Also known as: split testing, A/B test
A/B testing (or split testing) is the practice of comparing two versions of something — a subject line, an opening line, an ask — that differ by a single variable, sending each to a comparable group, and measuring which performs better. In outreach it's how you improve reply rates with evidence instead of opinion. The discipline that makes it work is changing only one thing at a time, so you can attribute any difference in results to that change.
Pick one variable to test — for example, two different subject lines or two different calls to action — and keep everything else identical. Split a meaningful sample of your list so each version gets enough sends to produce a trustworthy result; a handful of emails tells you nothing. Then compare the metric that matters for that variable: subject lines are judged on open rate, while the message body is judged on reply rate. Whichever wins becomes your new baseline, and you test the next variable from there.
The two biggest errors are testing several changes at once (so you can't tell what worked) and declaring a winner on too little data (so you're reacting to noise). A subtler one is optimizing the wrong metric — chasing opens when the real goal is replies and meetings. Done right, A/B testing compounds: small, deliberate, well-measured changes accumulate into a message and cadence genuinely tuned to your market.
Outreach is full of strong opinions about what 'works,' most of them untested. A/B testing replaces opinion with evidence, and because the gains compound over time, it's how good campaigns become great ones rather than staying stuck at average.
Test one variable at a time — most commonly the subject line (judged on open rate) or the message body's angle, value point, or call to action (judged on reply rate). Keep everything else identical so any difference is attributable to that one change.
Enough sends per version that the result isn't noise — a handful of emails won't tell you anything reliable. Give each version a meaningful sample before declaring a winner, then make that winner your new baseline.
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