Project Context
Multiple A/B campaigns for Fritz Fryer to optimize subject lines, CTA text, and layouts.
Role
Defined test variables, set up A/B splits in Campaign Monitor, and analyzed results via GA4/UTM.

Key Learnings & Next Steps

• One Variable at a Time: Isolating subject lines vs. layout prevented confounding results.
• Statistical Significance: 20% audience splits hit significance in ~4 hours—next round, test 30% splits for faster wins.
• Documentation: Building a “Subject Line Swipe File” saved 2 hours of brainstorm time per month
• Tools Used: Campaign Monitor · Mailchimp · GA4 · Google Optimize · Figma

What Is A/B Testing in Email?

A/B testing (also known as split testing) in email marketing is the process of comparing two versions of a single variable—such as subject lines, CTA buttons, or layout blocks—to see which performs better.

Why do it? Because gut instinct doesn’t beat data-backed decisions. A/B testing helps you increase open rates, clicks, and conversions, often with very small design or copy tweaks.

What You Can A/B Test

Here’s a breakdown of elements you can test at different levels:

1. Subject Lines

• Length (short vs. long)
• Urgency vs. curiosity
• Use of emojis or personalization

One of my subject line tests led to a 2,800% increase in click-through rate for Fritz Fryer.

2. Preview Text

Often overlooked, but important!

• With vs. without preview
• Emotional vs. feature-based lines

3. CTA Buttons

• Copy: “Shop Now” vs. “Get Yours” vs. “Grab My Discount”
• Placement: Above the fold or after product list
• Style: Ghost button vs. solid block
• Color contrast and padding (especially for mobile)

4. Layout & Design

• Single-column vs. product grid
• Hero image size
• Text on image vs. below image
• Use of whitespace or dividers

On mobile, even small layout changes (e.g., stacking product blocks vertically) can significantly improve tap-through rates.

5. Send Time

• Morning vs. evening
• Weekday vs. weekend
• Time zone segmentation

Combine with GA4 to track post-click behavior for each send time.

6. Personalization

• With or without name
• Dynamic product recommendations
• Location-based content

How I Run A/B Tests

Here’s my typical A/B testing setup in tools like Mailchimp or Campaign Monitor:

Step	Task
1	Decide which variable to test (one at a time!)
2	Set goal: opens, clicks, or conversions
3	Design two email versions
4	Define test audience split (e.g. 20% A / 20% B)
5	Let the rest (60%) receive the winning version after X hours
6	Monitor GA4 or UTM links to track post-click actions

Interpreting Results

Key Metrics to Watch:

Metric	Tells You
Open Rate	Subject line strength
CTR	CTA & layout strength
Click-to-open rate (CTOR)	Engagement quality
Conversion Rate	Overall effectiveness

Statistical significance matters! Don’t declare a winner until you’ve hit enough opens or clicks to validate.

Sample Test Result: “January Sale” Campaign

Variable	Version A	Version B	Winner
CTA Text	“Shop Now”	“Claim My Discount”	B
CTR	0.14%	4.2% (+2,800%)

Bonus: Quick Test Ideas for Beginners

Element	Try This
Subject Line	“Big News Inside” vs. “30% Off: Today Only”
Image	Static vs. animated GIF
Product Order	Bestsellers first vs. new arrivals
Footer	With social icons vs. without

Final Tips

• Always test ONE thing at a time
• Use a large enough sample size
• Be consistent (e.g., test every 2–4 campaigns)
• Track long-term trends, not just wins