Beyond the Numbers: How Entain Built a Data-Driven Mobile Gaming Growth Engine
The $50 million analytics paradox
Mobile game studios spend millions on analytics infrastructure. They deploy sophisticated tracking systems, hire data scientists, build comprehensive dashboards that visualize every conceivable user interaction. Yet most still struggle with a fundamental problem: converting data into sustainable revenue growth.
The issue isn't technical capability—it's strategic approach. Teams focus obsessively on data collection while neglecting the framework needed to transform insights into business impact.
Entain, the global entertainment and gaming company, developed a systematic approach that addresses this gap. Rather than collecting data and hoping for insights, they reverse-engineer their analytics strategy from clear business questions. The result is a framework that consistently drives measurable revenue improvements.
Start with questions, not data points
Most teams approach analytics backwards. They instrument everything they can think of, then try to find insights in the noise. Entain flips this entirely.
They use what I call the three-tier question framework:
Your North Star Question This is the one metric senior leadership actually cares about. For their signup form optimization, it was dead simple: "How many users who open the signup form actually complete it?"
Not conversion rate by traffic source. Not time-to-complete metrics. Just the core business question that matters.
The Supporting Cast These questions dig into the why behind your North Star:
Where exactly do non-converting users drop out?
How many users skip identity verification versus completing it immediately?
What's the difference between mobile and desktop conversion patterns?
The Nice-to-Knows These won't drive immediate decisions but might spark future opportunities:
What's the most popular avatar users choose?
Do users prefer certain signup times?
Which form fields take longest to complete?
This hierarchy keeps you focused on revenue-driving insights instead of getting lost in interesting but useless metrics.
Your tracking requests are probably too vague
Here's where most data projects go wrong. The analyst says "track text input in this field" and the developer implements something that technically works but misses half the user behavior.
Entain learned to be surgical about this. Instead of vague requests, they specify exactly what they need:
"Track when a user clicks into the email field, starts typing text, pauses for more than 3 seconds, and unfocuses from the field."
The difference is night and day. Precise tracking requirements mean you capture the complete user journey, not just surface-level events.
They also religiously reuse existing event names. New analytics automatically populate existing dashboards. Historical comparisons stay meaningful. Cross-platform analysis becomes possible.
It sounds boring, but this attention to data consistency prevents analysts from constantly second-guessing their own numbers.
One chart that saved them thousands in lost revenue
Entain was tracking their signup conversion rate and everything looked fine. The overall number was stable, hitting their targets, no obvious red flags.
But they also looked at conversion rate over time. That's where they spotted trouble.
While the overall rate looked healthy, the time-series chart showed a subtle but consistent drop starting from a specific date. Turns out there was a tracking bug in their post-signup login flow that was slowly bleeding conversions.
Because they caught it early through time-series analysis, they fixed it before it significantly impacted their overall numbers. The lesson? Single metrics lie. Trends tell the truth.
Testing isn't just about random splits anymore
Most A/B tests randomly split users and hope for the best. Entain takes a much more strategic approach.
They use feature flags to control exactly who sees new features. Want to test a new monetization flow? Instead of exposing it to random users, they target specific segments who are most likely to provide valuable feedback.
The secret sauce is their data integration. They pull in transactional data from their data warehouse and attribution data from their mobile measurement partners. This creates rich user profiles that let them test features with users who will actually benefit from them.
For example, testing a new payment flow with users who've made purchases before versus completely new users. Or testing retention features with users from specific acquisition channels.
It's testing with intent instead of testing with hope.
How to communicate data without putting people to sleep
Generating insights is only half the battle. The other half is getting people to actually act on them.
Entain's communication approach is refreshingly straightforward:
Kill the jargon Instead of "CTR decreased 15% with p<0.05 statistical significance," they say "450 fewer users are starting games each day."
Give absolute context When they found a 5x spike in account closures, they could have caused panic. Instead: "Account closures increased 5x this week—that's 80 users out of our 3,300 active base."
Know your audience Senior leaders get simple KPI charts with one clear number. Product teams get detailed user journey breakdowns. Developers get technical implementation requirements.
Be honest about limitations They're transparent about what their data shows and what it doesn't. This actually builds more trust than trying to oversell their insights.
The implementation reality check
Here's how this actually works in practice:
Weeks 1-2: Question Strategy Map out your North Star questions for each major user journey. Get stakeholder alignment on what actually matters for revenue.
Weeks 3-6: Data Infrastructure
Implement precise tracking requirements. Set up time-series views for key metrics. Create automated alerts for anomalies.
Weeks 7-10: External Integration Connect your analytics to transactional data, attribution platforms, and user acquisition sources. Build comprehensive user segments.
Weeks 11-12: Communication Systems Develop reporting templates for different audiences. Set up automated insight distribution. Train teams on data interpretation.
Why this approach works
The difference between Entain's approach and typical analytics implementations comes down to intentionality.
Most teams collect data hoping they'll find something useful. Entain defines exactly what they need to know, then builds systems to answer those specific questions.
Most teams test features with whoever happens to be available. Entain strategically targets users who can provide the most valuable feedback.
Most teams bury insights in technical reports. Entain communicates findings in language that drives action.
It's not about having more data. It's about having better questions, cleaner implementation, and clearer communication.
For those interested in watching the original presentation, please refer to the video below.