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Data-driven game development makes it easier to identify and solve gameplay issues and make updates based on insights gained by analyzing player behavior. However, gathering video game analytics can be resource-intensive. These best practices will improve the efficiency of your video game data analytics processes.
Key Takeaways:
Following these best practices will help you efficiently gather the information you need to analyze game data effectively.
Players generate a lot of data, and you probably don’t need to collect it all. Establishing your goals before collecting data will prevent you from collecting unnecessary data.
Start by asking yourself what you want to accomplish using analytics. Which questions do you need to answer? How can data help you achieve your goals? Based on the answers to these questions, set precise, measurable, and clear goals.
Video game data analytics departments track many key performance indicators (KPIs), but not every company needs to track and measure the same metrics. Commonly tracked KPIs include:
Based on your goals, identify which metrics you need to track and test only the KPIs that are important to you.
There are many ways to measure player data. For your analysis to be meaningful, you must define how to measure the data before tracking it. Factors to consider include the time frame over which you will measure and the units of measurement you will use.
You can analyze some metrics more meaningfully by studying specific cohorts rather than the entire player base. Segment your players into cohorts based on shared behaviors, similar demographics, spending patterns, or whichever shared traits are useful for your analysis.
Once you establish your cohorts, you can use video game data analytics to measure how your changes affect different players. This will help you fine-tune your decision-making. For example, if you aim to attract and retain more high-spending players, you can focus on changes that generate the desired results from that particular cohort.
It is helpful to view analytics as an ongoing process rather than something you do once per month or four times per year. Continuously test different features and measure how they impact your users’ behavior. This will help you keep your game fresh and extend its life.
Because you will be working with so much data, it is important to determine how you will organize it and avoid duplicating efforts. Collect data from the existing databases and other sources you already have before you start collecting new data.
Decide how you will name and store your files. Create a data collection template to ensure consistency and save time. Organize and document as you go.
If you start your video game data analytics with bad data, your analysis will be a waste of time. Scrub your data before attempting to analyze it. Remove duplicate records, errors, and white spaces.
Ensure all the data is in the same format and the columns are in the same order. Enrich the data with additional information as needed.
It is possible to do all of your analytics manually or using many separate tools, but this can be challenging. Consider using an analytics platform that provides automated tools to track player behaviors, actions, events, and other data.
Using an analytics platform will make it easier to compare data across dimensions, allow you to create custom dashboards to review key metrics quickly, and provide real-time performance monitoring. Some platforms provide out-of-the-box insights to help you get started quickly.
A good platform should integrate with your game engine and provide information security features. It should also allow you to create custom events to capture the specific game interactions you want to track.
Automating as much of your video game data analytics as possible will save significant time and effort. Use data pipelines to automate the flow of data from collection to analysis.
Use AI and machine learning tools to discover patterns in data and automate analysis. Integrate software development kits into your game to facilitate real-time data collection. Schedule automated data exports from your databases and use APIs to access data from different sources.
Don’t try to wait for the perfect analysis to make decisions. As useful as analytics is, it will not always provide a clear solution to the problem you are trying to solve. Your analytics should point you in the right direction, but you must test and adjust to achieve the desired result.
Even a well-structured analytics process is not infallible. Sometimes your data will be bad or your analysis will be wrong. If something seems wrong, test it again.
If you get the same result, try using a different tool or analysis method. Make sure you understand how the various tools you use define metrics and how metrics interact.
Whether you are new to game data analytics or are looking for ways to improve your existing processes, Sonamine can help. We provide hands-on campaign management that gets real results by blending machine learning, analytics, live ops, and CRM services into a single offering. Contact us today to get started.
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