From metrics to mastery: Tracking what matters in LiveOps

This is the final entry in a three-part blog series adapted from the book Running a Successful Live Service Game: Live Outside of Game Updates by Sergei Vasiuk (2025).

‍In part one, we broke down the foundations of value creation in live games. In part two, we explored how to build player paths through smart segmentation and value-driven operations. This last chapter dives into analytics, the heartbeat of LiveOps, and explains how to use data not just for reporting, but for driving decisions, generating insights, experimenting with intent, and shaping a healthy data culture across your team.

Two questions drive everything in LiveOps analytics: “Why are we doing this?” before launching any initiative, and “Did it work?” once it’s live. Together, they ensure you’re solving the right problems and learning from every step. Data adds a scalable, objective layer to feedback and helps align your creative vision with business outcomes.

But analytics isn’t black and white. Sometimes the most meaningful activities, like community events or branding initiatives, can’t be measured clearly. That doesn’t make them worthless. Instead, these efforts call for a different type of data: experimental analytics and insights.

Build a reporting framework around acquisition, retention, and monetization

At the foundation of any analytics system sits reporting. This is your bird’s-eye view of how the game is performing, and more importantly, why. The structure typically breaks down into three pillars:

1. Acquisition: Are you bringing in the right players? Think marketing campaigns, platform performance, re-targeting, and FTUE (First-Time User Experience). Funnel analysis and attribution help reveal where players come from and where they drop off.
2. Retention: Are players coming back? Key metrics here include session frequency, churn, engagement with events, and winbacks. It’s useful to break this down by lifecycle cohorts (Day 1, 7, 30, etc.) and to identify power users with metrics like active days per month.
3. Monetization: Are you delivering enough value that players want to spend? Reports here cover ARPU, ARPPU, % of payers, transaction volumes, and item-level performance. Over time, dashboards should help teams spot trends, shifts, and optimization opportunities.

While reports are great for high-level monitoring, their true value comes from sparking deeper questions. That’s where insights come in.

Insights: Turning data into actionable ideas

Unlike dashboards, insights tell you something you didn’t already know. They’re hypothesis-driven, surprising, and often game-changing. They might come from surveys, player feedback, market research, or competitor observation, and they all aim to answer one question: “What should we try next?”

Insights are most valuable when they challenge assumptions. For example, if you discover that young, social players are more likely to pay for character skins, that might inspire you to test emotionally framed offers targeted at that segment. It’s not about being right, but about learning fast.

Experiments: Proving what works

Experiments bring rigor to your testing. The gold standard is A/B testing with control and target groups, clear success metrics, and statistical significance. But it’s not always so tidy. Many experiments involve trade-offs: One KPI might go up while another goes down. It’s up to the business to decide what success looks like for them.

And when A/B testing isn’t possible? You can use modeling tools like regression analysis, Facebook’s Prophet, or Google’s Causal Impact to estimate outcomes and measure impact.

Experiments are at the core of a healthy LiveOps flywheel. The more you test, the faster you learn. And the more confident your team becomes in what drives performance, the better.

Predictive analytics: Look ahead, not just back

Beyond reporting and testing, analytics can help you predict the future. Predictive analytics falls into two buckets:

1. Product forecasting: Estimating future audience growth, revenue, and engagement based on trends and seasonality.
2. Player behavior prediction: Using machine learning to anticipate churn, spending, or event participation based on in-game behavior.

While the math is complex, your job in LiveOps is to apply the output. For example, if a model predicts that a player will churn in two weeks, what campaigns or offers can you run to retain them?

Key performance metrics (like precision, recall, and accuracy) help you evaluate the quality of your predictions. But models alone aren’t magic. Without the infrastructure and readiness to act on predictions (via tailored offers, messages, and features), predictive analytics won’t reach its full potential.

Data culture: The real game-changer

Great analytics depends on a great data culture. That means:

• Bias awareness: Acknowledging that data can be twisted to fit narratives.
• Flexibility: Accepting that some valuable ideas aren’t measurable—yet.
• Common lexicon: Aligning the team on what metrics mean and how to interpret them.
• Accountability: Treating analysts as strategic partners, not just support.
• Learning Mindset: Embracing experimentation and tracking outcomes over time.

Data isn’t just about numbers. It’s about the people using them. The best cultures empower analysts and stakeholders to ask smarter questions, work transparently, and use insights to guide bold, creative decisions.

This wraps up our three-part blog series on Running a Successful Live Service Game. If you missed the earlier chapters, start with Part 1 on value creation and Part 2 on creating the player’s path. Thanks for reading - and if you’re ready to turn data into your biggest competitive advantage, now’s the time.

Start your data-driven journey with GameAnalytics

If data is the heartbeat of LiveOps, then GameAnalytics is your pulse monitor, built to help you track, understand, and optimize every beat.

Our analytics tools are designed specifically for game developers, making it easy to dig into acquisition, retention, and monetization metrics without drowning in complexity. From real-time dashboards to segmentation, predictive models, and experimentation workflows, our platform powers decisions at every stage of your game’s lifecycle.

Whether you're tracking player behavior, validating hypotheses through A/B tests, or building out your own insight engine, GameAnalytics gives your team the clarity and control needed to operate with confidence.

Start using data not just to report the past, but to shape the future of your game.