How can you analyze player behavior to improve retention and monetization in online games?

1 Data collection

Analyzing player behavior starts with collecting data from the game. This data can be quantitative or qualitative, and can be sourced from in-game analytics, surveys and feedback, and user testing and observation. For in-game analytics, platforms like Google Analytics, Unity Analytics, or GameAnalytics can be used to integrate analytics into the game and visualize the data. Surveys and feedback can be gathered using tools like SurveyMonkey, Google Forms, or Typeform, as well as in-game feedback systems, forums, or social media. User testing and observation can be done with tools like PlaytestCloud, UserTesting, or TestFlight to conduct user testing with real players, or by using screen recording, eye tracking, or biometric devices to observe player behavior.

4 Data visualization

The fourth step to analyze player behavior is to present and communicate your data analysis results using data visualization. Data visualization can be an effective way to convey your insights and findings, as well as support decision making and action planning. Popular ways to visualize data include charts and graphs, which can show trends, comparisons, distributions, relationships, etc. Platforms like Google Data Studio, Tableau, or Power BI can be used to create line charts, bar charts, pie charts, scatter plots, etc. Heat maps and funnel charts can also be used to show how players interact with game elements and progress through game stages. Hotjar, Crazy Egg, or Mixpanel are platforms that can help create and analyze these visuals. Infographics and dashboards are another way to visualize data by combining multiple types of data into a single presentation. Canva, Piktochart, or Infogram are platforms that can help create infographics and dashboards that highlight key insights and recommendations for game performance, player behavior, and monetization and retention strategies.

5 Data interpretation

Interpreting and evaluating your data visualization results is the fifth step to analyze player behavior. This helps you understand what the data means, and how it relates to your game goals and objectives. Benchmarking and comparison is a way to interpret your data by comparing it to other sources, such as industry standards or competitors. Actionable insights and recommendations can be used to translate data into practical suggestions for improvement or optimization. Storytelling and narration is a way to interpret your data by creating a story that connects the results to your game vision. This can be used to communicate and persuade stakeholders, such as investors or players, and showcase achievements, challenges, and opportunities.

6 Data-driven development

The sixth and final step to analyze player behavior is to apply and integrate your data interpretation results into your game development process, and create a data-driven culture in your game team. Data-driven decisions can optimize game design, features, and monetization and retention strategies based on player behavior data. Experimentation and testing platforms like Firebase, Optimizely, or VWO can be used to conduct A/B testing, multivariate testing, or beta testing. Iteration and improvement can be achieved with agile and lean methodologies such as scrum, kanban, or sprints. Learning and sharing platforms like Slack, Discord, or Trello can be used to collaborate and communicate with the game team and stakeholders. Furthermore, blogs, podcasts, or webinars can be used to share learnings and insights with the game industry and audience.