How does Deciding Data work?

I own an ice cream maker. I still buy my ice cream at the store.

Meta's tests teach Meta's algorithm. Ours teach you.

What a significance test can and cannot tell you about an email test, and the numbers that answer the questions a decision needs.

Cheaper, faster tools give us more creative options than ever across email, paid ads, and our sites. Experiments do the reverse: they narrow the field to what actually works.

AI has solved the creative production bottleneck for ad teams. The new constraint is learning velocity, how fast you can turn test results into genuine understanding.

Speed and testing aren't opposing forces. Learn why systematic experimentation actually accelerates your creative cycle, especially during peak seasons.

You don't need to build a separate system to validate insights from experimentation. Your content creation process does it.

Discover how the biological concept of eye saccades inspired a great approach to ad testing, helping us find clear signals in a noisy marketplace and deliver more reliable, and faster results.

Learn how related ad features can weaken your test results and discover strategies to isolate true drivers of performance.

How to build experimentation processes to continuously update your most important decisions.

Cumulative advantage can increase the randomnes in ad performance. We have some ways to see through it, and use it.

Experiments that focus on the ideas you are testing, rather than the platform, can help you learn even faster.

Happily, there is a technique that does a great job finding the best balance between testing new ads and using the best ones we've found.