A/B testing is a controlled way to roll out a new software version to a subset of users so you can monitor real-world behaviour and limit its use while you gather valuable information about user interaction. Through this technique, you can learn which version of the software is more successful or more liked by users.
Sometimes called an A/B release, it means you ship two versions of your product and route traffic so some people see the old version and some see the new one. Think of it as a staged rollout with built-in measurement, so you can stop or roll back quickly if the new version causes bugs or does unexpected harm. It is a broader experimentation method that compares variants to learn which performs better.
A simple example of an A/B release:
You want to change the checkout flow on an e-commerce site, but you are worried about payment failures. You release the new checkout to 10 per cent of live traffic while 90 per cent keep the old flow. You monitor payment success rates, error logs, and customer support tickets for that 10 per cent, and if problems arise, you halt the rollout and fix the issue before increasing exposure. This is an A/B release because the goal is a safe rollout and operational control, not a pure hypothesis test on conversion rates.
Here are some practical tips when considering using A/B testing. Keep the metrics you watch simple and relevant to safety and user value. Automate rollback triggers for obvious failure modes, and treat the rollout as a learning loop where customer reports and small qualitative notes matter as much as conversion numbers. The two main uses are protecting customers and systems, such as in the e-commerce payment example. And when you need a clear answer to a product hypothesis of what will work best. E.g a button that says 'buy now' and one that says 'special offer'.
Sometimes called an A/B release, it means you ship two versions of your product and route traffic so some people see the old version and some see the new one. Think of it as a staged rollout with built-in measurement, so you can stop or roll back quickly if the new version causes bugs or does unexpected harm. It is a broader experimentation method that compares variants to learn which performs better.
A simple example of an A/B release:
You want to change the checkout flow on an e-commerce site, but you are worried about payment failures. You release the new checkout to 10 per cent of live traffic while 90 per cent keep the old flow. You monitor payment success rates, error logs, and customer support tickets for that 10 per cent, and if problems arise, you halt the rollout and fix the issue before increasing exposure. This is an A/B release because the goal is a safe rollout and operational control, not a pure hypothesis test on conversion rates.
Here are some practical tips when considering using A/B testing. Keep the metrics you watch simple and relevant to safety and user value. Automate rollback triggers for obvious failure modes, and treat the rollout as a learning loop where customer reports and small qualitative notes matter as much as conversion numbers. The two main uses are protecting customers and systems, such as in the e-commerce payment example. And when you need a clear answer to a product hypothesis of what will work best. E.g a button that says 'buy now' and one that says 'special offer'.
