The Game Analytics Masterclass

Last month, I flew to Copenhagen, the home of GameAnalytics, to record this masterclass series. Walking into their HQ over a decade after that pitch meeting felt like stepping into the fully realized version of the idea Morten Wulff, GameAnalytics CEO and co-founder, was fighting for back in 2013, when he pitched the company successfully to me at Rovio. A belief that data should be both accessible and transformative, the backbone of every hit game, not an afterthought.

And that’s why analytics matter so much in live-operated games. It’s not about charts or attribution windows or even optimization. It’s about truth. Live games survive or die based on how fast a team can detect reality, interpret it, and act on it. 

The way I see it, analytics is the studio’s nervous system. When it’s healthy, games grow. When it’s not, teams lose time, money, and conviction without knowing why.

This four-part series is the culmination of that idea: how to build, launch, scale, and monetize games by seeing the truth earlier and acting on it faster. 

01 Build: Data Maturity Before Data Sophistication

Most teams imagine their data journey as a staircase: add more tracking, more dashboards, more models, and eventually the truth will reveal itself. In practice, the opposite is true. 

The studios that scale cleanly start with almost ascetic simplicity. They track fewer events, enforce stricter naming conventions, ask fewer questions, and build tighter feedback loops. Their clarity comes from discipline.

The function of data in a modern studio is not to create dashboards; it is to reduce decision latency. The value isn’t in collecting information but in aligning the organization around a shared understanding of reality. Maturity comes from being able to answer the same question consistently across product, UA, and leadership. Sophistication, such as predictive models, automated insights, and AI-powered exploration, only adds value once that shared truth exists.

AI is accelerating this divide. It’s making low-maturity teams even noisier while making high-maturity teams dramatically more effective. Analysts aren’t being replaced; they are being elevated. When the grunt work goes away, the real leverage becomes judgment: the ability to frame hypotheses, translate context, and force the organization to confront the right decisions.

Key Takeaways

1. Maturity creates trust; sophistication amplifies it.

A studio with simple, consistent, well-governed events will generate clearer insights than one drowning in fragmented tracking. Only after maturity is achieved does sophistication produce meaningful returns.

2. Insight quality is proportional to organizational alignment.

If UA, product, and analytics cannot answer the same KPI the same way, no amount of tooling solves the problem. Shared definitions create shared decisions.

3. The value of data is measured in speed, not surface area.

The best studios minimize the time between question → answer → action. Reducing latency increases the number of decisions a team can make correctly before competitors even react.

4. AI elevates analysts from operators to decision architects.

With AI handling queries and pattern detection, the analyst’s value shifts to framing decisions and challenging assumptions. The leverage moves from “knowing SQL” to “knowing what matters.”

02 Acquire: Marketability as a System, Not a Metric

Marketability is often reduced to CPI, which is a dangerous oversimplification. CPI is a snapshot of attention rather than a sign of value, intent, or long-term viability. The best studios treat marketability as a layered system that evolves across prototype, soft launch, and scale. Each stage has its own purpose, its own instrumentation, and its own decision framework.

Concept testing is about raw appeal: does the theme, fantasy, and visual promise resonate? Soft launch is about truth: does the product deliver on that promise, and do users behave in economically viable ways? Scaling is where the complexity emerges: channel elasticity, creative fatigue, regional variance, and cohorted LTV:CAC curves determine whether the game can grow profitably.

What separates elite studios is not their ability to run tests, but their ability to interpret them. 

Teams with deep genre knowledge can identify long-tail value even when early signals look rough. Teams without genre knowledge must borrow patterns from the market: deconstructing top creatives, understanding player motivations, and building upon proven psychological hooks.

Key Takeaways

1. Prototype = appeal testing, not product testing.

Early marketability work measures whether the fantasy grabs attention, not whether the game is good. CTR and IPM tell you which direction to build toward.

2. Soft launch validates retention, stability, and payer intent.

This is where marketability and gameplay intersect. Weak retention or weak payer signals cannot be fixed with better creatives.

3. Scaling requires understanding economic resilience.

LTV curves, CAC curves, channel-specific behavior, and geographic elasticity determine whether a game can scale profitably. CPI loses relevance at this stage.

4. Genre experience creates unfair predictive power.

Teams that have built multiple similar games can forecast long-term value early. Without that experience, deconstructing proven creatives becomes the fastest way to gain pattern recognition.

Make sure to check out: 7 Steps to Build a Winning Creative Strategy with Ad Insights

03 Retain: Expectations Shape Retention More Than Features Do

Retention is commonly approached as a sequential product problem: fix onboarding, add content, smooth difficulty spikes. But most early retention failures stem from a more fundamental mismatch: the fantasy (ad or an IP) that brought players in does not match the experience they receive. When the promise and the reality diverge, even great games bleed players.

Effective retention design begins by aligning acquisition and product. Creatives, app store pages, early gameplay, and first-session pacing must reinforce the same core fantasy. Retention is not merely about keeping players; it’s about delivering the experience they believed they were signing up for. When those expectations align, retention becomes dramatically easier to influence.

Late-game retention operates on different logic. It depends on depth: systems, compulsion loops, content pacing, and community structure. As the game matures, personalization becomes increasingly important. AI-driven sequencing, adaptive difficulty, and tailored content paths allow games to meet players where they are, rather than forcing every player through the same experience.

Key Takeaways

1. Creative alignment is one of the strongest retention levers.

If the ad promises one fantasy and the game delivers another, players churn regardless of game quality. Early-game retention rises sharply when fantasy consistency is maintained.

2. Early-game and late-game retention must be diagnosed separately.

Early churn is about clarity, pacing, and friction. Late churn is about depth, content cadence, and systems design. Treating them interchangeably wastes development cycles.

3. Benchmarks are helpful reference points but terrible decision drivers.

Retention must be evaluated against the expectations of your specific fantasy and genre. A “good” D1 for one game may be insufficient for another.

4. UA-product feedback loops compound long-term success.

Sharing insight between acquisition and product creates a virtuous cycle: better-targeted players meet better-tuned experiences, improving both retention and UA efficiency.

04 Monetize: LTV Emerges From Understanding, Not Extraction

Monetization is often treated as a layer added on top of gameplay. But sustainable monetization emerges from a deeper alignment between player motivations, game structure, and content cadence. 

When a game team understands why players show up, how they progress, and where friction naturally occurs, monetization becomes an extension of engagement rather than an interruption.

The real power of monetization lies in segmentation. Not all players value the same things, move at the same pace, or respond to the same incentives. Effective monetization identifies these differences early and uses them to shape personalized value paths. When players feel understood, they spend more consistently and more willingly.

Every monetization system carries inherent structural risks: offer fatigue, payer over-dependence, economic instability, and flat LTV tails. The best studios track these early signals long before revenue declines appear. They treat monetization like a living system, continually adjusting pacing, reward flows, and content supply to maintain long-term economic health.

Key Takeaways

1. Monetization starts with genre selection, not FTUE design.

Genres define motivations, spending patterns, and UA economics. By the time monetization “layers” are added, the structural constraints are already in place.

2. Segmentation is empathy at scale.

Different players have different needs and spending triggers. Effective monetization aligns with those differences rather than fighting against them.

3. Revenue curves reveal systemic weakness early.

Spiky revenue, slowing LTV tails, and declining ROAS elasticity signal future collapse. These indicators appear months before topline revenue drops.

4. Good games create potential energy; good monetization systems convert it.

Engagement and emotional investment create the conditions for revenue. Smart pacing, meaningful rewards, and well-timed value propositions turn those conditions into predictable growth.

One More Thing…

A huge thank you to GameAnalytics for investing the time, people, and resources to make this masterclass possible. They opened their HQ, brought their teams into the conversations, and treated this project not as content but as a contribution to the entire industry. 

If you found these insights useful, the best way to thank them and to encourage more teams to build masterclasses with DoF is simply to like, share, and pass this series along to someone who will benefit from it. That kind of signal goes a long way, and it helps us bring even more of these deep-dive sessions to you and people who need them most.