Split Testing (or A/B Testing)

Split testing (A/B testing) compares variants of an email to see which performs better on a chosen metric.

Definition & Examples

What is Split Testing?

Split testing, or A/B testing, is a method used to optimize email performance by comparing two or more variations of an email against each other. Elements such as subject lines, call-to-action buttons, images, copy length or sending times can be tested. The test group is divided into segments, each receiving a different version, and metrics like open rate, click-through rate and conversions are measured to identify the most effective variant.

This statistical approach removes guesswork from email marketing decisions by providing concrete data about what resonates best with your audience.

Why it matters

• Data-driven decisions: Replace assumptions with statistical evidence about what works

• Improved performance: Continuous testing can increase open rates by 10-15% and click rates by 20-30%

• Better ROI: Higher-performing emails generate more revenue per send

• Audience insights: Learn what motivates your specific subscribers to engage

• Competitive advantage: Outperform competitors who rely on best practices alone

Core principles of effective A/B testing

Statistical significance

• Test with large enough sample sizes to achieve reliable results

• Aim for at least 95% confidence level before declaring a winner

• Account for seasonal variations and external factors

• Run tests for sufficient duration to capture different user behaviors

Single variable testing

• Change only one element at a time to isolate cause and effect

• Test the most impactful elements first (subject lines, CTAs, timing)

• Document all test variables and results for future reference

• Build a testing roadmap with prioritized experiments

Clear hypothesis formation

• Start with specific, measurable predictions

• Base hypotheses on data, user feedback, or industry insights

• Define success metrics before launching tests

• Consider both primary and secondary effects

What to test in email campaigns

Subject lines

High-impact variables:

• Length (short vs long)

• Personalization (with vs without first name)

• Urgency language ("Limited time" vs neutral)

• Question vs statement format

• Emoji usage and placement

Example test:

• Version A: "Your weekly newsletter is here"

• Version B: "5 tips to boost productivity this week 📈"

Email content and design

Copy variations:

• Short vs long-form content

• Benefits-focused vs feature-focused messaging

• Formal vs casual tone

• First person vs second person language

• Social proof inclusion

Visual elements:

• Button colors and sizes

• Image placement and style

• Layout (single vs multi-column)

• Font choices and sizes

• Color schemes

Call-to-action (CTA) optimization

Button text variations:

• "Buy Now" vs "Shop Today"

• "Learn More" vs "Discover How"

• "Get Started" vs "Try Free"

• Action-oriented vs benefit-oriented

Button design:

• Color (contrasting vs brand colors)

• Size (large vs medium)

• Shape (rounded vs square)

• Placement (top vs bottom)

Sending optimization

Timing tests:

• Day of week (Tuesday vs Thursday)

• Time of day (morning vs afternoon vs evening)

• Frequency (weekly vs bi-weekly)

• Timezone considerations for global audiences

Segmentation approaches:

• Geographic targeting

• Behavioral segmentation

• Demographic splits

• Engagement level grouping

How to set up A/B tests

Planning phase

1. Define objectives: What specific metric do you want to improve?

2. Choose test element: Select one variable to test

3. Create hypothesis: Predict which version will perform better and why

4. Set success metrics: Define primary and secondary KPIs

5. Calculate sample size: Ensure statistical validity

Test setup

Sample size calculation:

• Use statistical calculators to determine minimum audience size

• Typical split: 50/50 for two variants, adjust for multiple variants

• Reserve portion of list for winner rollout (e.g., test 20%, rollout to remaining 80%)

• Account for list growth and churn during test period

Random assignment:

• Ensure truly random distribution to avoid bias

• Use subscriber ID or email hash for consistent assignment

• Avoid testing during unusual periods (holidays, major events)

• Document external factors that might influence results

Execution best practices

Test duration:

• Run tests for at least 24-48 hours for open rate tests

• Extend to 7 days for click and conversion testing

• Consider multiple send times to capture different user behaviors

• Stop tests early only if results reach high statistical significance

Quality control:

• Preview all variants before sending

• Test deliverability across email clients

• Monitor for technical issues during send

• Track unsubscribe rates and spam complaints

Analyzing A/B test results

Statistical analysis

Key metrics to evaluate:

• Open rate: Subject line and sender name effectiveness

• Click-through rate: Content and CTA performance

• Conversion rate: Overall campaign effectiveness

• Unsubscribe rate: Audience satisfaction

• Revenue per email: Business impact

Statistical significance testing:

• Use proper statistical tests (chi-square, t-test)

• Don't declare winners prematurely

• Account for multiple comparisons if testing more than 2 variants

• Consider practical significance alongside statistical significance

Result interpretation

Understanding lift:

• Calculate percentage improvement of winning variant

• Assess whether improvement justifies implementation effort

• Consider confidence intervals, not just point estimates

• Evaluate consistency across different segments

Segment analysis:

• Break down results by subscriber segments

• Look for patterns across demographics or behaviors

• Identify when personalization improves results

• Consider different approaches for different audiences

Advanced A/B testing strategies

Multivariate testing

Test multiple elements simultaneously to understand interactions:

• Subject line + CTA color combinations

• Image + copy variations

• Layout + timing optimizations

• Requires larger sample sizes but provides richer insights

Sequential testing

Build upon previous test results:

• Test winning elements against new challengers

• Gradually optimize multiple campaign elements

• Create testing roadmaps based on impact potential

• Document learnings for future campaigns

Behavioral triggered tests

Test automated campaign variations:

• Welcome series A/B tests

• Cart abandonment email variants

• Re-engagement campaign approaches

• Birthday vs anniversary messaging

Cross-channel testing

Coordinate tests across multiple touchpoints:

• Email + social media consistency

• Landing page alignment with email design

• SMS + email message coordination

• Website personalization sync

Common A/B testing mistakes

Insufficient sample sizes

Problem: Declaring winners with too few data points leads to false conclusions

Solutions:

• Use statistical calculators to determine minimum sample sizes

• Wait for adequate data before making decisions

• Consider confidence intervals, not just point estimates

• Account for segmentation effects on sample size

Testing too many variables

Problem: Testing multiple elements simultaneously makes it impossible to identify what drove results

Solutions:

• Change only one element per test

• Create separate tests for different variables

• Use multivariate testing only with sufficient sample sizes

• Document and prioritize testing hypotheses

Premature optimization

Problem: Stopping tests early or acting on incomplete data

Solutions:

• Set predetermined test duration and stick to it

• Achieve statistical significance before declaring winners

• Consider business context alongside statistical results

• Test during representative time periods

Ignoring external factors

Problem: Not accounting for seasonality, holidays, or market events

Solutions:

• Document external factors during test periods

• Repeat important tests during different time periods

• Consider creating separate baselines for different seasons

• Adjust testing schedules around known disruptions

A/B testing tools and platforms

Native email platform features

Most email service providers offer built-in A/B testing:

• Loops: Integrated split testing with statistical analysis

• Mailchimp: Comprehensive testing options

• Klaviyo: Advanced segmentation and testing

• ConvertKit: Simple A/B testing interface

Advanced analytics tools

• Google Analytics: Track conversions and revenue impact

• Optimizely: Sophisticated experimentation platform

• VWO: Conversion optimization tools

• Adobe Target: Enterprise-level testing

Statistical analysis

• R or Python: Custom statistical analysis

• Excel/Google Sheets: Basic significance testing

• Statistical calculators: Online tools for sample size and significance

• Survey tools: Collect qualitative feedback on test variants

Building a testing culture

Organizational setup

Testing governance:

• Establish testing protocols and approval processes

• Create hypothesis documentation templates

• Set up regular review meetings for test results

• Maintain testing calendar to avoid conflicts

Team training:

• Educate team on statistical concepts

• Provide tools and resources for test setup

• Create testing playbooks and guidelines

• Share results and learnings across organization

Continuous improvement

Documentation practices:

• Maintain testing history and results database

• Document failed tests as well as successes

• Create testing playbooks based on learnings

• Share insights with broader marketing team

Evolution of testing program:

• Regularly review and update testing priorities

• Graduate from basic to advanced testing methods

• Incorporate new tools and technologies

• Expand testing to new channels and touchpoints

Industry benchmarks and expectations

Typical improvement ranges

• Subject line tests: 5-20% improvement in open rates

• CTA tests: 10-30% improvement in click rates

• Design tests: 15-25% improvement in engagement

• Timing tests: 10-40% improvement depending on audience

Testing frequency recommendations

• High-volume senders: Test every major campaign

• Medium-volume senders: Test 1-2 elements monthly

• Low-volume senders: Focus on highest-impact elements quarterly

• Automated campaigns: Test annually or when performance declines

Measuring long-term impact

Portfolio optimization

Track cumulative impact of testing program:

• Overall performance improvements over time

• Revenue attribution to testing initiatives

• Cost savings from improved efficiency

• Subscriber satisfaction and retention improvements

Predictive insights

Use testing data for future planning:

• Seasonal performance patterns

• Audience preference evolution

• Channel effectiveness changes

• Content topic performance trends

Related terms

• Subject line

• Click-through rate

• Email deliverability

• Conversion rate

Key takeaways

• A/B testing removes guesswork from email marketing by providing statistical evidence of what works

• Test one variable at a time with sufficient sample sizes to achieve reliable results

• Focus on high-impact elements like subject lines, CTAs, and sending times for maximum improvement

• Proper statistical analysis and patience are crucial for accurate results

• Build a systematic testing program with documented processes and regular optimization cycles