A/B Testing Your Way to Better Email Performance

Author

Koala Lab
March 13, 2025

If you want to optimize your email marketing performance, A/B testing is your best friend. Rather than guessing what works, A/B testing (also known as split testing) allows you to experiment with different variables, gather data, and make informed decisions to improve engagement.

But what should you test? That depends on the problem you're trying to solve.

Let’s break it down with specific A/B testing ideas and real-world e-commerce examples.

📩 Boost open rates

📈 Increase click-to-open

📉 Reduce opt-outs

1. Boosting Open Rates

Your open rate tells you how many recipients actually opened your email. If this number is low, your email isn’t grabbing attention in the inbox. Try A/B testing the following:

✅ Sender Name – People open emails from senders they trust. Test using a personal name versus a brand name.

Example

Version A: Sender Name - Emma from Lush & Glow

Version B: Sender Name - Lush & Glow

✅ Sending Time – Timing can make a big difference. Test morning vs. evening, weekdays vs. weekends, and different time zones to identify when your audience is most active.

Example

Version A: Group A receives email at 7 AM

Version B: Group B receives email at 7 PM

✅ Subject Line – The first thing your audience sees. Try A/B testing different styles:

  • Short vs. long subject lines

  • Questions vs. statements

  • Emojis vs. no emojis

  • Urgency-driven vs. curiosity-driven (e.g., “Last chance! 24-hour sale” vs. “A little something just for you…)

Example

Version A: Subject Line - "Your Skin Will Thank You – 20% Off Inside!"

Version B: Subject Line - "The Secret to Glowing Skin (Limited-Time Offer!)"

2. Increasing Click-to-Open Rate (CTOR)

Click-to-open rate measures how many people clicked on a link after opening your email. If your CTOR is low, your email content may not be compelling enough. Test these elements:

✅ Email Visuals – Does an image-heavy design work better than a minimalist approach? Try A/B testing a product showcase vs. a text-only email.

Example

Version A: Visual - Lifestyle images with customer using product

Version B: Visual - Product image with product on standalone display

✅ Email Copy – Test different tones, lengths, and styles of messaging. Do concise, direct messages perform better than storytelling-style content?

Example

Version A: Email copy is short and concise with bullet points

Version B: Email copy is more detailed and written in essay format

✅ CTA (Call to Action) Copy – Your CTA button is where conversions happen. Test “Shop Now” vs. “Get Your Deal” vs. “Claim Your Reward” to see which drives more action.

Example

Version A: CTA - "Get 20% Off Now!" (Button in bold color)

Version B: CTA - "Your Skin Deserves This – Shop Now" (Text-based link)

✅ Email Layout – The structure of your email matters. Test a single-column layout vs. a multi-column grid. Try placing the CTA button higher vs. lower in the email.

Example

Version A: Email Layout - Single-column layout with one featured product

Version B: Email Layout - Grid-style layout showcasing multiple products

3. Reducing Opt-Out Rates

Unsubscribes are inevitable, but if your opt-out rate is high, something needs fixing. A/B test these strategies:

✅ “Manage Preferences” Option – Instead of an instant unsubscribe, offer an option to adjust email frequency or select preferred content.

Example

Version A: No “Manage Preferences” option in the email footer.

Version B: Include “Manage Preferences” option in the email footer.

✅ Email Frequency – Are you sending too many or too few emails? Test a weekly vs. bi-weekly send schedule to see which retains more subscribers.

Example

Version A: Email Frequency - Group A receives email weekly

Version B: Email Frequency - Group B receives email bi-weekly

Email marketing is never a one-size-fits-all game. What works for one brand may not work for another. A/B testing allows you to make data-driven decisions that continuously optimize your email strategy. Start with one test at a time, analyze the results, and refine your approach accordingly.

⚠️ A/B Testing Disclaimer

When running A/B tests, only test one element at a time to ensure you can accurately identify what’s driving the results. If you change multiple variables simultaneously (e.g., subject line, CTA, and layout in the same test), it becomes difficult to determine which change impacted the outcome.

Additionally, make sure your sample size is statistically significant. Running a test on too small an audience may lead to misleading conclusions.