An A/B test is a live experiment that compares two or more versions of a game element — shown to different groups of players — to determine which version leads to better player behavior or outcomes.
It’s not a gut check. It’s design through data.
1️⃣ Definition
An A/B test is a controlled test of variants, typically run on live games, where user groups experience different versions of a feature, screen, mechanic, or value — and designers use real metrics to decide which performs best.
📍A/B testing is not about proving you're right — it's about learning what works in reality.
2️⃣ Why A/B Testing Matters
Goal | What It Helps With |
Validate design changes | Does this tutorial improve retention? |
Optimize monetization | Which offer converts more? |
Tune progression | Is the new XP curve less grindy? |
Refine events | Which duration drives more participation? |
Improve UX | Which button label gets more clicks? |
📍A/B testing replaces intuition with actionable evidence.
3️⃣ Examples of A/B Tests in Games
Feature | Variant A | Variant B |
FTUE | 10-step tutorial | 4-step fast intro |
Starter Offer | $0.99 for 500 gems | $1.99 with bonus skin |
Reward Table | 10 gems daily | 15 gems every other day |
Ad Placement | After level 1 | After level 3 |
Drop Rates | Normal gacha | Boosted rare drops |
UI Text | “Play Now” | “Start Adventure” |
Event Duration | 3 days | 5 days |
Loop Length | 10-minute sessions | 20-minute sessions |
📍Don’t test ideas. Test outcomes.
4️⃣ The A/B Test Process
- Define a hypothesis
- Create variants
- Group A: Current XP curve
- Group B: Shortened curve
- Split the audience
- Randomly or by cohort
- Ensure groups are equal in size, region, and playstyle
- Run the test
- Let both versions live long enough to stabilize
- Measure KPIs
- Retention, conversion, engagement, LTV
- Analyze and decide
- Which version wins? Keep, iterate, or kill
→ “Reducing grind will improve D1 retention.”
📍No hypothesis = no A/B test. Always ask what you’re trying to prove or disprove.
5️⃣ Common KPIs for A/B Tests
Metric | What It Reveals |
D1 / D7 Retention | Did onboarding improve? |
Conversion Rate | Which offer feels worth it? |
ARPU / ARPPU | Which version yields better spenders? |
Session Length | Are loops too short or too long? |
Feature Adoption | Are players using the new system? |
📍A KPI without interpretation is just a number. Pair it with context.
6️⃣ Best Practices
- Test one variable at a time
- Choose a clear success metric before running
- Ensure large enough sample sizes
- Use control groups to isolate effects
- Account for seasonality or update noise
- Combine quantitative data with qualitative feedback
📍Test big impact, low effort changes first — UI text, timing, pacing curves.
7️⃣ Common Pitfalls
Pitfall | Consequence |
Testing too few users | False positives or misleading results |
Ending test too early | Volatile data = bad decisions |
Testing too many variables | Can't isolate what caused the change |
Ignoring player feedback | Metric win ≠ player trust |
Assuming correlation = causation | Behavior might be influenced by untracked factors |
📍A/B testing is science in a system. Treat it with that level of care.
Summary
Term | A/B Test |
What it is | A controlled experiment comparing game feature variants |
Why it matters | Helps teams make better design, monetization, and UX decisions using real data |
When to use | Any time you need to test a change in offers, onboarding, pacing, or systems |
Design goal | Improve player experience with confidence — not guesswork |
📍A/B testing isn’t just about finding the best version. It’s about building a culture of learning, adapting, and improving — with your players at the center.