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Experiments Overview

Test configuration changes safely before rolling out to all users.


What are experiments?

Experiments let you A/B test changes on a percentage of your traffic:


Why use experiments?

Direct deploymentExperiments
All-or-nothingTest on % of traffic
Risk to 100% of usersLimited risk
Hard to measure impactClear before/after comparison
Can't undo easilyEnd anytime

What can you test?

Test typeExample
Floor prices$0.75 vs $1.25
Timeouts1200ms vs 1500ms
Ad slots4 vs 5 in-content slots
LayoutsDifferent slot arrangements
PartnersWith/without a partner
Refresh45s vs 60s interval
Lazy loadingOn vs off for specific slots

Experiment workflow

1. Create experiment

Define what you're testing:

  • Control (current configuration)
  • Variation (new configuration)
  • Traffic split (% to each)

2. Run experiment

Let it run for sufficient time:

  • Minimum 7 days
  • Ideally 14 days
  • Enough traffic for significance

3. Analyze results

Compare metrics:

  • Revenue per 1000 sessions
  • CPM
  • Fill rate
  • Page speed (if relevant)

4. Act on results

  • Winner found: Deploy winner to 100%
  • No clear winner: Run longer or try different test
  • Loser clear: End experiment, keep current

Common tasks

TaskLink
Create experimentCreating experiments →
Monitor resultsAnalyzing results →
End experimentEnding experiments →

Quick actions with AI Assistant

You sayWhat happens
"Create an experiment for floor prices"Guided experiment creation
"How is my floor price experiment doing?"Shows current results
"End the timeout experiment"Marks the experiment complete
"Roll out the winning variation"Walks you through copying the winning config into a release to deploy

Best practices

Do

  • Test one variable at a time
  • Run for at least 7 days
  • Wait for statistical significance
  • Document your hypothesis

Don't

  • Test too many things at once
  • End too early
  • Ignore negative results
  • Skip the analysis

Next steps