What kinds of player insights are operators typically missing without a tool like yours? We don’t track live player behaviour, that’s not what we’re built for. Instead, we analyse the UX itself: the components, form fields, buttons, interactions, error states and performance characteristics that define the experience. Analytics might show that conversion dropped, but they rarely explain why. We analyse 12 core customer journeys: registration, navigation, deposit, gameplay, betting and so on, and identify where friction is being introduced.


A good example is market entry. We worked with a client launching in Brazil. Rather than analysing their own site in isolation, we analysed the top ten brands in that market and identified what users expect to see, from registration flows to payment methods. Tat allowed the operator to launch with a UX aligned to local expectations. As changes are made over time, those UX improvements can then be correlated with improvements in behavioural metrics within the operator’s own analytics stack.


How do those insights translate across different internal teams? Tat was a deliberate design choice for us. We have two core products: a UX benchmarking tool and a promotional and proposition benchmarking tool. Product teams tend to focus on the former, while commercial and marketing teams focus more on the latter.


Within the UX tool itself, we analyse four distinct categories. Brand perception and messaging feed into marketing and copy teams. Usability, things like error handling, terminology and recovery paths, are owned by UX professionals. Technical performance and SEO fundamentals sit with engineering teams.


Customer experience can’t be siloed. Players don’t care which department owns which part of the journey. In gaming, outside of brand and spend, operators really have two levers: UX and proposition. Our tools are designed to benchmark both.


What blind spots do you most commonly uncover? Ironically, it’s usually the basics. Tere’s a tendency to chase big, shiny features, complex personalisation engines, new mechanics, when in reality UX is about meeting fundamental user expectations. Digital products share common patterns: button placement, form behaviour, error handling. When operators deviate unnecessarily from those patterns, they introduce friction.


Error handling is a major blind spot. Every user will encounter an error at some point. Most platforms simply show a generic message and expect the player to contact support. In practice, users don’t do that, they leave. Designing effective recovery routes, with clear explanations and alternative actions, can have a disproportionate impact on conversion and retention.


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Do relatively small UX changes actually make a material difference? Tey can, yes. Each analysis produces a prioritised list of recommendations, ranked by severity and impact. Some changes are quick front-end fixes; others require deeper platform changes and longer timelines. Clients on annual subscriptions tend to work through those recommendations over time. We can see which changes have been implemented and how UX scores evolve as a result. While we don’t have access to operators’ commercial data, many report clear improvements once friction points are addressed. It’s very much case- by-case, depending on platform flexibility and internal resources.


If an operator launches something genuinely new, can you still assess its impact? Absolutely. Even novel features need to conform to core UX principles to succeed. Our analysis is based on hundreds of underlying data points, heuristics, functional requirements and usability principles.


Competitive benchmarking adds weighting to those principles based on market norms. If 90 per cent of operators in a given jurisdiction design a particular flow in a certain way, that tells you something about user expectations in that market. We can analyse wireframes, concepts or live implementations and assess how well they’re likely to land.


Looking ahead, is the future of iGaming analytics about prediction, personalisation or automation? Personalisation is talked about far too generically. Tere are many different types, implicit versus explicit, content, UX, product, and they all serve different purposes. Gaming is largely habitual and time-based. Players often know what they want to do before they arrive.


Discovery tends to happen off-platform rather than on it. In some cases, heavy-handed personalisation can actually get in the way.


Te biggest shift right now is automation, driven by advances in AI. AI has existed in gaming for decades, but what’s changed is accessibility. Tese tools are no longer limited to data science teams, they’re becoming usable across organisations.


Are there any broader shifts operators should be paying attention to? One trend I’m watching closely is the move towards proprietary large language models. A year ago, many operators were blocking public LLMs internally due to IP and data security concerns. Now we’re seeing tier-one operators building their own models so they retain ownership of data while still benefiting from AI capabilities.


Tat has implications for recommendation systems, personalisation and internal decision-making. Te challenge is that building proprietary models requires significant investment. Operators that can afford to do it will gain an advantage; those that can’t may struggle to keep pace. Tat gap is likely to widen over the next couple of years.


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