Do’s and Don’ts in game analysis

Do’s and Don’ts in game analysis

❌ Test without a clear question
✅ Define hypotheses aligned with goals

❌ Use generic KPIs
✅ Create metrics tailored to your genre

❌ Run complex A/B tests
✅ Isolate a single variable for clean results

❌ Prioritize randomly
✅ Focus on high-impact metrics (ARPU, retention, etc.)

❌ Rely on one revenue stream
✅ Combine IAPs and ads (hybrid monetization)

❌ Treat all players the same
✅ Personalize by segmenting player behavior

❌ Ignore qualitative feedback
✅ Blend metrics with real player opinions

❌ Trust raw data blindly
✅ Validate and audit data regularly

❌ Share results without context
✅ Explain conditions and outcomes clearly

❌ Only study successful games
✅ Analyze failures for valuable lessons

❌ Rely only on data or only on intuition
✅ Balance both with playtesting + insights

❌ Set difficulty once and forget it
✅ Adjust difficulty as players progress

❌ Dump raw data on stakeholders
✅ Use clear visuals and dashboards

❌ Communicate updates randomly
✅ Provide regular, predictable updates

❌ Start tests without clear goals
✅ Define objectives and success criteria

❌ End tests too early
✅ Wait for statistical significance

❌ Change conditions mid-test
✅ Keep tests consistent

❌ Use mismatched test segments
✅ Compare balanced user groups

❌ Overlap test users
✅ Keep user groups clean and separate

❌ Misread early data
✅ Let tests reach maturity before analyzing