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Video games are one of the few industries where it’s relatively common for a tiny studio to out-compete a major AAA developer.
What people don’t often talk about is what comes next.
Australia has hundreds of small, independent studios creating video games, some of which have achieved remarkable success. But in a market crowded with both massive AAA titles and thousands of indie releases, you’re only as good as your most recent release. When a passion project becomes an overnight hit, the real challenge is following it up with something that proves the success was not a fluke.
For that, you need to understand your audience. Data analytics is a way to turn player data into meaningful insights that can guide future developments. Used well, it helps you focus development time on what actually matters, make a stronger case to investors, and deliver experiences players genuinely want to keep coming back to.
Key player metrics to track and analyse
One of the first problems studios hit with analytics is data overload. Tracking everything leads to noise, confusion, and poor decisions. Not all metrics are created equal. Some drive action. Others are vanity metrics that look impressive but change nothing.
At its core, game analytics is no different from analytics in any other business. You are trying to attract customers (players), keep them engaged, and generate sustainable revenue.
Some of the key metrics worth focusing on include:
- Average Revenue Per User (ARPU)
- Player Lifetime Value (LTV)
- Development costs
- Return on investment (ROI)
- Sphere of influence and advocacy
- Churn rate
- Churn prediction
- Player engagement
- Retention
These form the foundation for disciplined, scalable decision-making. Broadly, they fall into two categories: financial metrics and player behaviour. Your analysis needs to connect the two. Understanding player behaviour and how it impacts revenue will help you make better financial decisions.
Segment and categorise your players to track ARPU and LTV
Treating your player base as a monolith is a missed opportunity. Segmentation is a standard analytical method, where the goal is to group people into self‑similar cohorts or segments based on shared characteristics. Those characteristics might include playstyle, progression patterns, demographics, or purchase history. The goal is to understand how different behaviours translate into business value.
When you segment by playstyle, you can compare casual players with hardcore players, or new players with long-term veterans. You can then see how much revenue each group generates and how their engagement differs over time. To improve ARPU and LTV, you need to identify which cohorts drive growth.
For example, a free-to-play game that depends on microtransactions for revenue will likely have a small cohort of high-spending players. It is easy to view these players as your main drivers of revenue. However, you don’t want to fall into the trap of only considering revenue, or developing gameplay around this cohort alone. You also need to consider how player cohorts interact with one another. You likely have a large cohort of people who spend a lot of time in-game, but don’t spend any money on the game. This is probably your main player base, and you’ll want to prioritise keeping them. They create the world others want to be part of. If they leave, spending follows. No one spends money on a game that feels empty.
Another factor to consider is a player’s sphere of influence. The factors here are going to be very dependent on what type of game you’re making. If it’s a single player game, you’ll want to find people who are more likely to leave steam reviews, recommend the game to friends – or even stream themselves playing. When you have online multiplayer features, you want to identify the gamers who play together. When one person in the group buys a game or dlc, the whole group is more likely to follow, multiplying your revenue. You need to factor in sphere of influence when calculating LTV, because the value goes beyond their individual spend. These are highly valuable players and your data needs to capture that.
Pinpointing the 'why' behind player churn
One of the most immediate benefits of analytics is understanding why players stop playing. Churn, or the rate at which you lose players, is a critical metric for any studio, but is life or death for smaller developers. Every lost player matters. As an indie, you don’t have the luxury of massive audiences or ongoing marketing spend. When players stop playing, there isn’t a huge pool of new users automatically replacing them.
Now, you can and should listen to feedback from your players. However, relying on that feedback alone is a risk. The loudest voices in your community may clamour for a particular feature (or complain about one), but there could be a mismatch between those players and your broader player base. Player data that tracks actual behaviour is a far more precise way to understand and predict engagement and churn.
With churn analysis, you are looking for early warning signals. Some drop-off is expected, such as at the end of a content cycle. But data can also reveal specific friction points. If many players reach the same point and stop playing, something in that experience is pushing them away.
Segmentation is essential here, because you need to consider how any change you make will affect different player bases. You don’t want to fix churn in one cohort only to create it in another. Using experimental branch updates to address core friction points can stabilise your player base and enables you to analyse how specific changes impact different user segments.
Manor Lords, essentially a solo developed project for several years, successfully leveraged player feedback to iterate on complex features. Their experimental approach was vital for managing the game’s intricate systems and unpredictable player interactions.
In contrast, similar complexity has recently crippled Creative Assembly’s Total War series, where a combination of technical debt, game breaking bugs, and growing community dissatisfaction has hindered effective development.
Scenario:
For example, your data might show a churn spike after players encounter a specific boss. If you immediately nerf the boss, that might resolve the issue, or it could cause players who liked the challenge to stop playing. The Dark Souls franchise is a fantastic example of game series that defied the traditional logic that said games with a punishing level of difficulty would drive players away. Instead of a broken retention curve, developers FromSoftware created a game that was so successful it launched an entire genre of games. The level of difficulty (and mystery) creates a powerful community bond. Because the story and world are so cryptic, players are driven to collaborate and share discoveries online. This community led exploration has fuelled a massive ecosystem of YouTube content, where millions of viewers engage with exhaustive gameplay guides, secret hunting walkthroughs, and deep dive lore analysis.
Often, you can still find the answer to balancing difficulty and engagement within your data. You may find that actually, the difficulty is fine but the encounter is too long and repetitive and people get bored. Or you might see that there’s a big skill gap between casual and hardcore players. In that case, a better solution could be offering a lower difficulty setting after several failed attempts. You retain players who would otherwise quit without compromising the experience for others.
Churn should be predicted, segmented and actively managed, not just measured.
Validating features before committing resources
Another great way to use your player data is to prioritise where you focus dev time. Development resources are finite, time is money and not every feature will be worth the cost. Data analytics allows you to test assumptions and pivot quickly.
Instead of committing months to a feature players might ignore, you can release a prototype to a small segment of your audience and measure the impact. Does it increase session length? Does it improve retention? Does it change how players interact with each other?
If the data validates the feature, you can confidently invest in a full rollout. If not, you can cut your losses early and redirect effort elsewhere. Many successful games changed direction mid-development. Grand Theft Auto and Fortnite are two well-known examples of games that changed drastically from the original vision. Those pivots were driven by insights from testing and player behaviour. Smaller studios can reach those insights faster by embracing iterative testing from the start.
Indie studios also have structural advantages that enable you to turn insights into action quickly. You can implement decisions and measure outcomes far faster than AAA developers with larger teams and longer pipelines. You also aren’t weighed down by legacy systems or shareholder pressure to maximise short-term revenue. You can focus on building trust and long-term loyalty. Using data to improve the player experience, rather than squeeze it for revenue, pays off. Players notice when bugs are fixed quickly, when balance changes make sense, and when new features simply make the game more fun. Those moments build communities, not just sales.
Throughout development and ongoing maintenance of newly developed features, it’s also critical to keep opportunity cost in focus, ensuring that time, budget and creative energy are prioritised towards the changes that will deliver the greatest impact. A short-term initiative, such as a live event, seasonal or holiday update, might boost player count and ARPU temporarily, but if underlying performance or core gameplay issues remain unresolved, those gains are unlikely to translate into long-term retention.
Taking the first step with RSM
If you are sitting on player data but not sure how to turn it into better decisions, RSM can help. We will work with you to identify the metrics that matter, build clear dashboards, build models to predict key metrics, and translate data into action. With the right analytics in place, you can reduce risk, focus development effort where it has the biggest impact and build stronger cases for funding and expansion.
If you’re ready to put your player data to work, we would love to talk.
