Match Group, Inc. (MTCH) Earnings Call Transcript & Summary

Great question. We're still in the very early innings on recommendations, maybe the third inning. And today, every major release that we do still moves our core very substantially, right? And that's what early innings looks like. As you can see on this chart, we've made a series of major recommendation updates over the past 18 months. and Spark, which is, as Mark said, these 6-way high-quality conversations, they move meaningfully higher as the system has evolved. Codification V2, which is this big launch that we did in July, produced a single unified, coherent recommendation system that optimizes for one clear objective, which is Sparks, right, and Spark coverage. And when these very mature systems get tuned, right, the gains are very incremental. But the fact that we're seeing these step function changes every time we do a major release shows that we're still in the early innings.

So this term queue unification is something we kind of throw around internally. So let me make sure that I'm understanding it and also take a pass at explaining it to viewers. The way I think about it is we have maybe 10 or 15 different decks of cards. And maybe one deck of cards is sort of optimized to maximize revenue to the company. Another is optimized for the new user experience and other is optimized for, I don't know, retention of an existing payer, et cetera. And when the user comes in, let's say, Mark comes into Tinder, and we're trying to decide what recommendations to show him, we decide, based on what we know about him as a user, which deck do we want to pick up. And we pick up this deck and we start showing the card 1, card 2, card 3. Queue unification is basically taking all of those decks that are currently independent and combining them and reordering the sort of that much larger deck of cards based on what will maximize Mark's chance of not just matching. So not just saying, yes, I like this person, and she says, yes, I like Mark, but actually post match them arriving at a spark, a 6-way chat. Have I described queue unification perfectly?

Yes. I think you're spot on, right? So previously, every deck of cards had its own objective. As you said, right, on deck of cards was to maximize revenue for users that may be were at risk of churning and then one deck of cards was to help get new users a bit of a boost in the system so that they're retained. And now we don't have all these 15 decks. We have a single deck of cards and the only objective that our machine learning algorithms optimize on is sparks, right? And so that has been a complete game changer for us. It's really driven Sparks significantly higher for straight women. And we've got many other, I would say, segments of our ecosystem, which is not yet rolled out, and we're still continuing to roll it out.

Now the other aspect of our current system is that once you pick up the deck, whether it was 1 of the 15 or now the combined unified set of cards, right now, when Mark says swipe right on this person swipe left on this person, right on this person. The sequence of cards in the deck does not currently change real time. Can you describe this financial Yes, absolutely. So today, in the Tinder ecosystem, right, if your swiping behavior changes, if you're looking for something different today versus what you were looking for last week, it can take up to 4 hours for the system to update and to start to reflect that change in preference, whereas real-time adaptive recs, which is something we're working on. It's going to launch in late Q4. It's going to start having the system react in seconds to those changes in behavior. And we think that's going to be a big step change in outcomes for our users. And this is, of course, what we see in other services. If you're on LinkedIn and you're watching a video about a particular topic, you'll start getting more videos about that topic if you are in TikTok, Instagram, even Spotify, if you're skipping certain types of songs, the recommendation algorithm changes real time and you sort of feel it as a user. So that's coming in.

Absolutely.

So what's -- there have been people working on these problems for 15 years or so, kind of the whole history of Tinder. What is it about this moment in time in 2026 that's created this unlock these step changes that are available to us. Why now?

So previously, we were optimizing for likes for revenue and the big shift that's happened over the past 18 months is we've started to optimize for sparks and user outcomes, and we 100% believe and we know that sparks and user outcomes translate into mile growth. In fact, we believe that these even one step further we can go, which is targeting not just conversations or 6-way conversations, but really high-quality conversations can drive even better mow growth. In fact, this year, we have a user give back budget in place. that our teams can make changes that maybe drive engagement at the expense of revenue. In practice, we've seen that most of the changes that we make that drive engagement also drive revenue. So in practice, we've seen, we don't really need to use that user giveback budget. But the fact that we have that budget in place sets the right culture for our team so that they know they need to optimize for user outcomes.

It's -- I mean investors hear us talk about this usually give back thing all the time. So it's great to hear how it actually goes from like the Board to the investor community to where the real work gets done with engineers actually making changes to the recommendation algorithm and the freedom and latitude that -- and therefore, innovation and positive results that have come from it. Okay. So Mark, users are, of course, getting more sophisticated, and they're starting to understand all these different algorithms. I mean even the way users think about these LLM models now, like I'll hear random people, not even engineers, just people, people saying, like, "Ah, using SONETr using OPUS, are you using Fable,these different AI models. So it seems like people's sophistication about the way they interact with technology has increased. How are we taking that insight and bringing into how we talk about our recommendations with users?

Yes. So we learn a lot about what a user wants based on their site behavior and other things they do on the app. And previously, that was all kind of hidden to the user. But what we've realized is that we want to expose that. So we're working on different ways of presenting that information, telling them, hey, this is what we think you like based on your behavior. If we've got it right, great, let us know, and we'll keep on doing more of what we're doing. But if there's changes you'd like to make, let us know and we'll adjust very quick.

So we're trying to give you more transparency and control over the product themselves. So this obvious pushback to this product velocity initiative is just shipping more isn't always necessarily better. So how do we make sure that we're solving the right problems not just shipping more stuff?

Yes, that's a great question. Velocity, it's not just about shipping the most stuff, it's about shipping the right stuff. And the way that we make sure that we're doing that is to make sure that we start with a real consumer need grounded in research and a ton of direct member feedback. We take all of that information and then we're very liberal with what we prototype. We try a lot of things internally. We see what feels good. When we see something that we think has a high likelihood of improving the outcomes for our users, we'll then test it publicly. And then if we see that it drives sparks, that's when we decide to scale. If we launch something and it's not actually driving these positive outcomes, we don't move forward with it. We also regularly do an assessment of everything in the product, and we ask ourselves, is this feature driving the impact that we thought it would. If it doesn't, we take it out. Everything we saw in that video earlier today really solves a very clear human need, whether that's better discovery, helping people express themselves more authentically, lowering the pressure with every connection. And we also just have this tremendous design team that really works hires to make sure that all of the work that the product and engineering team is doing fits together nicely.

What stood out to me and the video is how much more social Tinder is becoming? Can you elaborate a little bit on that strategy?

We have spoken to thousands of our members and thousands of singles off the app, and we're asking them what do they want, right? And I think the thing that comes up time and time again is they want to bring their friends into the experience. They believe -- we believe that friends will lower the pressure, improve safety and really make everything more fun. And we have a lot of evidence that supports this. We launched Double Date last year. And in the U.S., more than 1 in 5 singles between 18 to 22 on Tinder has a double date pair. And when we talk to them, we hear they're talking, they're having more great conversations, the conversation tone and vibe is more fun. When they meet, they feel safer. It's lower pressure, there's lower expectations. And they've even come to us and said, hey, look, it's fun to be with one friend. We actually are a Double Date pair. But let's add more people. So a little bit of a sneak peek that we can maybe talk about in future events is that we're working on group hangouts now that supports more people. We also have our events feature that we've talked a lot about and a great learning there is that people bring their friends to those events, right? Very rarely do somebody come alone. So we're continuing to layer social into that experience, too. The whole point of this is that we're trying to talk to our users understand what it is they want and then give it to them. And I think by doing things like building double date, testing groups, building events, we're giving people new ways to connect that will drive reconsideration and bring new people into Tinder.

Tinder is better with friends. All right. So last one, beyond more real-world connections and recommendation improvements, what are the other big things that you are both focused on Mark?

I think one of the big pushes right now is really making sure that our matches become sparks, right? The fund doesn't -- the fun really begins after the match. People come to Tinder because they want to have great conversations, and ultimately, a great time together in real life. I think really, this post-match experience has been pretty underserved in years past. So we're really excited to have, as Vinay said earlier, completely rewritten our chat infrastructure, which is letting us now build on top of it, these great experiences to help smart conversations, help people plan meet-ups, we've got a lot of momentum going here, and I know that Aspell use the app over the next couple of weeks or a couple of months, they're going to see a huge difference.

Vinay what are you focused on?

So historically, a lot of our brands at Match Group have operated very independently. And I'm excited that we're starting to take more of a one match group approach to a lot of our tech services. The central Match Group AI team pursues longer horizon bets like conversational and Agentic AI that all brands benefit from. And the infrastructure that we're using, the GPUs, the machine-learning platform is increasingly shared across our brands. So our AI investments are reflected across the whole portfolio. And when we build trust and safety features, we build it once and we deploy it everywhere across all our brands, so things like age assurance, verification, AI moderation. They serve every brand and the learnings that we get from Tinder scale across the whole portfolio. And of course, Tinder's AI, dev tooling and agent platform will soon be used by other brands at Match Group as well.

Yes. I mean, again, we just had this product leadership offsite with leaders from around the world. And it was just amazing seeing so many people working on so many similar problems like we had people from our Tokyo office pairs and our Paris office who work on Meetic and our Vancouver office who work on recommendations and our L.A. office, who we're working on Tinder. And they're all focused on a lot of the same problems. How do you improve trusted safety? How do you improved recommendations? How do you people show up more authentically? How do you bring more friends into the dating experience. So getting more knowledge sharing and then, in some cases, more than knowledging actual shared technologies has been a big priority of mine, and it's great to actually see it starting to have an impact? That's all the time we have for today. I hope the product video helped bring to light just how much Tinder has changed. And more importantly, I hope our discussion helped explain how we're making it happen. We are now understanding consumers better than ever. We're building faster and more thoughtfully. We're using technologies like AI to accelerate that. And I'm going to let Mark and Vinay get back to building. So back to work guys.

Thank you.

Thanks, everybody.