Overview
A/B testing deployment, also called split testing, serves two versions of an application to different groups of users at the same time and compares how each group behaves. The decision rests on measurements. Version B replaces version A only if its metrics beat the baseline on a goal fixed before the test started.
Key concepts
Version A is the control, the release users already know, and it serves as the baseline. Version B is the variant, the same product with the one change under test. A traffic splitter assigns every user to a version by a defined rule, for example a hash of the user ID, so the same person sees the same version on every visit. Without that stickiness the data mixes both experiences and the comparison says nothing.
Implementation steps
- Define the goal and the metric first: conversion rate, engagement or performance.
- Build version B so that it differs from A only in the element under test, on identical infrastructure.
- Split users into comparable groups by random assignment, not by hand-picked segments.
- Route each group with feature flags, a load balancer or an experimentation platform.
- Collect the same metrics from both versions for the planned duration.
- Run the statistical analysis and check whether the difference is significant.
- Ship B, keep A, or run another round with a refined variant.
Advantages
The decision rests on the measured behavior of real users instead of opinions in a meeting. Exposure is limited to a share of the traffic, so a weak variant costs a fraction of what a full release would. Because the test measures a business metric directly, a technical change such as a faster checkout can be priced in conversions.
Challenges
A test needs enough users and enough time to reach statistical significance. Stopping early because the numbers look good is the most common way to crown a false winner. Poor randomization skews the result in the same way. Sticky traffic splitting also needs infrastructure that a plain release does not, and two live versions mean two sets of dashboards and incidents to watch.
Use cases
Typical subjects are layouts and visual elements, the wording of messages and calls to action, pricing and subscription models, and the effect of a new feature on retention. The method also answers purely technical questions, for example which of two implementations of the same search is faster under real load.
Advanced techniques
Multivariate tests compare more than two versions at once, which multiplies the traffic needed to reach significance. Experiments can also run back to back, optimizing one aspect at a time, or be tailored to user traits when the question is who benefits. Feature flags sit behind all of these, because they switch a change on for one segment without a new deploy.
Best practices
Change one variable per test. Otherwise you cannot tell which change moved the metric. Fix the sample size and the duration in advance. Watch real-time metrics, logs and alerts during the run, since a variant can break something the experiment metric does not show. Run long enough to see short-lived effects fade, and record each test with its hypothesis, variables and result, so the next one starts from evidence.