Stop guessing what works. A/B test every WhatsApp campaign.

A/B testing has been standard practice in email marketing for 15 years, but it's still rare in WhatsApp. Part of the reason is that most WhatsApp platforms don't support it natively — you have to manually create two campaigns, manually split the audience, manually compute the results in a spreadsheet. That's enough friction to discourage teams from testing systematically, so they ship one variant per campaign and hope for the best.

The cost of not testing is hard to see because you never see the variant you didn't ship. But the data is consistent: when teams start A/B testing WhatsApp campaigns, they typically find that one in three variants beats the original by 20%+ on conversion. Compounded across 50+ campaigns a year, the brand that tests every campaign ends up 2-3x ahead of the brand that ships one variant per campaign — without spending any more on infrastructure or audience.

Edesy's A/B testing is designed to remove the operational tax. You set up a campaign, click 'Add variant', change one thing, hit launch. Audience splits automatically; results show in real time; Bayesian winner detection tells you when you have a statistical decision; auto-promote mode shifts the rest of the audience to the winner without manual intervention. The whole loop is 5 minutes of work per campaign, and pays for itself many times over.

The Bayesian approach matters more than it sounds. Traditional frequentist A/B testing requires pre-committing to a sample size and waiting for 'statistical significance' (usually p<0.05). That's fine for huge web traffic but brittle for the smaller audience sizes typical of WhatsApp campaigns (often 5K-50K). Bayesian testing reports 'probability variant A is best' continuously — at 5K sends you might already have 92% confidence in a winner; at 20K you might have 99%. You can stop testing early when you're confident, or keep going when you're not. Much better fit for how WhatsApp campaigns actually work.