X / TWITTER A/B TESTING
Compare two posts before you publish.
Test whether a practical benefit or a question gives your post a clearer reason to read. Keep the link and topic the same.
Example messages.
A hardware co-op publishes a free blog guide on replacing a toilet flapper, a job the guide estimates at about 15 minutes with a $6 part.
Replace a worn toilet flapper in about 15 minutes with a $6 part. Our step-by-step guide: [link]
Does your toilet keep running after every flush? Here is how to replace the flapper yourself: [link]
Compare two X posts.
Run a clear comparison.
Use the same audience and product facts for both versions. Choose a question before running: clarity, credibility, interest or preference.
Head-to-head mode shows both texts together. Independent mode scores each separately against the same scale, then compares the scores for each profile.
What the results mean.
Modeled preferences cannot predict reposts or how the X feed distributes a post.
The survey supplies observed profiles. AI generates the reactions to your text. The included records are unweighted, and the results are not responses collected from people.
Frequently asked questions
How do I start?
Try a free study or open Studio to choose your audience. Paste both texts, add relevant context and run the study.
What should I do with a close result?
Inspect individual responses and revise the contrast. Small score differences are not a significance test. Use real audience feedback or a live experiment to validate consequential decisions.