A/B Testing
In one line
A/B testing randomly splits users between two variants and uses statistical comparison to decide which version performs better on a defined metric.
Going deeper
A/B testing randomly splits a user pool into two groups, shows each a different variant and uses statistical inference to decide which version actually wins. The discipline is in 'statistically significant', not 'looks better'.
Common ways A/B tests fail in practice include calling them too early on a small sample, watching too many metrics so random variance gets read as real lift, and letting self-selection break the groups. Writing the experiment design down before launch prevents most of these.
In low-traffic environments — most B2B contexts qualify — A/B tests often can't reach significance in any reasonable timeframe. Qualitative interviews, Bayesian methods or simply moving faster on judgement calls usually beat over-engineered split tests there.
Related terms
CRO
Conversion rate optimisation (CRO) is the structured practice of squeezing more conversions out of the traffic you already have by improving page, UX and messaging.
MarketingCVR
Conversion rate (CVR) is the percentage of visitors or clicks that complete a defined goal — purchase, signup, lead form — and is the headline efficiency metric for most digital campaigns.
MarketingMarketing Funnel
A marketing funnel is the staged model — usually visualised as a narrowing funnel — that tracks prospects from first awareness through to purchase and loyalty.
MarketingAttribution Model
An attribution model is a framework for deciding how credit for a conversion is split across the different touchpoints a customer interacted with on the way there.
MarketingGA4
Google Analytics 4 (GA4) is Google's current analytics platform for web and app, built on an event-based data model rather than the session-centric model that defined Universal Analytics.