A/B Tests
Compare post variants under a written hypothesis and keep what works.
Stop guessing which version works
Question or statement? Short or long? A hook that names the problem, or one that names the result? Most people have an opinion and no evidence. An A/B test publishes two or more versions of the same idea and compares them, so your next post is informed by what your audience actually did.
An experiment is a hypothesis plus variants
In FinalPost AI, an experiment groups posts from your Publish Queue as labelled variants (A, B, C) under a hypothesis you write down first, such as "a question hook gets more comments than a statement".
- Draft the variants as usual, by agent, by Jarvis or by hand.
- Create the experiment and add the posts as variants.
- Approve and publish them like any other post.
- Give them a few days, then compare how each did on its platform.
- Declare the winner. It is recorded with the experiment.
Over time you build a library of what works for your audience, in your own words.
The honest part
FinalPost AI does not pull like, comment and share counts for each variant into this page. Judging the winner is deliberately manual: each variant has a link to the live post, you compare the engagement there, and you make the call.
That keeps the result honest. A winner is declared by a person who looked at the posts, not by a number that might be measuring something else.
A/B tests do not write posts for you and do not change how anything publishes. They organise posts you have already drafted so you can compare them fairly.
Getting a useful result
- Change one thing between variants. Two differences and you will not know which mattered.
- Publish variants at similar times, to similar audiences.
- Write the hypothesis before you look at results.
The CVR Optimization agent proposes two ready-to-run tests in each report if you want a starting point.
A/B tests are on every paid plan and in the 7-day trial. The walkthrough is in A/B tests.