Justuno

Problem: All visitors see the same generic popup, even while you’re A/B testing different page experiences. This disconnect reduces relevance and can dilute the impact of both your tests and your onsite promotions. Solution: Convert passes each visitor’s active experiment and variation into a JSON object that Justuno can read. You then build Justuno rules like “if user is in Variation B of Test X, show this tailored popup” to align messaging with the tested page. Outcome: Visitors see popups that match the exact experience they’re in, increasing engagement and conversion rates. Your tests run cleaner, and onsite promotions feel like a natural extension of the variation, not a random interruption.

Problem: Control groups often get hit with aggressive discounts or conflicting popups, making it hard to measure the true lift of your experiments and contaminating your baseline performance data. Solution: Using Convert’s experiment and variation data in Justuno rules, you can explicitly exclude control variations from certain promotions. Justuno only triggers popups for test variations, leaving controls free from overlapping offers. Outcome: You preserve clean control data and more accurate test results while still using strong incentives in experimental variations. This leads to better decision-making and clearer attribution of lift to the tested changes, not to random discounts.

Problem: You want to test targeted discounts for high-intent users, but blanket sitewide offers erode margins and don’t let you see which variation and offer mix actually drives incremental revenue. Solution: Convert flags visitors in specific high-intent variations (e.g., product detail page layout B). Justuno uses that variation data to trigger a limited-time discount popup only for those test groups, while others see no or different incentives. Outcome: You isolate the impact of discounts within defined test variations, protecting margin and learning which combination of layout and offer converts best. Over time, you can roll out the most profitable variation-plus-promo combo at scale.

Problem: Headline and value prop tests on landing pages are undermined when sitewide banners or announcement bars push inconsistent messages, confusing visitors and muddying test insights. Solution: Convert sends the active messaging test and variation to Justuno, which then serves matching banners or bars based on that data. Each variation gets its own aligned announcement copy, benefits, or urgency messaging. Outcome: Visitors experience a coherent narrative from hero section to banner, boosting trust and clarity. You see stronger, more reliable performance differences between messaging variations and can confidently scale the winning storyline.

Problem: Email capture popups collect leads without context on which experiment or variation influenced the signup, limiting your ability to personalize follow-up campaigns or analyze which experiences drive better lead quality. Solution: Convert’s experiment and variation info is ingested by Justuno and attached to lead capture events. Justuno then passes those attributes into your ESP/CRM via its own integrations, tagging each contact with the test and variation they saw. Outcome: You can segment email flows based on onsite experience and compare downstream metrics like open, click, and revenue per subscriber by variation. This closes the loop between CRO and lifecycle marketing, revealing which experiences create the best customers.

Problem: Exit-intent popups are one-size-fits-all, ignoring which experiment a user is in and missing chances to recover abandoning visitors with the most relevant, proven message or incentive. Solution: Convert shares experiment/variation data so Justuno can trigger different exit-intent experiences per variation. You can, for example, show a content reassurance message for one variation and a small incentive for another, all driven by test logic. Outcome: Abandoning visitors see tailored, experiment-aware exit messages that reflect what’s already working in their variation. This improves recovery rates, reduces cart abandonment, and helps you identify which variation-plus-exit combo wins before rolling it out broadly.