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A/B Testing

Run experiments to optimize your server. A/B testing lets you show different experiences to different players and measure which performs better.

Test different spawn points, welcome messages, game mechanics, or any other feature. Analyse automatically splits players into groups and tracks conversion metrics so you can make data-driven decisions.

Setup

Step 1: Create an Experiment

  1. Go to A/B Tests in your project dashboard
  2. Click "New Experiment"
  3. Enter a name (e.g., "Welcome Message Test")
  4. Add your variants:
    • Control: "Welcome to the server!"
    • Variant A: "Hey {player}, welcome! Type /help to get started"
  5. Set traffic allocation (e.g., 50/50 split)
  6. Click "Create"

Step 2: Integrate with Your Plugin

Use the Analyse API to assign players to variants:

import net.analyse.api.Analyse;
// Get the player's assigned variant
String variant = Analyse.getVariant(player.getUniqueId(), "welcome-message-test");
if ("control".equals(variant)) {
player.sendMessage("Welcome to the server!");
} else if ("variant-a".equals(variant)) {
player.sendMessage("Hey " + player.getName() + ", welcome! Type /help to get started");
}

Step 3: Track Conversions

Define what "success" means for your experiment:

// When a player completes the desired action
Analyse.trackConversion(
player.getUniqueId(),
player.getName(),
"welcome-message-test",
"completed_tutorial"
);

How It Works

1

Player joins server

2

API assigns player to a variant (deterministic)

3

Your plugin shows the appropriate experience

4

Player either converts or doesn't

5

Analyse tracks results per variant

6

Dashboard shows which variant wins

Players are consistently assigned — the same player always sees the same variant, even across multiple sessions.

Experiment Dashboard

Each experiment shows:

MetricDescription
ParticipantsPlayers in each variant
ConversionsPlayers who completed the goal
Conversion RateConversions ÷ Participants
Statistical SignificanceConfidence the result isn't random
Lift% improvement over control

Best Practices

  • Test one thing at a time — Isolate variables for clear results
  • Run until significant — Wait for statistical significance before deciding
  • Start with 50/50 splits — Even distribution gives fastest results
  • Define clear goals — Know what "success" means before starting