Match Group CEO: Digital Dating, What Makes a Good Profile and the Loneliness Epidemic

You don't wanna date a loser. Right? Well, there's... You know, we believe there's someone for everyone, so I don't pass judgment. There's no such thing as a of your business model, isn't There's such thing as a loser. They're just somebody that hasn't met their their right person yet. Oh, that's... That sounds, that sounds really good. Hi, everyone. I'm Nikola Tangen, the CEO of the Norwegian sovereign wealth fund. Today, I'm joined by somebody really special, Spencer Roskoff, the CEO of Match Group. That's the company behind Tinder, Hinge and around a dozen of other dating apps. Now, think about it for a second here. 40% of couples today meet online. Not through friends, not at work. So, this is one of the biggest shifts in human behavior that we have ever seen. And Spencer, he sits in the middle of it. A bit of an outsider, he built the property giant Zillow before taking over a match a year and a half ago. And we, in the Norwegian sovereign wealth fund, own 1% of match. So, are cheering you on, Spencer. Thank you. I am glad that you are. I appreciate it. Thank you for having me. Now, how are matches actually made? We have a recommendation algorithm, the Rex Algo, which tries to figure out who to show to whom. The way I like to think about it is, there are decks of cards kind of on the table. Imagine 10 decks of cards on this table with 52 cards each. The order of each deck has already been determined by us based on certain rules associated with each user type. Like, there might be a deck that is optimized to show people in a particular order to keep to to keep them as a payer, essentially to retain their, you know, their monetization. There might be another deck for first time user to drive engagement based on first time users. There might be all these different goals. What we've recently done was we've done what we call queue unification where we've combined all of these decks into one and then resorted the cards. And that's radically improved the quality of recommendations as measured by matches and chats and sparks. And so what exactly so what exactly has has happened there? We've basically customized and personalized the order of a much larger stack based on what we know about you as compared with other people who have exhibited similar behavior to you in the past. And is that based on who I like? Yes, exactly. So if somebody likes people with red hair, they see a lot of red hair. They will, but one of the big limitations is they'll only see people with more red hair a couple hours later. So right now, our recommendations are not real time, which is a big When will it be a red time? By the end of the year, which is the way other recommendation algorithms work. If …