Systematic Experimentation
Replace isolated A/B testing tools with systematic experimentation across journeys, messaging, offers, and experiences. Use product data as the experiment backbone.
One testing brain for your store
Stop running isolated experiments. Use one shared memory for every visit, clicks, purchase and more.
Connected site/app, catalog, and campaigns
Combine Shopify, web/app events, email, SMS, push so all tests read from the same data.
Unified shopper profiles & lifecycle segments
Segment into Champions, Regulars, Promising, Needs Attention, At Risk using behavior.
Shared targeting across tests
Use the same segments for website, recommendation, and journey experiments.
Experiment on journeys, not just pages
Test the full experience from landing to checkout and across key merchandising surfaces.
Website experience experiments
Run A/B and multi-variant tests on complete journeys and compare conversion and AOV by variant.
Recommendations experiments
Experiment with cross-sell, lookalike strategies and measure clicks and AOV lift per segment.
Messaging & journey experiments
Test static vs dynamic campaigns, trigger logic, and timing rules in journeys.
CRO results you can trust and scale
See which ideas actually move revenue and roll them out with confidence.
Segment-level performance
Break results down by lifecycle segment, device, and channel to see what works for high-value customers.
Statistically sound decisions
Use built-in experiment metrics to compare variants on conversion and revenue per visitor.
Playbooks that compound
Turn winning variants into new defaults so every test improves the next one.
Unlock advanced experimentation tactics
Explore a curated library of proven A/B testing and CRO playbooks for eCommerce. Filter by goal—conversion rate, AOV, checkout optimization—to discover experiments that drive measurable revenue lift.
Frequently Asked Questions
Everything you need to know about experimentation and CRO
You can test website elements, product recommendations, email content, popup timing, and full journey flows—all from one platform with unified reporting.
Intempt uses Bayesian statistics with automatic sample size calculations. You'll see confidence intervals and probability to beat control, so you know when results are trustworthy.
Yes. You can target experiments by lifecycle stage, purchase history, traffic source, or any behavioral attribute—so you optimize for your best customers, not just overall averages.
Intempt's Bayesian approach works with smaller sample sizes than traditional frequentist methods. You'll still get directional insights even with modest traffic.
Intempt's experiment manager shows active tests and traffic allocation, preventing overlap. You can also set mutual exclusion rules for sensitive pages.
Ready to scale your experiments?
Test smarter, move faster, and grow with confidence.