Match Group, Inc Investor update

Hi, everyone. Thanks for joining us today for the CEO connection this quarter. Before we get started, I want to note that today's discussion may include some forward-looking statements, and the risks related to those are listed here and also in our filings with the Securities and Exchange Commission. Today, we're going to go inside Tinder's product and engineering teams to discuss something that you've heard us discuss quite a bit, which is our product velocity. It's a very important part of the Tinder story and something that investors are keenly focused on, and I get asked about a lot, for good reason. Today I'm joined by Mark Kantrowitz. Mark is our Chief Product Officer at Tinder. He leads our cross-functional teams of designers and product managers who help determine what we build into our product.

Mark has more than 20 years of experience as a product builder and an entrepreneur and a founder, including roles at Zynga and his own startups. Also with us is Vinay Kuruvilla, CTO of Tinder. Vinay leads engineering, AI, and product innovation at Tinder and also leads our central AI teams at Match Group. Vinay has 20 years of engineering and product leadership experience at companies like Amazon, Venmo, and Brightwheel. Before we get into that discussion, we're going to start with a quick look at how much the Tinder experience has evolved over just the past 18 months. Many of you probably aren't active Tinder users, so we wanted to bring the product to you by highlighting where it was, where it is today, and just how much has changed in a relatively short period of time. Let's take a look. It's pretty cool to see.

A lot of blood, sweat and tears went into 18 months. Hopefully, the video gives you a more tangible sense of what we mean when we talk about product velocity. I've been a tech executive for 28 years, and I have never seen a company change its core product so much in such a short period of time. What you just saw is the output, which I'm very proud of, and it's impacting our users and our business results every day. What I want to spend today focusing on is how we get it done. Mark, I'm going to start with you. We just showed how different Tinder looks today than 18 months ago. Let's start with the obvious question. What's changed, and why are we able to move at this pace today?

As we just saw in that video, nearly every part of Tinder has improved. Not only does it look different and it feels better, but more importantly, it actually works better for our users. We've had major strides in trust and safety, bringing down the prevalence of bots and bad actors by more than 60%. We greatly improved recommendations, driving better outcomes and sparks for our users, and we built new ways to connect, like Double Date and events. There are a few things that are really driving that change. The first is the org. The org has changed dramatically. We have smaller, more autonomous teams making much faster decisions. We've really sharpened our understanding of who we're building for and the problems we're solving with the use of new personas and a lot more time and focus directly talking to our users.

Lastly, we have reoriented the entire team around one North Star metric, Sparks, which is our term for a multi-way, six-way conversation. It really aligns everyone around driving better outcomes. The result of that is not only a product that works better, but a team that moves a lot faster.

Yeah, we just finished a three-day offsite, with products and engineering and marketing leadership from around the world coming together here in L.A. It was amazing how often personas and Sparks came up. In every conversation, people were talking about the personas. So these are the archetypes of whom we're building for. Sparks, obviously, is the KPI that we focus on. Vinay, take us under the hood from an engineering standpoint. How does Tinder actually build differently today?

Yeah. So Tinder's entire operating model around how we build has completely changed. We've got fewer linear handoffs, really tight collaboration with product design engineering, faster experimentation and iteration. Our engineering team is actually shipping at a velocity that's twice as high as it was a year ago. Faster shipping matters because it creates faster feedback loops where we can build, test, learn from our users, and improve based on the data and the metrics that we're seeing. We've made a number of investments in our technology stack. For instance, we've rearchitected parts of our code base which had a lot of technical debt, and were slowing us down, like our chat system. We've invested in our infrastructure, so teams like our recommendations team and our machine learning team can move a lot faster.

We've invested in our experimentation platform so that running and reading the results of experiments can be very cheap and fast.

Vinay, you mentioned tech debt, and this is a term that I know investors are familiar with because other public companies talk about it from time to time. What is our philosophy for paying down tech debt and maintaining pace of innovation even as we are kind of retrofitting old parts of the product?

It is a great question. Our philosophy has been to incrementally eliminate the tech debts and looking at the areas of the code base where the teams are getting slowed down the most, as well as the components in the product where we have the most potential to improve user outcomes. Chat was a great example, right? Our chat team was really getting slowed down because of tech debt in the code base. There was so much potential to modernize chat, add a lot of new features. We rewrote and rearchitected our chat system. We did it in a way so that every other part of our ecosystem could move forward while we were rearchitecting our chat system. We are going to continue to do that. Our next step would be onboarding. That is our approach to tech debt.

One for both of you. How does AI fit into all this from a product ideation standpoint and then from an engineering standpoint?

AI plays a critical role both within the product, how it benefits our users, but also how we build those products. Within the product, we use it to reduce friction during onboarding, helping people make better profiles, selecting better photos. We use it to improve trust and safety through features like Are You Sure? and Does This Bother You? in chat, as well as Face Check. We use it to improve our recommendations. We also use it in terms of how we actually build better. We have seen that AI essentially accelerates the entire product development life cycle from research and ideation, rapid prototyping to development. It essentially compresses the work that used to take months now into weeks and sometimes from weeks into days. The result of that is it essentially has let our teams have far more high-quality shots on goal.

We could take an example of events, which we're all very excited about. This was an idea of Spencer's back in January. We had our first meeting in January. We had rapid prototypes days later. In March, we launched our first MVP publicly to the people of L.A.

Just eight weeks later. Which is amazing to go from first meeting in January to public launch just a couple of months later.