Can dynamic content blocks be A/B tested?

Yes, you can A/B test dynamic content blocks. But here's the catch: you're not just testing copy or a hero image. You're testing logic. That changes how you set up the experiment and what you measure.

A standard A/B test swaps one static element for another. When you test a dynamic block, you're asking a bigger question. Does recommendation engine A outperform recommendation engine B? Does showing a personalized block at all beat a static fallback? Does the block placement change behavior?

Those are all testable, but each one needs its own clean experiment. Don't change the algorithm and the placement at the same time. You'll have no idea which variable moved the needle.

Here are the four most common things worth testing in dynamic content:

• Algorithm vs. algorithm. Run "Best Sellers" logic against "Recently Browsed" logic in the same slot for two random audience halves. Track clicks specifically on that block, not just total email clicks.
• Dynamic block vs. static block. Does personalization actually help? Swap the dynamic block for a curated, static alternative in the B variant. You might be surprised what wins.
• Block placement. Top of email vs. below the main CTA. Placement affects whether the personalized content gets seen at all before someone clicks or closes.
• Personalization depth. Tightly targeted content (based on purchase history) vs. broadly relevant content (top category picks) vs. no personalization at all. This one reveals whether your data is actually good enough to drive lift.

What you track matters too. Total click rate on the email isn't enough. You want click rate on the dynamic block specifically, conversion rate from those clicks, and how the results break down by segment. A recommendation engine might work brilliantly for high-purchase subscribers and fall flat for new ones. If you only look at the aggregate, you'll miss that story.

One thing worth keeping in mind: dynamic content tests need enough volume to be meaningful. If your list is small or your send frequency is low, you might not reach statistical significance quickly. Plan your test duration before you start, not after you see early results.

Platforms like Klaviyo, Braze, and Iterable all support dynamic block A/B testing to varying degrees. How much control you get over block-level click tracking depends on your ESP, so it's worth checking what data you can actually pull before you design the test.

And if you want to go deeper, our almanac covers how to measure personalization uplift in detail. Worth reading before you run your first algorithm test.