Bootstrapping a Second Data Venture

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In 2014, we covered John Wallace’s entrepreneur journey with DataSong, a bootstrapped venture that achieved a successful exit. LiftLab is his second company, and he is applying the same rigorous, lean, disciplined approach.

Sramana Mitra: John, welcome to the show again. It looks like you’re up to a new adventure. So, tell us about it. John Wallace: I’ve been fortunate to be building LiftLab for the past five years. The team had built a company with me called DataSong, which, you covered. I feel very honored and fortunate to be a second time attendee on one of these interviews.

Something unique I’ll share about us is that, when we took some time off from DataSong after its acquisition, a couple of my colleagues had gotten back together and they were trying to figure out what they wanted to build next and I had shared an idea with them. I actually came back and rejoined the old team. And that idea was the beginning of LiftLab.

Sramana Mitra: So tell me a bit about the ideation process. Like why, what struck you that this was the idea that you wanted to go after in your second? Because people tend to be more selective when they’re coming at this as a second-time entrepreneur after a successful business. I think the bar is higher, don’t you think, on what idea to give yourself to?

John Wallace: I think so. My answer is very specific to my field because I work in data science and AI. The idea that was the nexus for LiftLab was to not build models but to build a system for running experiments. They’re very complimentary forms of data collection. Experiments have some well-recognized advantages of designing and making data for the purposes of measurement. I said, let’s leave all this modeling behind and just run lots and lots of experiments. Let’s get good at it. And we felt there was a, a market need for that, and that’s how LiftLab started.

Sramana Mitra: And what validation work did you do before you fully committed to this idea?

John Wallace: Having already built and sold DataSong, we had a pretty, strong reputation in the market. We were able to get people on the phone much faster than the first time around. I started pitching this idea to a lot of different people. The person who did the due diligence on the algorithm of DataSong was an academic named Dr. Dominic Hanson. I said, “I want to pitch you on another idea and I want the tough love. If you don’t think we should build this product, I’d rather know it now.” And he actually thought we were onto something and decided to invest. He’s an investor and an advisor to LiftLab, so a part of the validation.

Sramana Mitra: Who would be in your original vision? Who would be the user of this?

John Wallace: We’re running now on dozens of brands, and our main user is typically the Head of Growth or Head of Performance Marketing or Head of Digital Marketing and any of the channel owners for that media below them as well as their agency.

Sramana Mitra: So, the use cases are all in advertising, marketing, performance marketing kinds of areas.

John Wallace: Yes, absolutely.

Sramana Mitra: And was Data Song also sitting in that echo chamber?

John Wallace: It was, and then, I don’t mind sharing this for other entrepreneurs. After selling DataSong, I thought that the last thing I want to do is work in that field again. I want to do something fresh, something new. I did work on a couple of pet projects, around the data of agriculture and taste. Then I rejoined my team and started working on marketing again. The difference is, we’re not just doing a kind of rebrand or repolish of the last algorithm. It’s a completely new data set.

Part of what the call to action for building another company is that the data we used in our old company was going away due to changes in privacy and changes in regulation. You’ve potentially heard about the back and forth a couple of years ago between Apple and Facebook and tracking. I foresaw that. I knew that changes were imminent, I couldn’t name them. We wanted to get ahead of it and plan a software that could work with new data sets. That was the motivation. We said, “Let’s not even worry about some of that data. Let’s run experiments in order to fabricate data.” The changes to iOS 14 really did validate our hypothesis. As I like to say, I think I owe Tim Cook a bottle of wine! He kind of made all of our speculations real with those changes to iOS 14.

Our conversation continues here.

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