A method of comparing two versions of a product or feature to determine which performs better with users.
A/B testing is comparing two versions of something to see which performs better. Show Version A to half your users and Version B to the other half, then measure which one wins.
Companies use A/B testing to make data-driven decisions instead of guessing. It is how Google, Facebook, Amazon, and Netflix improve their products.
E-commerce Site Testing Checkout Button
Version A (Control): "Proceed to Checkout" button (blue) Version B (Variant): "Buy Now" button (green)
Results after 1 week:
Winner: Version B increased conversions by 20%! Roll it out.
Headlines and Copy: Which wording resonates more?
Call-to-Action Buttons: Color, text, size, placement
No related topics found.
Page Layout: Different arrangements of content
Pricing: $9.99 vs $10, monthly vs yearly
Images: Which photo drives more engagement?
Forms: Short form vs detailed form
Features: New feature on vs off
Emails: Subject lines, send times, content
Conversion Rate: Percentage who complete desired action
Click-Through Rate (CTR): Percentage who click on element
Bounce Rate: Percentage who leave immediately
Time on Page: How long users engage
Revenue per User: Which version makes more money
Choose metrics that matter to your business goals.
You need enough data to trust results. 100 users is not enough. 10,000 might be.
Statistical Significance: Confidence that results are not due to chance
Most tools calculate this automatically. Wait for 95% confidence before deciding.
Sample Size Matters: Small differences need more data to prove. Large differences need less.
Testing Too Many Things: Change one thing at a time. Test button color OR text, not both.
Stopping Too Early: 100 visitors is not enough. Wait for statistical significance.
Ignoring Segments: Maybe green button works for mobile but not desktop. Dig deeper.
Testing Wrong Metric: Optimizing clicks but ignoring purchases is useless.
Not Having Hypothesis: Random testing wastes time. Have clear reason for each test.
Google Optimize: Free, integrates with Google Analytics
Optimizely: Enterprise A/B testing platform
VWO: Visual editor for creating variants
Split.io: Feature flags with A/B testing
LaunchDarkly: Feature management and testing
Custom Solution: Build your own with feature flags
Simple Client-Side Test:
// Randomly assign user to variant
const variant = Math.random() < 0.5 ? "A" : "B"
if (variant === "A") {
showBlueButton()
} else {
showGreenButton()
}
// Track which variant user saw
analytics.track("button_variant", { variant })
Feature Flags (better approach):
import { useFlag } from "feature-flag-library"
function CheckoutButton() {
const showGreenButton = useFlag("green-button-test")
return (
<button style={{ color: showGreenButton ? "green" : "blue" }}>
{showGreenButton ? "Buy Now" : "Proceed to Checkout"}
</button>
)
}
Test multiple variables simultaneously:
This creates 2×2×2 = 8 combinations. Requires much more traffic than simple A/B test.
Only do multivariate testing if you have massive traffic.
Low Traffic: Need thousands of visitors for meaningful results
Quick Decisions: Testing takes time. Sometimes you need to ship fast.
Obvious Improvements: Fixing broken checkout does not need testing.
Brand Changes: Logo redesigns should not be A/B tested.
Ethical Issues: Do not test things that could harm users.
Focus on high-impact tests:
Do not waste time testing footer link color.
Test separately for different devices. What works on desktop might not work on mobile.
Example: Long form works on desktop, short form wins on mobile.
Segment results by device type.
Most A/B tests show no significant difference or fail. That is okay!
Failed tests teach you:
Netflix runs hundreds of tests. Most fail. The winners make it worth it.
Companies like Amazon test everything continuously. It is part of their culture.
Benefits:
Start small: Test one thing this month. Build habit of data-driven decisions.
A/B testing removes guesswork from product decisions. Test changes before rolling them out to everyone. Measure real user behavior instead of assuming.
Start with high-impact areas like checkout, pricing, or landing pages. Use proper tools. Wait for statistical significance. Learn from both wins and losses.
Data-driven development is how modern products improve continuously. A/B testing is the foundation.