Analytics

In game research, analytics refers to collecting and analyzing player data to understand behavior, improve game design, and enhance user experience. Here are some commonly used types of analytics in game user research (GUR):

Descriptive Analytics

These tell you what is happening in the game.

• Session length – How long players play in one sitting.

• Retention rates (Day 1, Day 7, etc.) – Percentage of players who return after their first play.

• Churn rate – When and why players stop playing.

• Level completion rates – How many players finish each level or area.

• Funnel analysis – Where players drop off in a sequence (e.g., tutorial → first quest → first boss).
   

Behavioral/Event Analytics

These track specific player actions.

• Clickstreams or heatmaps – Show where players click or move most often (especially in UI-heavy games).

• Event tracking – Logs events like item pickups, enemy kills, deaths, quest completions.

• Pathing data – Traces movement or decision paths through levels or menus.
   

Performance Analytics

Used to assess how well players are doing.

• Score distributions – Helps balance difficulty.

• Time to completion – Indicates engagement and difficulty.

• Failure points – Shows where players struggle or get stuck.
   

A/B Testing Data

Used to compare different game versions.

• Test groups receive different features, and key metrics (like retention or revenue) are compared.

• Results analyzed via statistical tests (e.g., t-tests, chi-square) to measure significance.

See A/B Testing
 

Monetization Analytics

Tracks in-game purchases and revenue behavior.

• ARPU (Average Revenue Per User) – How much each user spends on average.

• Conversion rate – Percentage of players who make a purchase.

• Lifetime value (LTV) – Estimated total value of a player across their time in-game.
   

Engagement & Sentiment Analytics

Looks at player experience and satisfaction.

• Play frequency – How often users return.

• Session intervals – How much time passes between sessions.

• Survey data (GEQ, PANAS-SF, PENS, etc.) – Paired with analytics to measure emotional/experiential outcomes.

• Text/sentiment analysis – Analyzes chat logs, forums, or reviews for common themes and emotional tone.
   

Predictive Analytics (Advanced)

Uses historical data to predict future behavior.

• Will a user churn soon?

• Which users are most likely to spend?

• Requires machine learning models and large datasets.

Here are some game analytic platforms that allow for free or freemium, as well a subscription uses.
 

• GameAnalytics – Free; great for indie/mobile games; retention, progression, events

• Unity Analytics – Free up to threshold; built-in for Unity games

• Firebase – Free tier; mobile-focused; tracks events, retention, crash reports

• PlaytestCloud – Free trial; UX playtesting with video, audio, heatmaps

• Lookback.io – Free trial; records usability testing sessions

• Split.io – Freemium; A/B testing and feature flagging

• Google Looker Studio – Free; visualize game data from sheets or Firebase

• Observable – Free public notebooks; custom interactive visualizations

• Jupyter (Python) – Free; powerful for data crunching with Pandas, Matplotlib

• MonkeyLearn – Free tools for text and sentiment analysis (chat logs, reviews)