Experiment faster. Decide sooner.

Experiment faster.
Decide sooner.

Make good decisions up to 80% faster with Group Sequential Testing (GST).

GST is the advanced math that powers our platform, developed by Georgi Georgiev (ABsmartly advisor and author of the book “Statistical Methods in Online A/B Testing"). We built our super-fast stats engine to save you time.

What’s GST (and why should I care)?

Group Sequential Testing lets you safely check your data during a test, so you can ship “winners” and stop “losers” the moment they're statistically valid. And it works exactly how it sounds:

“Group” means that instead of analyzing every single user one by one, you analyze data in “groups” or batches (e.g., every week or every 1,000 visitors).
“Sequential” means that you make decisions in a sequence. You set checkpoints before you start your test when you'll look at the data and ask: “Do we have enough evidence to stop now?”

This approach lets you check your test results more often so you can make faster decisions—without ballooning your false positive rate.

Stop early when the result crosses either boundary.

Efficacy and Futility Boundaries: The Two Statistical “Lines in the Sand”

When you run a GST experiment, you’re not just checking a significance level (or p-value) at the end of your run time. You’re watching two boundaries:

The efficacy boundary (the “win” line).
If your treatment crosses this line, it’s a winner. You can stop the test and roll it out.
The futility boundary (the “cut-your-losses” line).
This is the unsung hero of GST. If your treatment crosses this line, it means the change is either doing nothing, or it's hurting. You can stop the test immediately and iterate. Or, free up that traffic, your time, and your attention for another idea.

LATAM Airlines Gained 54% Efficiency

“At LATAM Airlines, we experienced an 54% efficiency gain in experiment durations by leveraging Group Sequential Testing (GST), allowing teams to make decisions faster and allocate resources more effectively.”