Some emails win. Now comes the tricky part: figuring out why.
Your autumn sale is over, and it’s time to plan the winter campaign. But first, you need to know: which emails worked best, and why?
The data is there, but it’s scattered across separate reports. One email got more opens, another more clicks. What made the difference? You could spend hours comparing numbers in spreadsheets, only to end up guessing what to try next.
So, how do you connect the dots when the data is scattered everywhere?
Let's unwrap the situation:
The Problem: Your email performance data is scattered across reports, making it hard to see what worked and why. Without the full picture, your next campaign is more guesswork than strategy.
The Agent: @email_performance_comparison
Solution: Compare your emails in one place, uncover what drove the results, and get actionable suggestions for your next campaign. Less guessing, smarter campaigns.
Picture this
It’s Monday morning, and the winter sale launches in three weeks. Maya is sipping her coffee, staring at a mess of email reports from last season.
Three emails, three reports, and no clear answers. Maya is trying to figure out what went wrong last time and how to avoid making the same mistakes with the upcoming winter sale.
The clues are there somewhere, but piecing them together would take hours.
That’s when the Email Performance Comparison agent comes in handy. Maya opens the agent, selects her autumn sale campaigns, and asks: “Which of my emails performed best, and why?”
A few minutes later, she has her answer:
| Subject Line | Sent | Open rate | CTOR | |
| A | "Autumn Sale: 20% off all jackets" | Tue, 9 AM | 18% | 6% |
| B | "Maya, your jacket is waiting (ends tonight)" | Fri, 6 PM | 31% | 7% |
| C | "5 ways to layer for fall" | Thu, 8 AM | 24% | 14% |
Email B got the most opens. Surely that’s the winner? Maya wonders.
“Not quite,” the agent explains. “B’s personal, urgent subject line grabbed attention, but the email lacked a clear call to action and had the highest unsubscribe rate.”
What about C?
“C drove the most clicks. Its useful content and clear next step encouraged readers to engage, even with fewer opens.”
And A? Its generic discount subject line failed to stand out.
Maya now has a plan for the winter campaign: combine B’s attention-grabbing subject line with C’s clear content and call to action. She’ll also A/B test different subject lines to find the approach that gets the most engagement.
Instead of guessing what works, Maya now has clear, data-backed actions to improve click-through rates, reduce unsubscribes, and drive better campaign results.
@email_performance_comparison agent takes the guesswork out of your next campaign. It puts your Optimizely Campaign emails side by side to help you understand what worked, what didn’t, and what made the difference, from subject lines and content to timing and audience. Plus, it gives you actionable suggestions to make your next campaign even better.
The idea behind it: Every campaign is an opportunity to learn. By understanding what worked last time, you can build on those insights, make smarter decisions, and give your next campaign an even better chance of success.
How to try it: Simply type @email_performance_comparison into your Mark Chat and tell it which campaigns to compare, or describe them (e.g. “my September newsletters”). It compares opens, clicks, and click-to-open rates (CTOR), flags metrics that deviate more than 20% from the average, and suggests an A/B test to try next.
of senders measure the ROI of promotional emails, while the rest either don’t measure it or are unsure if it’s being calculated.
of senders who measure the return on investment (ROI) of promotional emails report returns of over $10 for every $1 spent.
of email marketers cite A/B testing as a key strategy for improving email marketing’s already impressive 36:1 return on investment (ROI)