Ep 564 7x on Paper. Break Even in Cash. Adam Spector on Staying in the Game After a Sale.

Colin Morgan (0:01)

Hi there, and welcome back to another edition of Built to Sell Radio, the podcast designed to help you punch above your weight in negotiation to sell your company. I'm the executive producer Colin Morgan. Now, ask an owner what they will do after they sell, and most say a version of the same thing. They want to stay close to the action, back a few founders, sit on some boards, help the next person do what they did. Now, it's a good plan, but it just takes longer to pay out than anyone expects. Adam Spector co-founded LiftIgniter, which used machine learning to personalize websites the way YouTube does. Now, when growth stalled, the board hired a CEO to sell.

Adam wanted to keep building, but was outvoted two to one. So, that's what they did. Put his money into other people's startups. More than a decade in, that portfolio is up seven to eight times on paper, and roughly break even in cash.

Listen for how long a paper win takes to become money you can spend, and for what staying close to the action really looks like. Without further ado, here is Adam Spector. Enjoy.

John Warrillow (1:13)

Adam Spector, welcome to Built to Sell Radio.

Adam Spector (1:16)

It is great to be here. Thanks so much for having me.

John Warrillow (1:20)

You're welcome. You and I are members of an organization called PEF, post-exited founder.

I think that's how we actually originally got in touch because LiftIgniter had a pretty big exit. Maybe walk through that and how that kind of changed your world.

Adam Spector (1:36)

Yeah, absolutely. So John, so LiftIgniter was a really interesting company.

The thesis at the time was we wanted to personalize every single internet action or activity.

And the idea behind that, so this was, you have to think back. So this was like, I'm going to double check my memory even here. The 2014, right? So 2014, it's pretty early days in the world of personalization.

All of us are used to, when I say personalization, if you go on, I see you look at YouTube regularly, most people do, your YouTube feed is completely different than mine, number one. But your YouTube feed actually also changes in real time. So if you're on the go on your phone, your feed might look a bit different than when you look at it on your computer sitting at your home or your office. It also might change based on your searches, based on activities, you name it, right? Like if you fly to a new country, you might start seeing new YouTube videos about being in Paris, if that's where you flew to or something. So my co-founder at the time, he actually came from, he was one of the five people who actually created the YouTube machine learning algorithm to personalize YouTube. So the first mass use of YouTube, of machine learning, not artificial intelligence, this is like very pre artificial intelligence, to personalize YouTube and they saw massive gains. So he decided to leave, it has an amazing background, decided to leave YouTube and Google, and went through Y Combinator, and then basically LiftIgniter was started. And with the idea being, we want to go personalize all the other websites. So shopping websites, imagine you're looking for, you're wearing a nice black t-shirt it looks like right now. You want to go personalize and get a great new black t-shirt. Well, how do you go find that t-shirt on the website? Or with news and media, right? Well, YouTube seeing 20, 30, 40, 50%, I don't remember the exact numbers, improvement in click-through rate and time on site, these metrics that really drive revenue for YouTube, why can't everyone else drive the same numbers as well? And so that was a thesis of LiftIgniter as we were guiding your lift in a sense.

John Warrillow (3:36)

So you sold to website owners, people who had websites?

Adam Spector (3:40)

I sold to media website owners and essentially brand owners, right? So you're selling your product online, you want to increase the value of the shopping cart. And so we would sell to those, those were our two target markets.

John Warrillow (3:53)

And what was the revenue model? Did you do a rev share or did you sell it just like right up?

Adam Spector (3:57)

One of the hardest challenges, great question. It was rev share times, we tried to do, so we tried to do rev share, but doing attribution was really difficult, right? So deciding, was it because of us that you had this increase in sale or time on site, or it's because of something else? And so that was really difficult frankly. The other one we tried to do is sort of sell, essentially a SaaS agreement, right? So number of engagements or people on site or website visits, we would price that out, essentially on the amount of data flowing through our systems would be sort of more of a SaaS price.