In a direct A/B test, simple, text-based automation emails outperformed beautifully designed emails with dynamic content. The text version won on both click-through and conversion rates, proving that simplicity and speed often beat complex visual design in automated flows.

Many marketers equate CRO with just A/B testing. However, a successful program is built on two pillars: research (gathering quantitative and qualitative data) and testing (experimentation). Overlooking the research phase leads to uninformed tests and poor results, as it provides the necessary insights for what to test.

When testing copy like titles or subject lines, change only a single modifier word (e.g., add "Quick Fix" to "HR Guide"). This isolates the variable, providing clear learnings about what resonates with your audience, unlike testing two completely different sentences where the "why" is unclear.

In an analysis of 50 past email campaigns, ChatGPT's 5.2 model correctly identified the winning A/B test variation 89% of the time without performance data. Marketers can use this predictive capability to vet campaign elements like subject lines and creative before launching live tests, potentially saving time and resources.

Jay Schwedelson argues against obsessing over statistical significance in A/B tests, as marketing conditions are too fluid. He suggests focusing on directional data instead. If a test provides 'a little more juice' and moves metrics in the right direction, it's a win worth implementing and building upon.

Instead of only testing minor changes on a finished product, like button color, use A/B testing early in the development process. This allows you to validate broad behavioral science principles, such as social proof, for your specific challenge before committing to a full build.

Instead of guessing whether a day-of confirmation email helps or hurts, treat it as a variable to test. Send the email to one cohort of prospects and not to another, then track the show rates for each group. Even a small percentage increase can be significant, providing data-driven validation for your process.

Instead of asking an AI tool for creative ideas, instruct it to predict how 100,000 people would respond to your copy. This shifts the AI from a creative to a statistical mode, leveraging deeper analysis and resulting in marketing assets (like subject lines and CTAs) that perform significantly better in A/B tests.

Despite mature backtesting frameworks, Intercom repeatedly sees promising offline results fail in production. The "messiness of real human interaction" is unpredictable, making at-scale A/B tests essential for validating AI performance improvements, even for changes as small as a tenth of a percentage point.