improves margins and gives more control. What we’ve seen is that most in-house attempts struggle to get results. Either they don’t launch, or they don’t scale. Most product roadmaps are already packed. Teams are juggling market launches, regulation updates, or the next brand rollout. Squeezing in something like ML-based personalisation – which needs dedicated focus – rarely works out.


Even hiring a strong ML engineer is just the start. You also need a product team that understands player behaviour across platforms and brands. You need analysts who can challenge assumptions, guide model design, and measure impact. You need engineers to build secure infrastructure, support integrations, and keep everything stable. And all of them need to work together, full-time, toward a single goal.


Te competence you need is tremendous, and the key people need real iGaming experience. Tat kind of setup is rare and expensive to maintain. Our job as a supplier is to keep raising the bar so operators and platforms think twice before building in-house. So, collaborating with an external team that’s focused solely on this technology and this space will deliver better results, faster, and with fewer risks.


Has anything surprised you about where personalisation delivers the most impact?


At first, we expected the strongest results in large, high-traffic markets. More data usually means better performance – more signals to train models, more chances to optimise. But it turned out that smaller markets can benefit just as much, sometimes even more. Tese regions often get


less focus, so when you introduce personalisation, it stands out. Players notice when the experience feels relevant – especially in places where the default approach has been quite generic. One of our clients runs across multiple geographies, including a few smaller test markets. In that case, our Lobby Personalisation showed strong results across the board – with up to 19 per cent growth in turnover and a nine per cent lift in retention. So, scale isn’t the only success factor. Relevance is.


Where should operators start if they want to explore personalisation? What are the first steps?


A good starting point is just understanding what kind of player data you already have and how you’re using it today. Most operators already collect a lot, but it often sits in silos or only gets used for reporting. So the first step is shifting the mindset: from looking back at what happened to asking why it happened and what should happen next.


After that, focus on one area where personalisation can make a visible difference – something like onboarding new players or improving lobby relevance. You don’t need to start with a massive transformation. One targeted use case is enough to test the waters, see impact, and build momentum.


When we start working with an operator, we run a discovery phase first – this includes an analytics overview and building player profiles. Tat alone often uncovers valuable insights and missed opportunities for business growth. And most importantly – AI implementation should always be treated as a step-by-step process, not an all-or-nothing move.


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