Topic · Conversion and CRO
A/B testing strategy
Most A/B tests fail because the hypothesis was weak.
DefinitionConversion and CRO
What is a/b testing strategy?
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
How Xpand Media runs it
Industries that care about this
Sector relevance
- VerticalB2B SaaSB2B 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.
- VerticalDTC e-commerceDTC 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
Common questions about a/b testing strategy
What sample size is needed for a valid A/B test?
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.
Can we run multiple A/B tests at once?
Yes, on different pages or different visitor segments. Same page same time risks interaction effects that confound results.
What is the difference between A/B and multivariate testing?
A/B compares two variants with one variable difference. Multivariate compares many variable combinations at once, requiring much higher sample sizes to isolate effects.
How long should a test run?
Stopping early causes false positives.
What metrics besides conversion rate matter?
Bounce rate, time on page, scroll depth, downstream pipeline conversion. Optimizing for top-of-funnel conversion alone can hurt revenue.
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