retain our financial focus and we will operate efficiently, but we believe our differentiation justifies capturing a larger portion of the programmatic digital opportunity. We will expand our sales and marketing footprint. We will continue to invest in our platform, differentiation. We will pursue the growing CTV opportunity, which is a rapid and growing opportunity for us. We’ll expand our vertical capabilities. And there are a number of other avenues that we’re excited to explore, to grow revenue faster. As I go, I mean, I think for me, the focus needs to just continue to be on creating and helping our company create value. Not getting distracted by a lot of the things that come with being a public company and really maintaining our laser focus on value creation for our customers, so that they get value every time they spend money with us on a campaign, and that we continue to innovate and remain differentiated and special. And I think if we do that, the finance and the market results will take care of themselves.
Julian Klymochko: Now, Jim, one key process of going public and being a public company is, you know, constantly getting the word out there, telling the AdTheorent story. Now, with the respect to the AdTheorent story, what do you think is the most important aspect that investors should know?
Jim Lawson: Thank you for that question. We have, you know, a multiyear track record of disciplined, operational and financial success. Our core technology platform and products are highly differentiated and value adding from the perspective of our customers. We have several strategic advantages, accelerating demand for our offerings. The industry and regulatory privacy changes make our approach to data and digital ad targeting. Generally, the future for AdTech, we believe our performance is unrivaled in the space. Our platform and capabilities and approach are unique and different. And we believe that now is a great time to be an AdTheorent stakeholder. And we couldn’t be more excited to be here.
Julian Klymochko: I really like the privacy aspect. That’s something that’s going to be more and more of a focus in the future. And then the fact that it perhaps works even better than the competitors utilizing your machine, learning data science technology platform. That just seems like a win-win and could be a win-win for investors here. So, to the extent that any investors are interested in following the story, the current ticker symbol, MACQ for these special purpose acquisition company that you are emerging with. And yeah, other than that, I think we touched on all the major aspects of the AdTheorent story today. So, Jim, I’d like to thank you very much for coming on the podcast today and sharing your thoughts and ideas and your background, so super interesting.
Jim Lawson: Thank you, Julian. Thank you, Michael. Appreciate the opportunity to be here.
Julian Klymochko: All right, we wish you the best of success in the upcoming merger and going public transaction.
Jim Lawson: Thank you so much.
Julian Klymochko: All right, take care.
Jim Lawson: All right.
Julian Klymochko: Bye everybody.
Thanks for tuning in to the Absolute Return Podcast. This episode was brought to you by Accelerate Financial Technologies. Accelerate, because performance matters. Find out more at www.AccelerateShares.com. The views expressed in this podcast to the personal views of the participants and do not reflect the views of Accelerate. No aspect of this podcast constitutes investment legal or tax advice. Opinions expressed in this podcast should not be viewed as a recommendation or solicitation of an offer to buy or sell any securities or investment strategies. The information and opinions in this podcast are based on current market conditions and may fluctuate and change in the future. No representation or warranty expressed or implied is made on behalf of Accelerate as to the accuracy or completeness of the information contained in this podcast. Accelerate does not accept any liability for any direct indirect or consequential loss or damage suffered by any person as a result relying on all or any part of this podcast and any liability is expressly disclaimed.