How do you use data to make game development decisions?

1 Data collection

The first step to using data is to collect it. You need to decide what kind of data you want to track, how to measure it, and how to store it. For example, you might want to track user behavior, feedback, preferences, retention, engagement, revenue, or performance. You can use various tools and platforms to collect data, such as analytics services, surveys, testing platforms, or user research methods. You should also consider the ethical and legal implications of collecting and handling user data, and respect their privacy and consent.

2 Data analysis

The next step is to analyze the data you collected. You need to identify patterns, trends, correlations, or outliers that can reveal insights about your game. You can use different techniques and methods to analyze data, such as descriptive statistics, inferential statistics, visualization, or machine learning. You should also apply critical thinking and domain knowledge to interpret the data and avoid biases or errors. You should also validate your findings with other sources of data or feedback.

3 Data application

The final step is to apply the data to make decisions. You need to use the data to inform your game development goals, strategies, and actions. For example, you might use the data to improve your game design, user interface, art style, sound effects, difficulty level, or monetization model. You can also use the data to test your hypotheses, experiment with different features, or optimize your game performance. You should also monitor the impact of your decisions and measure the results.

4 Data pitfalls

However, using data is not without challenges and risks. You need to be aware of some common pitfalls and avoid them. For example, you might collect too much or too little data, or irrelevant or inaccurate data. You might also analyze the data incorrectly, or draw false or misleading conclusions. You might also apply the data inappropriately, or ignore other factors or perspectives. You might also lose sight of your game vision, creativity, or innovation.

5 Data best practices

Using data effectively requires following some best practices. Before collecting data, you should define your game objectives and key performance indicators (KPIs). Additionally, it's important to select the right tools and methods for collecting and analyzing data, as well as preprocess the data to guarantee its quality and reliability. To get a better understanding of your findings, you should use multiple sources and types of data to cross-validate and triangulate. Furthermore, you should communicate and visualize your data in a clear and convincing manner. It's also important to balance data with intuition, experience, and feedback, while experimenting and iterating based on the data.

6 Data benefits

Using data in your game development process can bring many benefits, such as understanding your users better and meeting their needs and expectations. It can also help you improve your game quality and user satisfaction, increase retention and engagement, boost revenue and profitability, reduce risks and uncertainties, and enhance innovation and differentiation.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?