A/B Testing
What is A/B Testing?
A/B testing splits an audience randomly between two versions of an asset — Variant A and Variant B — and measures which produces a better outcome. In modern autonomous GTM, A/B testing has moved beyond email subject lines: revenue teams test agent prompts, scoring thresholds, sequence branches, landing-page hero copy, and even pricing-page layouts. The discipline is the same — define one variable, hold everything else constant, wait for statistical significance, then ship the winner. The execution is faster: AI agents now generate, deploy, and read out tests in hours rather than weeks, which means a team that A/B tests by reflex can compound small wins into category-leading conversion rates.
Why it matters
- Removes opinion from optimization — the data picks the winner, not the loudest voice in the room.
- Compounds quickly: a 10% lift on every step of the funnel multiplies into a far larger end-to-end gain.
- Builds institutional memory — every shipped test becomes a documented learning the next agent inherits.
Use cases
- Subject-line testing. Two cold-email variants sent to matched audience halves; the higher reply rate wins.
- Sequence-branch testing. Different follow-up cadences for prospects who open vs ignore the first touch.
- Landing-page testing. Two hero variants on the pricing page; the higher demo-request rate wins.
How turgo helps
turgo's revenue agents propose A/B tests automatically — flagging assets with low engagement, drafting variants, running the split, and recommending the winner once significance is reached.
See turgo in action →
