Best Practices

Follow these best practices to run successful experiments and build an effective experimentation program.

Experiment Design Best Practices

Create experiments that deliver clear, actionable insights:

Hypothesis Formulation

• Be Specific: Clearly define what you're changing and why

• Ground in Data: Base hypotheses on analytics, user research, or previous tests

• Make it Measurable: Ensure the outcome can be quantified

• Connect to Business Goals: Link to KPIs or strategic objectives

Examples:

✅ Good Hypothesis: "By changing our pricing page headline from 'Choose Your Plan' to 'Start Saving Today' we expect to see a 15% increase in trial signups because it focuses on customer benefits rather than the decision process."

❌ Poor Hypothesis: "A new homepage design will improve performance."

Variant Design

• Test One Variable at a Time: Isolate what you're testing for clear causality

• Create Meaningful Differences: Changes should be substantial enough to potentially impact behavior

• Limit Variants: 2-4 variants is optimal for most tests

• Consider Mobile: Ensure variants work well on all device types

Metric Selection

• Choose Direct Metrics: Select metrics directly affected by your changes

• Include Funnel Steps: Track intermediate steps as secondary metrics

• Monitor for Side Effects: Include metrics that might be negatively impacted

• Consider Time Delays: Account for metrics that may take time to materialize

Duration Planning

• Run to Significance: Let tests run until statistical significance is reached

• Minimum Duration: Run for at least one full business cycle (typically one week)

• Maximum Duration: Avoid running tests longer than 4-6 weeks to prevent external factors

• Seasonal Considerations: Account for weekday/weekend patterns and holidays

Experimentation Program Best Practices

Build a sustainable, effective experimentation program:

Process Development

• Test Prioritization Framework: Use PIE (Potential, Importance, Ease) or similar method

• Experiment Calendar: Plan tests in advance with a dedicated roadmap

• Documentation System: Record all tests, results, and learnings

• Review Cycle: Regularly review past experiments to identify patterns

Sample Prioritization Framework:

Experiment Idea	Potential (1-10)	Importance (1-10)	Ease (1-10)	PIE Score
New Homepage Hero	8	9	6	7.7
Checkout Simplification	7	10	4	7
Pricing Page Layout	6	8	8	7.3

Team Structure

• Cross-functional Input: Include perspectives from marketing, product, design, and engineering

• Clear Roles: Define who owns hypotheses, implementation, analysis, and decisions

• Experimentation Champion: Designate someone to advocate for testing

• Executive Sponsor: Secure leadership buy-in and support

Common Pitfalls to Avoid

• HIPPO Decisions: Avoid overriding data with highest-paid person's opinion

• Moving Goalposts: Define success metrics before running the test

• Data Dredging: Don't search for significance in metrics after the fact

• Premature Stopping: Avoid ending tests too early when seeing desired results

• Confirmation Bias: Don't dismiss results that contradict assumptions

Building a Culture of Experimentation

• Celebrate Learning: Value insights from both winning and losing tests

• Share Results Widely: Make test results accessible to the organization

• Reward Testing: Incentivize hypotheses and experiments, not just "wins"

• Reduce Implementation Cost: Streamline the technical process for creating tests

• Start Small, Scale Up: Begin with simple tests and increase complexity over time

Implementation Best Practices

Technical best practices for clean, reliable experiments:

Code Quality

• Separate Concerns: Keep experiment code isolated from core functionality

• Feature Flags: Use feature flags for easy enabling/disabling

• Minimize Flicker: Prevent control/variant flashing with proper implementation

• Performance Testing: Ensure variants don't negatively impact page speed

QA Process

• Cross-Browser Testing: Verify variants work in all supported browsers

• Device Testing: Check functionality on different device types and sizes

• Traffic Allocation Validation: Verify traffic split matches configuration

• Tracking Verification: Confirm events are firing correctly

Advanced Implementation

• Server-Side Testing: Implement experiments at the server level for performance-critical changes

• Backend Experiments: Test algorithms, pricing models, or infrastructure changes

• Holdout Groups: Maintain unexposed control groups for long-term measurement

• Mutually Exclusive Tests: Prevent users from being in multiple conflicting tests

Scaling Your Experimentation Program

As your program matures, implement these advanced practices:

Experiment Velocity

• Test Volume: Aim to run multiple concurrent experiments

• Quick Implementation: Reduce time from idea to live experiment

• Result Analysis Time: Decrease time to extract insights from results

• Implementation Time: Minimize time to deploy winning variants permanently

Knowledge Management

• Experiment Database: Maintain a searchable repository of all tests

• Insight Library: Document learnings separate from specific tests

• Pattern Recognition: Identify patterns across multiple tests

• Knowledge Sharing: Regular sessions to discuss insights and learnings

Advanced Analysis Techniques

• Segment Discovery: Automatically identify segments where variants perform differently

• Interaction Effects: Understand how concurrent experiments affect each other

• Long-term Impact: Measure the sustained effect of changes over time

• Machine Learning Optimization: Use AI to suggest and optimize experiments

By following these best practices, you'll build an effective, data-driven experimentation program that consistently delivers meaningful improvements to your key metrics and business outcomes.