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Qualitative Research

A/B Testing

A/B Testing

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Definition:

A/B testing is a research method used in UX research and product development to evaluate two variants, typically labelled A and B, by exposing different user groups to each and measuring their responses. The goal is to identify which version drives better outcomes, whether that means higher task completion, stronger comprehension, or greater user satisfaction. In UX research, A/B testing is often combined with qualitative methods to understand not just which variant wins, but why users respond differently to each. This combination of quantitative signal and qualitative depth gives enterprise teams a more complete picture of user behaviour and preference.

How Conveo Does It

Conveo supports the qualitative layer of A/B testing by running AI-moderated video interviews with real participants, not synthetic respondents or AI avatars, to uncover the reasoning behind user preferences between variants. Teams can launch a study in under 30 minutes and receive rich, analysed findings within days, making it practical to run qualitative follow-up research alongside live A/B tests at enterprise scale without sacrificing speed or rigour.

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Frequently asked questions.

A/B testing in UX research is a method of comparing two design or content variants to see which one performs better with real users. Participants are exposed to either version A or version B, and their behaviour or responses are measured. The results help teams make evidence-based decisions about design, copy, or functionality rather than relying on opinion or guesswork.
A/B testing gives enterprise teams a structured, repeatable way to validate decisions before committing to full rollouts. At scale, even small improvements in user experience can have significant business impact. It reduces the risk of costly design mistakes and creates a culture of evidence-based decision making. When paired with qualitative research, it also helps teams understand the motivations behind user behaviour, not just the outcomes.
A/B testing compares two specific variants to determine which performs better on a defined metric, typically using larger sample sizes and quantitative measurement. Usability testing focuses on observing how individuals interact with a product to identify friction points and usability issues. A/B testing tells you which option wins; usability testing tells you why users struggle or succeed. Both methods are valuable and often work best when used together in a research programme.
AI is accelerating both the execution and analysis phases of A/B testing. Automated platforms can now detect winning variants faster and adjust traffic allocation in real time. On the qualitative side, AI-moderated research tools allow teams to quickly gather and synthesise participant explanations for their preferences, reducing the time between running a test and understanding its results. This makes it easier to act on findings while they are still relevant.
Enterprise teams typically use A/B testing to identify which variant performs better at scale, then follow up with qualitative interviews to understand the reasons behind user preferences. This two-stage approach prevents teams from optimising for the wrong outcomes. For example, a variant may win on click-through rate but leave users confused. Qualitative research surfaces those nuances quickly, helping product and insights teams refine their decisions with confidence before wider release.

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