Max Tesla, Co-founder and CEO of Blask, leads a company focused on giving iGaming operators a clearer view of the market beyond their own internal dashboards. Blask positions itself as an AI-native iGaming analytics platform that helps businesses turn complex market data into actionable decisions through real-time insights, predictive analytics, and industry-specific intelligence.
In this interview with CasinoRank, Tesla brings a mix of product, growth, and strategic experience. He explains why internal KPIs alone no longer provide a complete picture, where operators still misread the competitive landscape, and why external market intelligence is becoming increasingly important in shaping smarter long-term decisions.
As CEO of Blask, how do you see AI-driven market intelligence shaping long-term strategy for iGaming operators?
Max Tesla: For a long time, iGaming operators have been making strategic decisions based primarily on internal data: player acquisition costs, retention, LTV, campaign performance etc.. Those metrics are of course essential, but they describe only what is happening inside the company. The evolution of AI-driven market intelligence changes this by adding a continuous view of what is happening outside the company, how demand is shifting between brands, how markets evolve, and where new opportunities are forming.
In the long term, this transforms how strategy is built. Instead of reacting to results after they appear in internal dashboards, operators can begin identifying structural changes in player demand earlier. This allows them to prioritize markets, allocate marketing budgets, and adjust positioning based on real market signals rather than assumptions.
In other words, AI does not replace strategic thinking. It provides the missing context that allows strategy to be grounded in reality.
How has external market data, beyond internal KPIs, evolved in shaping marketing and growth strategies for iGaming operators over the past few years?
Max Tesla: Historically, marketing decisions in iGaming were driven mostly by performance marketing metrics. Teams optimized campaigns, acquisition funnels, and affiliate strategies based on internal analytics. What has changed is the growing recognition that internal metrics cannot explain the full picture of market dynamics.
For example, a campaign may underperform not because the marketing execution is weak, but because overall demand for the brand is declining relative to competitors. Without external benchmarks, that difference is almost impossible to identify. This is actually where our internal metrics like Blask Index, Brand Acquisition Power (BAP), Competitive Earning Baseline (CEB) and Acquisition Power Score (APS) come in. They represent the actual interest towards specific brands in the given market.
With the intensifying competition and affiliate market growth, operators increasingly need market-level visibility, understanding share of demand, brand momentum, and how competitors are capturing attention.
External market data therefore becomes not just an analytical layer, but a strategic one. It helps operators understand whether they are losing performance because of internal inefficiencies or because the competitive landscape itself is shifting.
From a leadership perspective, what strategic blind spots do you believe still exist in how iGaming operators understand their competitive landscape?
Max Tesla: One of the biggest blind spots is that many operators still evaluate competition only through direct commercial metrics.. However, competition in iGaming often begins much earlier, at the level of player attention and demand. Brands compete long before a player registers or deposits.
Another blind spot is the assumption that regulated markets operate in isolation. In reality, offshore brands often compete for the same audience, and ignoring them can create a distorted view of market dynamics. Brazil is the perfect example of this situation: according to Blask data, 426 out of 583 brands operating in Brazil function without a local license and yet they compete for the same audience as the “locals”. More broadly, a substantial share of Brazil’s betting market is still estimated to operate outside licensed system.
Finally, many operators still rely heavily on fragmented data sources. This creates a situation where different teams (marketing, product, partnerships) interpret the market using completely different signals. Without a unified framework for measuring market demand and competitive positioning, strategic decisions can easily become inconsistent.
What does tangible, measurable AI-driven value look like in the context of iGaming marketing intelligence?
Max Tesla: Real AI-driven value is not about automation for its own sake. It is about turning complex, fragmented signals into clear benchmarks that decision-makers can act on.
In marketing intelligence, this means translating massive volumes of data such as search behavior, brand visibility, game popularity or market specific trends, into indicators that explain how demand is evolving.
The measurable value appears when teams can answer questions like:
- Which markets are gaining real player demand right now?
- Which competitors are capturing that demand?
- Where does our brand stand relative to the market baseline?
When AI is applied correctly, it reduces uncertainty. Instead of guessing where growth may happen, operators can see early signals and quantify opportunities in advance.
Blask is built specifically for iGaming. Why does that specialization matter in AI-driven marketing intelligence, and what are the risks of using generic data platforms?
Max Tesla: iGaming is an unusually complex industry from a data perspective. Markets are fragmented, regulation varies dramatically across jurisdictions, and many important signals, such as brand demand or game popularity, are not captured in traditional analytics platforms.
Generic data tools are designed to measure digital marketing performance, but they rarely understand the structural specifics of the iGaming ecosystem.
A good example is the United States. While the market is often discussed as a single entity, in reality each state has its own regulatory framework, licensing rules, and competitive environment. Marketing strategies that work in New Jersey may not translate to states like Texas or California, where the regulatory landscape is entirely different or still evolving. Without understanding these structural differences, data can easily be misinterpreted.
Vertical specialization matters because meaningful insights require industry-specific context. For example, understanding how player demand shifts between regulated and offshore brands, or how game-level popularity affects operator performance.
Without that context, generic analytics platforms often produce numbers that appear precise but are strategically misleading. In industries as competitive as iGaming, the cost of acting on misleading signals can be extremely high.
As data volumes grow and competition intensifies, what mindset helps ensure AI-driven insights create clarity and strategic direction, rather than information overload?
Max Tesla: The key is to focus on decision-oriented metrics rather than raw data.
Many companies collect enormous amounts of information, but the real challenge is translating that information into signals that support strategic decisions.
A useful framework is to structure analytics around three core questions:
- Where is demand emerging?
- Who is capturing that demand?
- What realistic share of that opportunity can we achieve?
If AI systems are designed to answer these types of questions, they help simplify complexity rather than amplify it.
Ultimately, the goal of AI-driven intelligence should not be to produce more dashboards. It should be to help leadership teams make fewer, but more informed, strategic decisions.
With a background in digital media and a keen eye for emerging technologies, Ronaldo bridges the gap between players and platforms through clear, insightful reporting to the iGaming industry.
