Jun 29, 2026

Build Faster, Earn More: How to Get Started with In-Game Analytics

Part of the Build Faster, Earn More series, this guide offers a deep dive on in-game analytics building on what we covered in our recent Revenue and Analytics article.

In-game analytics are a crucial part of the success of any live game, empowering your decision making and validating your design. In this article, we'll tackle how to get started if you're completely new to in-game analytics, along with tactics to refine your existing analytics approach.

Unlike platform analytics that can be found in the Developer Dashboard⁠ which show high-level metrics like total users, revenue, and engagement; in-game analytics address core functionality implemented in your game. This allows you to understand how your players are interacting with your features. In-game analytics are not a feature to consider building in your development life cycle - they are an integral part of your design. These metrics are the clearest feedback that indicates your design is resonating with your players, helping you diagnose any problems and make targeted fixes.

In this article, we will dive into the following topics:

Starting with questions, not tools: How you can get started implementing and leveraging in-game analytics, and how to ask the right questions

The analytics pipeline: How to set up your data pipeline, what tools to choose, and dashboard best practices

It's never too early or late to start: Getting started no matter where you are in development

Each topic will include real case studies from VR studios whose in-game analytics meaningfully improved their games' retention and engagement.

Starting with questions, not tools

Getting started with a new tool set, even one as tried and tested as telemetry, can be intimidating. Many new or small teams don't know what information can be gleaned from data or what tools to use to get it. The task can appear daunting if you attempt to implement all possible data hooks at once. It helps to begin by formulating a question about your game that you can feasibly find the answer to. Having a question and using data to answer it is the fundamental step in the process of analytics. This question/answer step is the starting point even for developers at large studios with entire teams devoted to implementing and analyzing data.

The goal of answering your first question should be to help validate your design, not to try to change a KPI. We recommend most developers start with their core loop or their tutorial. Think about how your players initially experience the game and ask "are players doing what I think they will?" Let this question guide your first data event implementation.

Your first questions should be simple ones that can be easily answered. Instead of something large in scope like "what is leading to my low Day 1 (D1) retention?" try something targeted at a singular quest or system like "how long does a match take?" "what is the win rate for the first level?" "do my players drop off on any step in my tutorial?"

Once you've asked a question, and implemented events aimed at answering it, you can validate that player behavior is matching your design intent. For example you may ask if players are getting stuck in your tutorial. By adding an event for each step of your First Time User Experience (FTUE) you can plot completion rates and easily visualize where your users are dropping off. By targeting this problematic step you can start to transform insights into business improvements.

The analytics process in three steps: ask where players get stuck, instrument start/end events for each tutorial step, and plot the drop-off.

Case Study: VAIL

AEXLAB, the team behind VAIL⁠, asked this very question about their FTUE and answered it through in-game analytics. The team had recently implemented a new tutorial and wanted to know how players were navigating it. By implementing an event at each step and counting completions, they were able to identify a singular step responsible for a 6% drop-off in new users. These were players interested in the game, but who struggled navigating the tutorial. Not transitioning through the FTUE meant potential players and business was lost.

AEXLAB instrumented each FTUE step and found a single step responsible for a 6% drop in new users.

After identifying that drop-off between high-level FTUE steps, the team did not stop investigating. They implemented a set of new fine-grained events to better understand the issue. With their new data they were able to pinpoint the exact troubling step, which armed them with precise information about what was difficult or under-communicated.

After finding the problematic step, AEXLAB broke it down into sub-actions with finer-grained events pinpointing the exact mechanic that was under-communicated.

Data won't always be this clear, but even when it doesn't immediately answer the core question, it should inform new events that could give you deeper granularity or highlight areas worth exploring with playtesting. Data is great for answering "what" is happening but not "why". Oftentimes it's worthwhile to implement new events, then kick off a playtest. You can ask your testers directly what is behind any discrepancies you may find.

The analytics pipeline

Once you have your questions it's time to start answering them. You will now need to set up an analytics pipeline capable of answering your questions. Your pipeline will exist in three discrete stages.

Your pipeline in three stages: generate telemetry events from player actions, organize validated data in queryable formats, then model it for insight.

Step 1: Generating Data

Generating data from your game requires the implementation of telemetry events. These are blocks of code that trigger when a user completes a specified action. These events are sent, along with accompanying metadata, to a separate organized storage. Think of these events as the building blocks of your telemetry system. They should be descriptive of a specific moment or action you want to measure without being overly prescriptive, at least to start.

A starter event taxonomy: common player actions mapped to consistent event names that work across genres.

Standardization vs. Customization

Many events are consistent across different genres. Events like ftue, reward, quest, and iap should be present in most games (see table above for triggers). Some slightly more specific events like match_start and match_end require that the game be separated into discrete, focused periods of action. A shooter may be structured around competitive matches, while a puzzle game is structured around solving a specific level. In both cases this could be referred to as a "match".

Some events will be very game specific. In the shooter game it would be common to have an event like weapon_upgrade if you've implemented an RPG system, while a puzzle game might have something like booster_use. Neither event would make sense in the context of the other game. Keeping events consistent across genres makes it easier to compare performance across multiple titles or benchmark against other studios at your publisher. Even with a single game, consistent naming sets you up well for future growth. This can also go a long way to shortening the time it takes to implement events if you have a standardized list.

The real way most games personalize their learnings is through their metadata. Metadata is a powerful force multiplier on top of your event system. Metadata is how you add context to your events, fitting them to your game and using filters to segment your players into smaller, more targeted behavioral groups. As an example with the match_end event discussed above, in a shooter, match_end would include some general metadata such as duration, win/loss, reward. It would also include metadata such as total kills, total loadout_value, defeat conditions (player, bot, level, etc). This thorough tracking of metadata allows you to answer the largest number of questions with the fewest events sent.

The match_end event stays portable across genres. Metadata is how you make events specific to your mechanics without fragmenting your event schema.

Progressive Implementation

Even experienced teams can easily overwhelm themselves with data implementation. In an effort to know absolutely every preference, desire, and issue your players face, you may find yourself implementing too many events. This will paradoxically make it harder to tease out important information later, and balloon your data storage costs.

Therefore, it is recommended to start small. Pick a few questions, based around your core loop, and implement as few events as possible to capture player behavior. Leverage metadata to deeply inform your limited number of events.

This progressive approach should extend through event implementation all the way to your dashboard and insights generation:

Implement events as you have questions about specific features.

Add metadata as you find edge cases the events cannot answer.

Build views for your data as you have need of them rather than architecting a large, rarely used dashboard.

Case Study: Contractors

Caveman Games, the team behind Contractors⁠ had a simple question: does win rate impact retention? They could answer this question easily by either implementing match_win, or the more versatile match_end with metadata win and loss. Through data visualization they tracked the number of wins in the first session compared to the likelihood of returning the following day. Their results showed that as the number of wins increased on a player's first day, so too did their D1 Retention.

Caveman Games tracked wins on Day 0 against Day 1 return rates, revealing that more first-day wins meant higher odds of coming back.

This led them to their next question: how many players are in each win bucket? Are players on their first day more likely to have 0 wins or 3+? The answer to these questions revealed an alarming decay curve:

The follow-up revealed a steeper problem: most new players weren't completing enough matches to accumulate the wins that drive retention.

Only 50% of new players played two matches, and only 33% played three. While three or more wins on the first day correlated to exceptionally high D1 retention, barely a third of players were even playing that many matches—let alone winning. This led to the next question: what was the win rate on the first day?

Low, flat win rates across early matches told the team the problem wasn't player skill. Something in the system was deterring new players.

The team found that only 15% of players successfully extracted (the win condition) from their first match, despite the onboarding experience being designed to be easier for new players. More troubling, the win rate did not increase for subsequent matches. So far, the team had answered their initial question and detected an underlying problem with their onboarding flow with a simple set of telemetry events. From this analysis, more information would be needed to diagnose the cause of the low win rates.

In their further investigation, continuing to leverage metadata to its fullest, they dug into death rates, finding that 17% of deaths were coming from bots. Bots were not intended to be lethal to new players. Tuning bot accuracy dropped the rate of players losing to bots by 80%.

Investigation into the map revealed that most new players were not starting in the intended new user map, leading to higher rates of failure at the hands of more skilled players. Changes made correctly sorted new players to the intended FTUE map. Between all their targeted changes, the team was able to increase their extraction rate for new players 4×, improving their D1 retention rate by five percentage points.

After tuning bot accuracy and fixing map routing, new-player extraction rates improved 4× and D1 retention rose five percentage points.

It's possible the team could have detected this problem through playtesting. However, by asking a simple question and consistently implementing events and metadata, Caveman Games was able to follow a trail of clues to turn insights into action. This resulted in a much more fun experience for new players.

Step 2: Tools to send and store data

The tools you choose to shape, send, store, and eventually transform your data will have a strong impact on what each of these stages looks like. Generally speaking for each step, tools range from basic and limited to powerful and complex, with generally a trade off seen between cost and customization.

Your generation, warehousing, and insights layers each have tools that scale with you. Start accessible, then upgrade as your questions get more complex.

For smaller or budget conscious teams, it should be noted there are accessible options that have large support communities. Many cloud-based analytics platforms offer free or low-cost tiers that include event ingestion and data warehousing - allowing you to get started without a large upfront investment. Some developers have found success pairing these tools with AI assistants for data exploration, creating a flexible starting point for their analytics journey. Note that Meta does not endorse any specific third-party analytics vendor or AI tool, and all developers should evaluate tools based on their own needs, budget and compliance requirements.

When considering incorporating AI into your analytics workflow, there are some specific things to keep top of mind. First, respect player privacy and applicable laws. Second, verify all AI outputs. AI tools can produce inaccurate results, so always require citations to underlying data and validate all AI output against known figures.

For teams that are ready to scale, there are many more advanced options. Business Intelligence (BI) tools like Omni will give you a lot of control, the ability to quickly filter your views, and fast iteration on new data slices. Many platforms will give discounted starter rates or free trials so you can experiment for yourself to see which best meets your needs in terms of structure, flexibility, and cost so you can explore your data without sacrificing clarity or consistency.

Step 3: Dashboards to Drive Decisions

The last stage of setting up your analytics pipeline is a dashboard. As mentioned in the previous section, this may not be right for every team, but dashboards lift analytics from being one person's passion project to a resource the entire team can share and action on. Different teams will have different needs so always start with a sense of the audience and the purpose of the dashboard. For example, the needs of a small team who mainly need to provide standardized views to an investor will be different from a larger team, who are interested in doing deep dives into player behavior.

Consider what actions you want someone to be able to take when looking at your dashboard. Is it important that you are able to compare this year's data to last year's data? Ensure that at the top level of your dashboard you can filter by a specific date or date range. Does someone need to be able to follow a complex chain of player behaviors to find churn points? Prioritize building out a Sankey⁠ view of events.

When you need to follow a complex chain of player behaviors to find churn points, a Sankey view makes the full picture legible at a glance.

Remember consistency saves time when building your views. For example, if you frequently splice your data by the player's country, ensure that country has the same color and is in the same place within the chart across all views to reduce the likelihood of someone misreading the data.

Lastly, keep types of charts in mind when you are designing your static views. Trends are best explored via line charts where changes over time are easily observed. Comparisons are easiest to view through bar charts, and a stacked bar or pie chart is best for composition.

Match your chart type to the question: bar charts for comparisons, line charts for trends over time, stacked bars or pie charts for composition.

It's never too early or late to start

While the best time to implement analytics is before you need to answer a question, if you plan to support your game post-launch in any way, the second best time to add analytics is now. There are a wealth of tools available to help you get started with in-game analytics.

If you have nothing implemented yet, start with what you want to know. Come up with 3–5 questions you have about how your players interact with your game, then follow the flow in this article to find your answers. Ultimately, we hope this helps you to build the best possible version of your game for your players.

For the latest updates, follow us on X⁠, Facebook⁠, and LinkedIn⁠, subscribe to our monthly VR developer newsletter, and join the conversation in the Meta Developer Forum⁠.

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