Define your experiment
Name the variants, set the goal metric, and choose the user segments you want to include — all from the experiment builder.
Experiment Analysis
Know which variant winsbefore you ship.
Run A/B tests with confidence. ACAI compares conversion paths, cohort behaviour, and statistical significance across every variant — and explains what drove the result.
Checkout Flow Experiment
14 days · 42,680 sessions · AI confidence 96%
Variant B wins
Control
3.8%
Baseline
Variant A
4.9%
+28.9%
Variant B
5.6%
+47.4%
Conversion by day
Ship recommendation
Roll out Variant B — returning mobile users show strongest lift at +62%.
Confidence threshold
95%statistical significance out of the box
Variants supported
UnlimitedA/B, multivariate, and holdout groups
Analysis depth
6 layersconversion, retention, segment, device, source, cohort
How it works
From your first event to your first answer.
Let events flow in
ACAI captures every interaction for all variant groups from the moment the experiment is live, with no sampling.
Compare variants automatically
As data arrives, ACAI calculates conversion rates, lift percentages, and statistical confidence for every variant in real time.
Get the ship decision
ACAI explains which variant wins, why it won, which segments responded best, and what to watch after rollout.
Capabilities
Everything the feature does.
Statistical significance engine
Bayesian and frequentist methods with configurable confidence thresholds and minimum sample sizes.
Segment-level breakdown
See lift by device type, traffic source, user role, plan tier, geography, or any custom property.
Multi-metric tracking
Track primary and guardrail metrics simultaneously so you catch regressions before they ship.
Early stopping detection
ACAI flags when a variant has enough data to call and warns against stopping too early.
Post-ship monitoring
Keep watching variant metrics after rollout to detect novelty effects and long-term retention changes.
AI winner explanation
Plain-language summaries explain the primary driver, best segment, and recommended rollout plan.
Experiment Intelligence
Numbers that change how teams decide what to ship.
Experiment analysis works when teams trust the result and act on it quickly. These metrics show how ACAI reduces experiment duration and improves winner call accuracy.
Statistical confidenceSegment breakdownGuardrail metricsAI ship decision
Avg. experiment duration
11d-38%
Faster statistical confidence means teams ship winners sooner.
1 / 3
Use Cases
When teams reach for this feature.
Does one-page checkout outperform multi-step?
ACAI compares conversion, drop-off step, and mobile vs desktop behaviour across all variants with one click.
Which welcome flow activates more users?
Track activation rate, time-to-value, and D7 retention for each onboarding variant without manual cohort work.
Does annual pricing messaging lift upgrades?
Compare upgrade rates, revenue per user, and expansion signals across pricing copy variants.
Ship winners faster with statistically valid decisions
Avoid false positives with guardrail metric monitoring
Understand which user segments respond to each variant
Get a written rationale for every ship or no-ship call