A/B Testing

Problem

Teams often make product changes based on intuition or assumptions rather than data, leading to unclear outcomes and disagreements about what ‘better’ means. Without controlled experiments, it’s impossible to know whether a redesign actually improves conversions or if a performance optimization truly helps users.

Solution

Randomly assign users to different variants and measure how each performs against specific metrics. This gives you data-driven answers about which implementation actually improves conversion rates, engagement, or performance rather than relying on opinions.

Example

This example shows how to implement a simple A/B test by deterministically assigning users to control or variant groups, then tracking their behavior to measure which variant performs better.

// Deterministically assign users to variants based on their ID
function getVariant(userId, experimentId) {
  // Hash the user ID combined with experiment ID to ensure consistent assignment
  const hash = hashCode(userId + experimentId);
  // Use modulo to split users evenly between control and variant groups
  return hash % 2 === 0 ? 'control' : 'variant';
}

// Determine which button text to show based on the assigned variant
const variant = getVariant(user.id, 'checkout-button-test');
const buttonText = variant === 'control' ? 'Buy Now' : 'Add to Cart';

// Track user interactions with the variant information for later analysis
trackEvent('button_click', { variant, experimentId: 'checkout-button-test' });

Benefits

• Replaces subjective debates with quantifiable data about what actually works for users.
• Measures real-world impact on conversion rates, engagement, or performance metrics.
• Reduces risk of shipping changes that harm user experience or business outcomes.
• Enables continuous experimentation and incremental product improvements.
• Provides statistical confidence in decision-making rather than relying on assumptions.

Tradeoffs

• Requires significant traffic volume to reach statistical significance quickly.
• Adds complexity to deployment infrastructure and feature flag management.
• Can slow down decision-making while waiting for enough data to accumulate.
• Demands careful statistical analysis to avoid false positives or misinterpretation.
• Not suitable for testing major architectural changes that can’t be easily toggled.