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Topic · Conversion and CRO
Most A/B tests fail because the hypothesis was weak.
Definition
A/B testing is the practice of running two or more variants of a page or experience in parallel to determine which performs better against a measurable goal. Effective A/B testing depends on a strong hypothesis, sufficient traffic for statistical power, isolated single-variable changes, and a pre-registered success metric.
Services that operate this topic
Service
Full-funnel paid media managed for pipeline and revenue. not impressions. Google, YouTube, LinkedIn, and Meta under one operator.
Service
Custom websites and landing pages built for speed, mobile-first, and conversion. with full tracking and A/B testing baked in.
Industries that care about this
Vertical
B2B SaaS
B2B SaaS in 2026 buys through ChatGPT and Perplexity before it ever sees Google. If your brand isn't cited in AI answers, demos don't book.
Vertical
DTC e-commerce
DTC in 2026 wins on creative volume, not creative quality. AI pipelines that produce 40-80 vertical-video variants per week beat brands that produce 8.
FAQ
Depends on baseline conversion rate and minimum detectable effect. Use a calculator. A typical 5% conversion baseline needs 4,000-8,000 visitors per variant to detect a 20% relative lift at 80% power.
Yes, on different pages or different visitor segments. Same page same time risks interaction effects that confound results.
A/B compares two variants with one variable difference. Multivariate compares many variable combinations at once, requiring much higher sample sizes to isolate effects.
At minimum: one full business cycle (typically a week) plus enough traffic to hit your sample size. Stopping early causes false positives.
Bounce rate, time on page, scroll depth, downstream pipeline conversion. Optimizing for top-of-funnel conversion alone can hurt revenue.
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