The role of sequential testing in A/B testing

Sequential testing helps data and experimentation leaders analyze results in real-time, so they can make quicker, more informed decisions. This approach helps your team spot winning strategies sooner and cut ineffective tests early, which boosts efficiency and impact.

What is sequential testing?

Sequential testing is a dynamic approach to A/B testing that empowers teams to make better data-driven decisions faster.

Unlike traditional A/B testing, which has fixed sample sizes, sequential testing enables teams to continuously analyze data.

The flexibility of sequential testing helps experimentation and data leaders iterate more efficiently, adjust and refine hypotheses on the fly, and get more precise outcomes.

How is sequential testing different from Bayesian models?

Both sequential testing and Bayesian models analyze A/B test data but have their own methodologies and goals.

Sequential testing is all about what’s happening with the data in real-time.

Using sequential testing helps teams make decisions, adjust hypotheses, and tweak strategies without worrying about a fixed sample size.

So, if your team thrives on adapting quickly to unexpected results or emerging trends, sequential testing might be for you.

Now, Bayesian models?

Bayesian models enable teams with large data sets to use existing knowledge and update beliefs with new data.

You have to be careful with the prior in Bayesian models, though – if not built correctly, the entire analysis can go off-course.

How is sequential testing different from Frequentist models?

Sequential testing helps teams analyze data as the experiment runs and enables them to make decisions based on evolving insights.

“Traditional” isn’t a word you’d use to describe sequential testing – but you could use it to describe Frequentist models.

Frequentist models are a lot like traditional hypothesis testing, where you state your hypothesis before the experiment and keep the parameters nice and controlled throughout.

You have to be really organized to use a Frequentist model because of how little flexibility it offers.

Truth be told, the results of a Frequentist model are great and highly reliable. In fact, the results offer the best statistical power!

But for a Frequentist model to work, your team can’t stop or change the experiment the way it could in sequential testing.

You can learn more about the differences between sequential, Bayesian, and Frequentist models here.

How is sequential testing used in A/B testing?

Sequential testing speeds up decision-making A/B testing without sacrificing statistical validity.

Let’s take a look at how it’s applied.

Minimum number of users

One of the reasons sequential testing is such a great option is that teams don’t need a huge number of users to conduct testing. In fact, a couple of dozen users per test will do.

The need for a small number of users makes sequential testing ideal for businesses that struggle to obtain a large sample size or just want to move quickly.

Who can use sequential testing?

The beauty of sequential testing is that businesses of any size can use it.

Small companies can use sequential testing to adjust their approach in real-time, regardless of audience or resources.

But some of the biggest companies can also use sequential testing to move quickly.

In fact, Netflix uses sequential testing for metric tracking, and Booking.com uses it to see if changes enhance customer experience.

Stopping boundaries

In sequential testing, we have what’s called stopping boundaries.

Think of stopping boundaries as predetermined checkpoints. When you hit one, it means you can wrap up the experiment early without messing up the stats.

If you cross the upper boundary, it's a green light – the test variant is better than the control and good to go.

Cross the lower boundary, though, and you can quit early. Crossing the lower boundary means there’s no sign the variant will beat the control.

Each test sets its own boundaries. The boundaries depend on how often you check the data and the error rate you’re okay with.

Estimated Lift vs. Observed Lift

In a sequential test, distinguishing between estimated lift and observed lift is crucial.

Estimated is the projected improvement in a metric. It’s what you think will happen.

As data builds up, that estimated lift shift. You can use it to determine if you should keep going, tweak things, or wrap it up.

Observed lift is the actual improvement you see in the metric once the test is finished. Waiting until the test is done guarantees that decisions are well-founded and not premature.

Why do CRO experts use sequential testing?

Conversion rate optimization (CRO) experts use sequential testing because it makes running A/B tests smooth and boosts results.

When we asked three CRO experts why they use it, here’s what they said.

Controlled interim analysis

Sequential testing gives teams the opportunity to do “controlled peeking.”

In other words, you can keep an eye on things and analyze data as it comes in.

That way, you can be sure you’re making smart decisions without falling for incomplete data or false positives.

Faster decision making

In fast-paced industries, being quick to roll out new features gives you an edge.

And when teams use sequential testing, they can spot winning variations early by monitoring data as it accumulates.

That kind of insight helps teams boost user engagement and seize growth opportunities by deploying the winning variations sooner.

Increased ROI

But to Georgi Georgiev, controlled peeking and faster decision-making aren’t the biggest draw of sequential testing.

Instead, he believes that sequential testing's biggest benefit is the potential for a higher return on investment.

When teams use sequential testing, they know when to pull the plug on unsuccessful tests, which cuts losses. It also avoids ineffective options for users.

But sequential testing also shows teams what will work ahead of schedule. And when they deploy the winning variations, they see immediate revenue gains.

Ultimately, sequential testing is crucial for modern A/B testing. It helps teams adjust quickly to evolving data trends, make faster decisions, and maximize their experimental efficiency.

Ready to find out how sequential testing integrates with Kameleoon’s other powerful statistical tools? Check out this blog post.