Unless there is a very, very big change in how these AI and large language models work. One thing that they still don't have and probably won't have in the near future is judgment and taste. Hello everyone and live from SBC Summit America's 2026. We are here for another episode of Eye Gaming Daily brought to you by OptiMove, the creator of positionless marketing and number one play engagement solution for Eye Gaming and sports betting operators.
I'm Fernands Nonot, media manager for SBC and your host for today. We'll wait a very special guest today, someone from OptiMove themselves, Shai Frank. How are you today? Shai Frank, SPP of product and GM of America's for OptiMove. Yes, that's right. Thank you. Hi, good morning. Happy to be here. How are you? All good, all good. How you find the event? Yeah, very exciting event. We've been here for quite a few years in these events. We have a big booth.
We enjoy the industry and the people that we know our clients and prospective clients, some of our competitors. It's an exciting event. Happy to be here. And happy to have you back on Eye Gaming Daily. It wasn't with me, it was with Joe Streeter back then. But ever since you were with us last time, OptiMove has launched OptiMove AI. So I want to get into AI because of course it's a big topic of conversation. Everyone is talking AI here at SBC Summit America. So you launched OptiMove AI.
So how does that impact your positionless marketing vision going forward? And what does it mean for OptiMove? It's a good question. And maybe a short refresher on what positionless marketing is because I'm not sure everyone is aware or remember. It's a simple concept. It means that over the years technology in general and obviously AI in particular is allowing people to extend their range and be less in a small box.
So think about historically how companies in general scaled their processes, marketing including. You establish an assembly line with multiple workstations and multiple teams or people that are specializing in specific tasks. So if it was a car assembly line, you had the Henry Ford situation like 100 years ago. But even with marketing campaign, you had the people that if you wanted to do a campaign, you had to request assistance from IT or engineering to help you with the audience.
They would run some SQL queries to pull up a list. So you had to wait for that. And then once you have the list, you need to hire an analyst to design an A-B test experiment. And you go and you wait for creative to come up with assets. And everyone is a specialist in their role. But the end result is that you have an assembly line with multiple weights and multiple people that have to work in order to get one campaign out the door.
And it scales well because now you can do in theory, you can do more of that. But in practice, the time from idea to execution was very long because you had to wait for all these five or six different specialists to give you their input in order to get one campaign or one journey out the door. And especially in the iGaming industry where things are moving so fast and player expectations are always growing. So we cannot afford having such a long period from idea to execution.
And we believe that as technology has evolved and AI is becoming more and more prominent, you no longer have to have so many different teams and specialists involved in one task. A person that could never run SQL queries is still able to analyze data on their own because technology surfaces data insights to them. And technology allows you to design a Navy test without being a PhD in statistics.
And you can analyze campaign performance without understanding the math behind it because technology just does it for you. And clients that we see and brands that we see that adopt this philosophy no longer are moving faster, but they also change their own processes and their org structures to facilitate faster, more agile movement. And they adopt the technology to just scale and move faster. And we have a lot of examples like that. So let's position this.
The inspiration is from positionless basketball. In the past, the tall player had one job, stand under the basket, catch a rebound and dunk it in. Nobody would expect them to shoot three pointers. But in the last several years, we see tall players, very tall players like Wambaniyama, are shooting three pointers, are dribbling. And this is positionless. So same thing with positionless marketing. And the recent release of, to your actual question, the recent release of OOptima AI accelerates that.
So marketers, the power of data and the power of creative and the power of optimization so they can do more by themselves. And the system supports them to do that so they can move even faster.
They can work from within OptiMove platform to ask questions about their customer data, understand hidden gems in customer segments, design campaigns, design audiences, design experiences, including the creative work itself within the brand guidelines, within the compliance and regulatory constraints, and just move faster. And we can probably talk more about that later.
Yeah, absolutely. And AI in CRM is often related or often linked to finding the right campaign for each player and personalizing the campaigns that reach the end user. So at the same time, operators have different goals, different objectives. So can AI, like OptiMove AI specifically, help arbitrate between objectives and not just campaigns? Yeah, that's a very good question. You know, I think intuitively when you think about how AI can help us personalize our customer experience further, right?
I think the recent, it's funny to say intuition because it's all like very new, but the recent intuition would be, okay, I want to target my customers or my players with a specific context or a specific offer, but I want to give the AI a bank of potential options, right? Different levels of generosity, different reward types, right? And I want AI to decide what's the best offer per player. That seems to be very intuitive of how AI can help you. And that is perfectly correct, right?
And part of the OptiMove AI suite is what we call offer decisioning. But here's the thing, offer decisioning, like the one that I mentioned, or even content variations, right? It works within the context of a specific campaign or a specific journey, right? I now want to cross sell players from sports to casino. That's the context.
And within that, I can offer multiple levels of, you know, free bets, free spins, deposit matches, each of them with different levels of generosity and the content creative changes, but it's still within the context of one campaign on one journey. But what happens when a player is eligible to different campaigns or different journeys at the same time, right? You have a life cycle journey for risk of churn and you have some seasonal campaign, right? The World Cup, right?
And maybe someone happens to be, it's their birthday week or their birthday month. And we also want to cross sell whatever players from sports to casino. And now all of a sudden, CHI is eligible to four different campaigns. But the underlying reasoning behind the campaigns are different objectives, right? If I'm a big sports fan, an operator may want to leverage my sports affinity to get me more and more engaged with World Cup, right?
So I have, so my World Cup campaign is trying to engage customers and players who are more into sports. At the same time, the same operator in general wants to cross sell from sports to casino, right? I might be in a high risk of churn and there is a life cycle that goes on. So we have different objectives, save customers from churning and usually you're willing to sacrifice some margins. So you're willing to be more generous when you do that. And I want to exploit more of my sports affinity.
So I'm more engaged with World Cup. At the same time, the operator wants to get me hooked into casino. Now I'm eligible to all of these three campaigns. Each one of them has different offers and different levels of generosity. Now how we decide? And this is where I think to your question, OptiMove can, AI can arbitrate between different objectives, right? So this is what we call journey decisioning.
So when a player or a customer is eligible to multiple different campaigns or journeys at the same time, typically those campaigns or journeys are serving different objectives. Then OptiMove AI needs to arbitrate between that. So it's no longer within a specific campaign I'm trying to maximize casino bet amount because I'm cross selling. Because the other campaign is trying to maximize retention rates in general and the other campaign wants to maximize NGR and not GGR.
So how do I arbitrate between all of these different objectives? And OptiMove AI in that case looks at kind of like the primary metric that determines the player's lifetime value. Beat GGR, beat NGR, doesn't matter. Each operator with their own definition of what a player lifetime value might be. And then OptiMove AI can arbitrate within that and say, okay, shy is eligible to a cross sale to a World Cup offer to a risk of churn their birthday.
What is the campaign today that puts shy on the path to maximize lifetime value in general, regardless of the specific objectives of each and every campaign? And then data changes, sports calendar changes, customer preferences changes, and we rinse and repeat and we personalize for each customer. Learn how OptiMove's positionless marketing is changing how iGaming teams operate.
Discover how operators are using OptiMove's positionless marketing platform to launch personalized CRM campaigns, dynamically change casino lobbies and bet slips, and create engaging game-of-life experiences. Learn more at OptiMove.com. And, of course, AI tools have already made it into the life of most people and most marketers actually use already Claude or ChatTPT into their work.
So how does that change the way iGaming teams approach or strategize around campaigns, segments and personalized player experiences? Yeah, I think it's funny. As a product person, for many years, when you wanted to build a product or you wanted to decide how your organization needs to work, there were always best practices, right? The world has figured out how to do software development 20 years ago with agile and things like that. So you didn't have to reinvent the wheel.
It's funny in these times, right? There are no best practices, right? Everyone is trying something and every day someone posts something on Twitter on LinkedIn, "Hey, look at this framework I created and look at the way that I'm doing things." And it's really exciting to be a part of that time. And we see different patterns emerging, right? We see people that they would prefer working with their own tools, right?
They are already working within Slack or within Claude or within ChatTPT in their own life, right? They want to meet them where they live. And on the other hand, we have specialized products and tools that have their own AI, right? Optimal AI within the optimal platform that has the benefits of creating seamless user experience between AI and point and click interfaces and an advantage with the context the platform can provide. So I think it's not either or, right?
There are different use cases and each use case you may choose the right path for you as a marketer or as a user to use. So when we built and released Optimal AI, we came up with this idea that says Optimal AI is inside Optimal. So you can use our own AI assistants and AI agents from within our user interface. They know everything about your data. They know everything about your campaign.
You build stuff with AI and you can seamlessly go from the output of what AI generates to the more traditional interfaces of, okay, okay, now let's tweak this with the mouse and keyboard and point and click and let's approve it. And you can go back and forth between AI and more traditional interfaces. And at the same time, we have Optimal AI outside of Optimal where you can use your own ChatTPT cloud, Gemini, whatever agent harness of choice to interact with Optimal's platform behind the scene.
So you can ask questions about your data and get results both from your own cloud skills that go to your own snowflake data warehouse. And at the same time from the data you have in Optimal and cloud in that case would be able to course reference between them. And we think this is powerful.
So we've released a new version of our Optimal MCP server who is highly, highly capable of answering questions about your data, creating audiences, creating campaigns, creating journeys, creating content and message templates. And from there you move into Optimal UI to approve and tweak, right? Nothing gets sent without human in the loop. And there's a third layer on top of Optimal, right?
Where now our professional services team can go and help clients with customizing very unique solutions and use cases that before SaaS companies like Optimal didn't really, couldn't really and didn't really want to do. You wouldn't customize software for individual customers, right? The SaaS industry always frowned upon things like that, right? We never do that. But now we have the infrastructure and we have the tools for our teams to go to a client.
They have a very unique use case, a compliance, a planning process, some content generation. And now our teams can help with and build custom solutions with AI. So for example, we help one of our clients create this planning app that fetches the sports calendar automatically on a daily basis, analyzes data from Optimal, finds the customers that have the affinity to each team or each match automatically, shows it in a nice custom application front end.
And in the click of a button, you create an entire marketing plan inside of Optimal with dozens of audiences and campaigns that are tailor made for that specific sports calendar of the week. This is something we couldn't have done before. And every client has their own thing, so it's hard to create a generic software like that. But now our teams can do it with the help of AI and the infrastructure that we build to support agentic workflows like that.
So the bottom line is that we are seeing different patterns of usage emerging. And we are building our product in a way that supports all of them. So you can work inside of Optimal UI with the context that you have. You can use your own agent of choice from outside of Optimal and interact with our platform. And you can get help from our teams on top of everything you have to customize solutions for you. And what has been the feedback from your partners to Optimal AI?
You mentioned the patterns just now. So I'm interested in that. So how does that work? And how do you work when you get the feedback from your partners and you identify those patterns that they use? Yeah, it's a great question. I think there's a lot of excitement in the world in general and with our clients and partners in particular.
When we released this new version of our MCP server, it included the option of working with Optimal Gamify, which is our loyalty, gamification and mini games product. So our MCP server supports our entire product portfolio, right? So you can use AI to interact with all pieces of our product portfolio.
But within less than one day, less than 24 hours after we released this, we had a client that used it to build an entirely customized loyalty front end that lives in their website and app within less than one day, right? Before we even completed the release notes, right? We just released the thing and a day later a client comes to us, "Hey, look what I built."
An entire webpage, which is not just demo, it lives in their actual website now that shows each player their loyalty points, their tiers, their progression across missions and tournaments and leaderboards and badges. And it's all customized, fully customized for their brand and the way they wanted it to look like and the mechanics of their program. And it's all based on our Optimal AI MCP server in that case.
And I gave you the example before of how our teams helped clients build this app for sports calendar planning. And we see a lot of excitement and a lot of people that are taking advantage of these new capabilities, as well as continuing adoption of features and capabilities that we had before, like decisioning agents, which are not entirely new. We just keep improving them all the time.
So we have a lot of clients that are using our journey decisioning, as I mentioned before, and are offered decisioning to optimize their level of generosity. And as capabilities and machine learning and AI models are improving all the time, every once in a while you get a step function on the capability and the adoption. And it's really exciting to see that. All right. Yes, sounds exciting.
And of course, in the future, it's also exciting because this technology keeps moving forward and advancing at a pace that is unbelievable. So how can AI in the future help iGaming operators with player experience and all that? Look, it's a great question. And the reality is that the planning and vision horizon has become very short. It's really hard. In the past, we were planning like a year ahead. We had a roadmap and a vision for like two, three years forward.
Now you can plan six months ahead. And by the time you get to month six, everything is changing. And Tropic just released less than two days ago their new frontier model, Fable. And with early experimentation, we seek a step function in its ability to reason and its ability to do things that you couldn't do before, right? And long-term things, which we didn't really know was possible less than 48 hours ago. So with the pace of progress, it's hard to predict what will be the future.
But I think it's safe to say that unless there is a very, very big change in how these AI and large language models work, one thing that they still don't have and probably won't have in the near future is judgment and taste, right? So AI is really good at getting an order from you and producing stuff. It can produce content, it can give you a summary and analysis. You can produce audiences and journeys and campaigns. But still, all of us are using AI in our daily basis or most of us.
And you can see that many times you have to, a human being has to steer the model to make good judgment calls, right? The model can do things very quickly, right? Like, "Oh, give me an analysis with my players." It will give me like a three-pager within seconds, like a three-page of all kinds of analysis and insights. But what out of these insights really matters for my business is something that AI hasn't figured out just yet.
So I think what we think is going to happen and already starting to happen is marketers or marketing organizations evolve from execution of plan, right? Build the audiences, build the tests, analyze the reports, create the journeys, analyze the journeys day in and day out. They evolve from that into becoming more of orchestrators and managers of agents. I have an AI agent that produces insights far faster and far more efficient than the human being go into querying some databases.
And I have an AI agent that can produce dozens and hundreds of audiences and campaigns within minutes. My job as a marketer is to apply the judgment that says, "What objectives do I need to make and what campaigns matter and which insights actually matter for my business right now so I can steer those agents into producing the right stuff?" All right, and when I get an email template produced by AI, the human has to apply their taste and say, "This is almost great, but you know what?
I need to change some layout here. I need to change some wording there because my brand in this region, we want to save this thing." It's becoming really good in following instructions. And I give it context and it produces email templates that are almost there. Right a year ago, they were like, "Great demoware. I can produce an HTML email. Looks great, but it's not really useful." And AI has evolved and it's now almost there. But this last mile is really hard for AI to cover.
And I think this is the place where marketers still need to apply their judgment and taste, but they have more leverage. They can move faster and they can orchestrate agents that do most of the grunt work for them.
And we are building our products to facilitate this idea of you have all these AI agents, you need to understand what they're doing, give them the rules, give them the instructions, give them the guardrails, and see the results, apply judgment, and use your taste as a human being to iterate on that.
And this is kind of how we built AI Decisioning Studio and the way that we're building the interface for our AI assistants to help facilitate the production of stuff while you still have to apply your judgment and taste on top of that. Cai, it all sounds very exciting, but unfortunately, we've run out of time. We can geek out on these things forever, I know. Yeah, absolutely. But we will continue to be looking at Optimum and Optimum AI and the evolution of AI in general.
Thank you very much, Cai Frank, SVP of Product and GM of Americas for Optimum. So that's all for today's episode. Thank you very much, Cai. Thank you very much for having me. Yeah, of course. Thank you very much, Naeem McDonald, for producing this episode and the team here at SVC Summit Americas. I'm Fernando Nott, and to our listeners out there, we'll see you in the next one. Goodbye.