Case study · Crypto-first operator
MINT: a shared data foundation from the first launch.
Three product categories. One place to understand the activity.
MINT chose Gamblitude while building its crypto-first operation. Casino, sportsbook and prediction-market style products shared one analytical foundation, with production dashboards running within three weeks of kick-off.
Launch results
The change at a glance.
First production dashboards
3 weeks
From kick-off to dashboards running against live operational data.
Governed Metrics
Day 1
Business definitions available from the operator’s launch.
Routine analytical requests
Up to 90%
Handled without dedicated analyst support during early operation.
Dedicated headcount at launch
0 analysts
The operator launched without a dedicated data-analyst team.
01 · The story
Build the analytical foundation with the operation.
MINT is a crypto-first operator founded by Zak Manhire, bringing casino, sportsbook and prediction-market style products into one Web3-native experience. The business was designed around progression, engagement and a lean team operating across several product categories.
That combination made the data foundation an early decision. Each product generated different patterns of activity, while the team needed a consistent way to read the operation as a whole. MINT selected Gamblitude before a single player placed a bet.
During soft launch and early live testing, before full token integration, MINT already had an analytical environment in place. The team implemented it alongside the platform, before reporting needs could accumulate.
02 · The story
Several products. More context to connect.
Launching several products at once creates more than a larger reporting workload. Casino activity, sportsbook behaviour and prediction-market positions ask different questions of the same data foundation. A useful view has to preserve those distinctions while still giving the team shared definitions.
MINT also needed crypto-specific context. The implementation brought wallet addresses, stablecoin preferences, network choice and settlement timing alongside conventional player and product measures. Those dimensions needed to become part of the analytical model rather than another disconnected export.
- Connect the three product categories without building three parallel reporting environments.
- Retain relevant asset, network and transaction context when interpreting performance.
- Explore an emerging product category without assuming its player behaviour matches casino or sportsbook patterns.
- Give a small team useful access during launch, when engineering and commercial attention are already stretched.
The central decision was to build a usable analytical foundation early enough to support the operation from the outset.
03 · The story
Shared definitions, several ways to investigate.
Gamblitude was deployed in parallel with the platform. The implementation centred on a common source of analytical data, self-service access and AI working with the same business definitions as the visual tools.
The warehouse and semantic layer were configured around MINT’s products and operating context. This included stablecoin turnover, GGR by asset and the distinction between on-chain and platform-side behaviour.
- A dedicated cloud warehouse with a lakehouse architecture for the connected event data.
- Governed iGaming Metrics and Attributes adapted to the crypto-first context.
- Product and category Dashboards, with Master Chart for ad-hoc exploration.
- Segments spanning the three product categories, as well as casino games and providers.
- AI Agent as a conversational route into MINT’s analytical model.
- Insight Radar for recurring investigation of key performance movements.
The first production dashboards were running against operational data within three weeks of kick-off. As the first wave of players arrived, the team already had working views across its product categories.
“We are building MINT to entertain players at the highest level. To do that, we need to understand player behaviour clearly enough to design an environment that is intelligent, measurable and continuously responsive. Gamblitude gave us that layer from the first day. We did not have to wait for our data stack to catch up with our ambition.”
“MINT is a glimpse into where the industry is heading. Three products, crypto rails, a retention-first economy, a team that wants to make decisions in real time. That is exactly the environment we built Gamblitude for. Starting an operator on an AI-native data foundation removes an entire class of problems before they have a chance to form.”
04 · The story
Keep launch investment focused.
MINT chose a managed iGaming data foundation instead of starting a separate internal warehouse and BI programme at launch. Building in-house would have required data leadership, engineering, modelling, BI capacity, infrastructure and licensing.
A comparable in-house team and stack was estimated to involve six-figure annual running costs and a build measured in quarters.
The practical decision was about where to put scarce launch resources. A managed foundation gave MINT access to established iGaming definitions and analytical tools while the team concentrated its attention on the product, brand, content and player acquisition. Predictive-model capability was also part of that analytical environment.
The value of starting from an established analytical foundation was not only the infrastructure itself. It also reduced the amount of domain-specific metric and reporting work the launch team needed to create and maintain from scratch.
05 · The story
Analytical access during early operation.
During soft launch and early live testing, product, growth and risk teams worked directly with the platform without SQL. Dozens of governed Metrics were ready from launch, and questions could be investigated in minutes.
Up to 90% of routine analytical requests were handled without dedicated analyst support. Engineering effort could therefore remain focused on product work, and no dedicated data analysts were required at launch.
- Player Segments were active across casino, sportsbook and prediction-market style products from the first week.
- The team could also segment casino games and providers.
- Behavioural cohorts connected with CRM and retention workflows through webhooks.
- Insight Radar checks were active from the first day.
- Monitoring covered movements in sportsbook margin, casino RTP, payment flows and prediction-market liquidity.
- Operational notifications reached team channels, with reaction time moving from hours to minutes.
Connected cohorts, monitored measures and delivery routes gave the team several ways to follow the activity behind an operational question.
“What struck us early on was how much ground we could cover with a small team. Every function had the same view of the business, the same definitions, the same real-time access. That changed the speed of decisions we made.”
06 · The story
Questions became a starting point.
MINT did not have an established reporting environment to migrate or years of dashboard habits to replace. In the first weeks, people across product, operations and finance used Gamblitude as their main route into business data.
AI Agent became a common starting point. Instead of first finding a suitable dashboard, users could ask a question in natural language and receive an answer with a chart and explanation, then continue the investigation.
Almost the entire operational team was active on the platform. Engagement with AI Agent grew from week to week, and multiple teams began creating their own Dashboards, Segments and Reports.
07 · The story
One foundation across a lean team.
Different functions could work from the same business definitions while investigating the details relevant to them. Product teams examined behaviour across the categories they were building; growth and affiliate teams could work with their analytical context; risk and operational teams could inspect the activity behind a change.
Predictive scoring supported growth and affiliate workflows from the first cohort. Alongside the shared warehouse and analytical tools, this gave those functions another way to examine their audience.
Starting with the data environment in place changed the order of work. The team could use operational evidence as the product developed instead of making reporting reconstruction a separate project after launch.
08 · The story
A new product category raised new questions.
One of the distinctive observations concerned prediction-market style products. In MINT’s early operation, the team identified behaviour that differed from its casino and sportsbook patterns, including deposits and the time horizon of individual positions.
Those differences informed how MINT approached engagement and retention for the product. A shared foundation made it possible to examine them alongside the operator’s other product categories.
“We expected the platform to handle the basics well. What we did not expect was how quickly it would help us see things we did not know to look for. Prediction-market style products and crypto behaviour generate patterns that are still being learned across the industry. Having a foundation that surfaces them natively is a real advantage.”
09 · The story
Make data part of launch planning.
- Build the analytical foundation alongside the operational platform.
- Connect products through shared definitions while preserving their distinctive context.
- Give a lean team visual exploration, reusable Segments and a conversational route to questions.
- Treat a managed foundation as an operating-model decision, with attention to both domain expertise and infrastructure.
- Use the first period of activity to learn about the product rather than reconstruct its reporting.
“Crypto iGaming is where a lot of industry innovation is happening right now. Operators in this space do not have time to spend a year and a six-figure budget building a data stack while their product evolves weekly. Starting with Gamblitude means they compete on product and player experience from day one, with the intelligence layer already in place behind them.”
MINT brings together casino, sportsbook, live streaming, prediction-market style products and Web3-native rewards. Learn more about the operator at
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