Lottomatica Grp Spa U/Adr

Good morning, everyone. Welcome to L'Automatica Q1 2025 results presentation. I'm here today with our CEO, Guglielmo Angelozzi, and our CFO, Lorenzo Vallangher. Now the floor directly to Guglielmo for the presentation. Guglielmo, please.

Thank you, Mirko. Good morning, everybody. Very happy To start with page two of the presentation, we're off with a very good start of the year. This is at market level. Online market continues to grow in a very healthy manner in the first quarter of 2025, with 18% up compared to last year. Payout for sports is very good, better than the normalized levels, both online and retail, and much better than the same period last year. And Lotomatica continues to outperform the market. This results into the best Q1 ever for us at $220 million. million euros of EBITDA, which is 47% up on our reported base compared to the same quarter of last year. Let's now go to page number three. It's important here to There's a bunch of things in this call, and we wanted to focus on the short-term good news, which are several, but also then have a deep dive on the structural advantages. In terms of short-term good news, We're happy to announce that we increased the target synergies for the PWO former SKS integration of 12.5 million euros, all cost synergies, which brings the target at 87 million euros by 2026. 61% of the synergies are already secured to date. And another very important element that I want to share is that the platform migration is underway and on track. This is usually the most complex part of an integration. All tech developments have been completed and we've pressed the button both for the retail and online. And the migration process will proceed and complete during the summer, most likely ahead of the original plans. The second very good news is that, as you know, but it's important to stress it, that successfully refinanced more than 50% of our debt, 1.1 billion euros, with rate savings in interest costs of 24 million euros per year. At the same time, we've received an upgrade on the rating, both by Standard & Poor's and by Moody's, and we've extended all maturities to 2030 and beyond. Last but not least on the short-term good news is that following the authorization from the AGM on the share buyback, The board, as yesterday evening, approved the start of the buyback by June, by next month, basically for the next 18 months, up to 10%, which is roughly 500 million euros. And this is... Again, a reaffirmation of our discipline on capital allocation and focus on shareholders' return. Now, going on to the structural advantages, given the macroeconomic context we are in, we thought it was important to stress again a couple of things which relate to more to the sector and to the sector in Italy. and not only to the company itself. Well, first of all, we have not even written it, but we are resilient to tariffs. That's pretty much self-explanatory. But more importantly, we believe the business is very resilient to macroadvance in general. This was the case in previous economic shocks and recessions. Think about 2007-2010 global crisis, global financial crisis, high inflation period lately. So resiliency to the tariffs, resiliency to slowdown of GDP, resiliency to inflation, all very good news underpinned, in our case, also by a business model which relies upon a franchising model. So with a big part of our cost base, which is, by the way, variable. The other point is around the business model. We've talked several times about what are the key elements at the base of our success and we've highlighted a few, but we want to take the occasion, like we did last time, in focusing on the long-term opportunity of the channel switch. to focus a little more on what's really at the heart of our competitive advantage, what has been at the heart for now and for the future. And so on top of the omnichannel, on top of the multi-brand, what is this asset which we call Automatica Core? which is basically about a unified technology, data, processes, and organization, which is delivering superior competitive performance. Now, let's go for a second to page number four. I don't want to spend too much time here. You have plenty of data. that allow you to compare the GGR of the Italian market, of course our addressable market, to, for example, the GDP changes. The first graph on the left is during the period of the global financial crisis. The graph in the center is what's happened pre-COVID, during COVID, and after COVID, and also lately the reaction of the Italian market to inflation. also compared to other discretionary consumer spending sectors. So the Italian gaming market has proven to be very resilient. Now let's go to next page, page number five, and let's deep dive to the source, one of the sources of our competitive advantage, also hopefully for the years to come. We've seen this model in the past. These are the six pillars of our business model, which are the backbone of what we're doing. And of course, there is omni-channel. Of course, there is multi-brand. But we wanted to show you how the other four elements really are built inside and how you should look at them in reading the current performance and also helping you understand where the company is going. It all starts with the proprietary technology. in terms of gaming platform and then AI platform. Then it goes into our integration capabilities. When you talk about integration capabilities, many times people think about the integration that you do of companies when you buy them. And that's, of course, a great source of value which we've been able to leverage in the past few years and currently. But there's a part within it which is really about the integration of data, customer experiences, processes, digital marketing capabilities that together with the proprietary technology drive an excellent offer and maximize customer engagement. Let's go one layer deeper. Page number six. What does this really mean? When we talk about proprietary technology in terms of gaming platform, it means that we do own our tech stack. It's just called Pegasus. All the key modules are in house, the player account management, promo engine, the betting platform, the risk management, and I can go on for a while. And these assets are integrated across the brands. This is a first huge enabler of value creation and of what I'll tell you in a minute. Second side is the proprietary cross-brand AI platform, which is called LAMP, which is based on a dedicated data lake. It's based on the integration with the gaming platforms with external modules like the CRM. with external data sources, market trends, geolocation data, third-party data vendors. I'll get that into a minute. This is the enabler. Then going one layer up, most importantly, data. We have, apart from transactional data, we have a huge amount of behavioral and third-party data which provide an incredibly insightful in-depth single customer view. We have tens of data sources, hundreds of digital custom variables, hundreds of business custom variables, and we have tools, which you can see under the voice customer experience, which allow us to accumulate additional behavioral data on the customer experience. How is the customer moving in its journey through our assets? How can we replicate and improve the customer journey through the hard analysis of this data? This is done through processes and practices which are built around an operating model which is customer journey led. So it's in the view of the customer. And of course, several advanced digital marketing assets, which I'll talk in a minute. These all together lead to offer excellence and customer engagement of quality. which now we've opened a couple of items for each of them, and I'm still at the theory part, but we want to share with you the framework. Then we'll go into a few examples and numbers. You want your offer to be large and deep. All this infrastructure that I mentioned to you allows us to have a wide and deep offer to meet the preferences of a large set of customers, improves the opportunities of cross-selling, and improves our capability to do the proper pricing both in sports betting and in casino. In terms of customer engagement, the two most notable items are that we are able, through this infrastructure, to do customized activation, basically relevant content at the relevant time in general, but also to the relevant customer in terms of communication, which is our capability to personalize messages and content upscale through different platforms, offering a very unique proposition and communication to every single platform. In a nutshell, which is page number seven, this Lottomatica core is in the end an integrated Martech ecosystem, which realizes a strong integration between transactional data, All of us, all of the players in the industry have a ton of transactional data, but we are able to put this data together with deep behavioral data, deep third-party data, and this allows us with our engines, with our models, with our AI agents to boost personalization and maximize customer engagement. Now, three quick examples, and then I'll get to the bottom line of this. Page number eight, A couple of examples on offer excellence. AI-led risk management. We've set up an agent which basically does, accepts roughly half, 45% of the sports betting tickets automatically, which means two things. We improve payout and we improve customer experience. 150 milliseconds on average to accept these tickets. This is great, and the percentage of tickets which are managed this way is increasing through time. On the other side of the slide, you can find another application of this MarTech ecosystem, which is, you know, unfortunately, we have bots betting against us, and they're very smart, so we've set up an agent which is fighting against this, basically. We first launched that on the Two most important brands, Goldbet and, most important in terms of size, Goldbet and Betel Automatica. And as you can see, after a while of using this system, basically the, let's call them, approaches from these bots have basically gone to zero. Then we moved out onto Betflag, same story. So this is basically about early inhibition of non-profitable speculative users. Just to give you an idea, the average payout of these tickets which have been rejected is 104%. And after a while, these guys, these bots, they turn somewhere else because clearly they're making less money with us. Another example is on the offer excellence page number nine, an AI agent for predictive model of casino content offering. The standard way for casino offering is usually static or algo-based. It goes to the generic player. You don't target the player. It's the marketing team choosing which products to list and how out of the library. You can have some algo-based proposition, which is relatively static. You can put new content and so forth. But this is for, you know, all the players. Then we are switching to an AI-based system, which is driven by the behavior of relevant clusters of customers, driven by the potential popularity of some games for certain relevant clusters of players. It's based on pricing, so the payout, the RTP of the games. And, you know, there are lots more variables that can be added than we are adding to the model. The important point is this is player-specific. It's already in production. It's in production on a subset of customers, and it's going to be extended over time to the entire customer base. And this is about the breadth and depth of the offer. It's about the customer experience and it's about the pricing also. Another example is tailor-based communication for the customer base to boost engagement and ARPU. You can study, these are examples clearly, you can study from time to time relevant clusters of players, for example, those who have... lately lost money on sport or casino or both. And you can do tailored communication or bonuses to them. And we've seen that you can have a material improvement of the weekly ARPU of those who are subject to this type of campaign vis-à-vis the others. Or you can go, for example, in sports, but you can reply the same on casino. You can identify a group of customers which is particularly sensitive to certain type of triggers in terms of novelty of bets on some soccer teams or on some players. And there is an example here of a day of main teams match when the agenda, when the calendar was full. compared to another period of time, Pisa, Vellino, Foggia, three teams which are not particularly usually in the top tier, but when there was less offer in the calendar. And with this type of engagement, we've been able to achieve huge numbers on, say, second-tier matches compared to first-tier matches when the calendar was crowded. Last but not least, another example of customer engagement. If you're able to identify preferences in your customer base, for example, the type of sports, the type of leagues, the type of teams, and you have an AI agent which allows you to generate a campaign automatically, for the relevant target talking about the image creation and the creation of the the campaign you can achieve you can literally do this in a minute in a minute compared to like hours or half a day of work which means this is basically online real time and it's scalable and the click-through rate When you present content which is in line with the preferences of your cluster of customers, it's three times, the click-through rate improves three times, and with this, the economics. So these are examples, but this is to show you page number 12. that, you know, we started these in 2023 with Goldbet and Better. Now then we applied a part of this to Betflag. Now when the migration is completed, these and additional features will be applied to Planet Twin and then next year to Totosi. So the story in the end is this is a source of information sustainable competitive advantage. But full potential is yet to be captured because it has to go through all brands. It has to go full scale on all brands. And we can add, and we have plans to add more and more features along the way. So I hope this helps put it in context, also having a long-term perspective on what we're doing. Now with this, I hand over to Lawrence. Lawrence, please.

Thank you, Guglielmo. On page 14, as Guglielmo said, we had a good start of the year with revenues growing plus 33% year-on-year, reaching 586 million euros in Q1 2025, compared to 440 million in Q1 2024. EBITDA is up 47%, totaling 220 million euros in Q1 2025, compared to 150 million euros in Q1 2024. Margins have also increased from 34% to 37.6%, also supported by the favorable payout this quarter. Moving to page 15 and looking at the financials by segment, we continue to see strong growth year on year in online revenues, plus 59%, as well as sports franchise was also plus 59%, and broadly flat in gaming. Similarly, Looking at adjusted EBITDA, online has grown 55% year-on-year, sports franchise at 132%, and gaming flat. So you can see at the margins and as a result also the growth in sports franchise has benefited from favorable sports payout in this quarter. We're on to operating cash flow. So if you look at the left-hand side, you can see the profile of our capex spend for the first quarter. So recurring capex are flat year-on-year at 21 million euros, broadly in line with what we had last year. And this is comparing versus Q1 2024 that does not include PwO. So this shows you just the scalability of our model in terms of capex. Concession capex are in line with the guidance we have given this year. So we spent 15 million euros this quarter. And the 29 million euros of one-off growth capex include predominantly carryover items that we had guided to in our full year 2024 results. So carryover bolt-ons of 15 million euros and deferred payments of 4 million euros. This excludes gold debt. On top of this, we've also got the integration costs in CapEx and related to the PwO that amount to 10 million euros. If you're at the right-hand side, you can appreciate the year-on-year improvement on the operating cash flow where we increased from 110 million euros in Q1 2024 to 184 million in Q1 2025. And this is driven by the improvement in EBITDA, as well as benefiting from the scalable CapEx that we just discussed. We go to page 17, so we can see the evolution of the net debt decreasing to 1.8 billion euros at the end of the quarter, so in terms of net financial debt. We have EBITDA of 220 million euros if you look at the path from end of year 24 to end of first quarter 25. So EBITDA of 220 million euros. Networking capital slightly negative. It's typical of the seasonality, of our seasonality. 65 million euros of capex that we described earlier. Then we paid 10 million euros to the sellers of Goldbet. This is again in line with guidance. We had 27 million euros of payments due. We paid 10, so we're now left with a bit more than 17 million euros left to pay. Then you have financial expenses and leases of 29 million euros and other items, which include integration costs of about 11 million euros. This brings us to a net leverage level of 2.1 turns. So we're at the lower end of our financial policy that we've stated we've had since the time of IPO. We want to stay in a range of between two to two and a half terms on a steady state basis. And this also makes it the right time to start our buyback program. Page 18. This is a quick recap of the refinancing in which we refinanced a bit more than half of our total debt stack. We did it on the 24th of April. where we refined 1.1 billion to redeem the 565 million senior secured notes due 2028 and 500 million euros of floating rate notes due 2030. This was attractively priced at 4.78, resulting in a run rate savings of 24 million euros per annum and an overall cost of debt slightly above 5%. Our credit rating also has been reviewed one notch up to BB by S&P and to BA2 by Moody's. And with that, we completed the presentation.