A/B testing is the method of simultaneously showing two different versions of an in-app experience to split user groups to determine which one better drives a target outcome.

What is A/B testing?

A/B testing is a controlled experiment in which two versions of a product experience (version A and version B) that is shown simultaneously to different user segments to determine which one drives a better outcome. One variable changes between versions; everything else stays the same.

In a SaaS context, A/B testing is how product teams move from gut-feel decisions to evidence-based ones. It is the difference between thinking a new onboarding flow will improve activation and knowing it does.

How A/B testing works

A/B testing follows a straightforward logic. You define a metric you want to move, such as activation rate or tour completion. You create two versions of an in-app experience. You split your user base randomly and show each group one version. After enough traffic has passed through both, you compare the results and adopt whichever version performed better.

The key word is randomly. Without random assignment, you cannot isolate the effect of the change you made from other variables (time of day, user cohort, acquisition channel) that could be skewing results.

What gets tested

Almost any in-app experience can be tested. Common examples in product-led growth contexts include:

• Onboarding flows: two different welcome modal sequences targeting the same user segment

• Tooltips: different copy, placement, or trigger timing for the same feature hint

• Checklists: five-step vs. three-step onboarding checklists and their effect on completion

• In-app messages: different copy or timing on a feature announcement banner

• CTAs: button labels, colours, or position within a product tour step

A/B testing vs. multivariate testing

A/B testing changes one variable at a time. Multivariate testing changes several simultaneously and measures all combinations. A/B testing is slower but gives cleaner causal insight; multivariate testing is faster but requires significantly more traffic to reach statistical significance. For most SaaS teams, A/B testing is the right starting point.

What makes a valid A/B test

Three conditions must hold for an A/B test to be meaningful:

• Statistical significance: the result must be unlikely to have occurred by chance. Most teams use a 95% confidence threshold before acting on a result.

• Sample size: too few users and the result is noise. Run a sample size calculation before you start, not after.

• One variable: if you change the copy and the timing in the same test, you cannot attribute the result to either change specifically.

A test that does not meet these conditions is not a test. It is an opinion with extra steps.

A/B testing in the context of onboarding and adoption

For product teams using a product-led growth model, A/B testing is especially important during the onboarding phase. Small improvements to the first-session experience (a shorter checklist, a better-placed tooltip, a more specific welcome message) can compound across cohorts into meaningful gains in activation and retention.

The most actionable tests are built around activation rate: the moment a user completes the core action that predicts retention. Identifying that action through cohort analysis and then using A/B tests to optimize the path toward it is one of the highest-leverage activities a product team can invest in.

With Jimo's no-code flow builder, teams can launch and iterate A/B tests on in-app experiences without engineering involvement, compressing the cycle from hypothesis to validated result from weeks to days.