Variant
A variant (also called a treatment or challenger) is an alternative version of a page, feature, or experience being tested against the control in an experiment, incorporating the specific changes hypothesized to improve performance.
Also known as: treatment, challenger, test version, variation
Why It Matters
The variant is where your hypothesis comes to life. It represents your best-informed guess about what will improve the user experience or business outcome. The quality of your variant design directly determines the quality of insights your experiment produces. A well-designed variant tests a specific, clear hypothesis. A poorly designed variant changes too many things at once, making it impossible to understand what caused any difference.
Variant design should be driven by data and research, not random guesses. The best variants come from analyzing user behavior data (where do users drop off?), user feedback (what do users complain about?), competitive analysis (what do successful competitors do differently?), and usability research (where do users struggle?). This research-informed approach produces variants with higher win rates.
The number of variants in a test also matters. While it is tempting to test many variants simultaneously, each additional variant increases the traffic needed to reach statistical significance. Most teams get the best results from focused A/B tests (one variant against control) with clearly defined hypotheses.
Industry Applications
An online grocery store tests a variant of their cart page that shows a progress indicator and estimated delivery time. The hypothesis is that reducing uncertainty will reduce cart abandonment. The variant decreases abandonment by 9% and is rolled out to all users.
A SaaS product tests three variants of their trial signup form: a 5-field version (control), a 3-field version, and a 1-field version (email only). The 3-field variant wins with a 22% higher signup rate and no decrease in trial quality, while the 1-field variant had higher signups but lower activation.
How to Track in KISSmetrics
A/B testing tools manage variant delivery and basic metric tracking. Use KISSmetrics to store variant assignments as user properties, enabling deep analysis of how each variant affects the full user journey - not just the immediate test metric but also long-term engagement, retention, and revenue.
Common Mistakes
- -Changing too many elements in a single variant, making it impossible to determine which change drove the result.
- -Creating variants based on personal preference rather than user research and data analysis.
- -Testing too many variants simultaneously without sufficient traffic, preventing any variant from reaching significance.
- -Not documenting the specific hypothesis and changes in each variant, making it difficult to learn from results.
Pro Tips
- +Write a clear hypothesis for every variant: "By changing X, we expect Y to happen because Z." This forces clarity and enables learning from both wins and losses.
- +When testing a radical redesign, also test an incremental change as a second variant to understand whether the full redesign was necessary or if a small change would have achieved the same result.
- +Archive screenshots and descriptions of every variant you test, building a visual library of what has been tried and what worked.
- +Use KISSmetrics to track variant performance on secondary metrics (average order value, retention, support tickets) to catch variants that win on the primary metric but hurt the overall experience.
Related Terms
Control Group
A control group is the subset of users in an experiment who receive the existing or unchanged experience, serving as the baseline against which the performance of test variants is measured.
A/B Testing
A/B testing is a controlled experiment that compares two versions of a web page, email, ad, or feature by randomly splitting traffic between them and measuring which version performs better on a defined success metric.
Statistical Significance
Statistical significance is a measure of confidence that the difference observed between test variants is real and not due to random chance, typically expressed as a percentage (e.g., 95% confidence) or a p-value threshold.
Sample Size
Sample size is the number of users or observations included in each variant of an experiment, determining the statistical power of the test and how confidently you can detect real differences between variants.
Multivariate Testing
Multivariate testing (MVT) is an experimentation method that simultaneously tests multiple combinations of page elements - such as headlines, images, and CTAs - to determine which combination of changes produces the best overall result.
See Variant in action
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