Smartlook

Problem: A new checkout variation wins on conversion rate, but stakeholders worry it may introduce hidden UX issues or long-term friction that aren’t visible in topline metrics alone. Solution: Convert flags the winning variation while Smartlook, enriched with experiment and variation events, lets teams filter replays and heatmaps to only those users. They visually inspect journeys to confirm there’s no new friction. Outcome: Teams ship winners with confidence, backed by both quantitative uplift and qualitative evidence. This reduces rollback risk, accelerates deployment, and builds trust in experimentation decisions.

Problem: A variation increases engagement on key elements but underperforms on final conversions. Standard analytics show the drop-off but don’t explain what’s confusing or breaking for users. Solution: Convert sends experiment and variation names into Smartlook as custom events. CRO teams filter funnels and replays by the losing variation to see exactly where users hesitate, rage-click, or abandon. Outcome: Teams pinpoint specific UX issues causing the loss, turn them into new hypotheses, and launch follow-up tests. What looked like a failed idea becomes an optimized, winning iteration.

Problem: Personalized experiences are live, but it’s unclear whether targeted segments actually navigate and engage as hypothesized, especially across complex multi-step journeys. Solution: Convert personalizations are tracked into Smartlook so each session replay is tagged with the active experience. UX researchers filter by personalization variant to watch end-to-end journeys for that audience. Outcome: Teams confirm whether the personalization truly matches user intent, refine targeting rules, and adjust content based on observed behavior, leading to more relevant experiences and higher segment-level lift.

Problem: An experiment suddenly shows abnormal bounce rates or funnel breaks for one variation, but analytics alone can’t reveal if it’s a technical bug, browser issue, or UX misstep. Solution: With Convert experiment and variation data flowing into Smartlook, teams instantly filter to sessions for the problematic variant. They replay affected visits to see console errors, misaligned elements, or stuck states. Outcome: Bugs are identified and fixed quickly, minimizing lost revenue and test pollution. CRO teams spend less time guessing and more time iterating on stable, high-quality experiments.

Problem: Multiple layout tests are running, but it’s hard to know which specific design elements drive attention or confusion within each variation beyond click-through metrics. Solution: Convert passes variation context into Smartlook, enabling heatmaps segmented by experiment and variant. Teams compare scroll depth, click density, and interaction patterns for each design. Outcome: UX improvements are prioritized based on actual behavior in each layout, not assumptions. This leads to cleaner designs, better content placement, and higher engagement on key elements.

Problem: Experiment reports with only numbers fail to resonate with non-technical stakeholders, making it hard to secure buy-in for future testing and UX investment. Solution: Convert provides the statistical outcomes, while Smartlook—filtered by experiment and variation—supplies curated replays and visual evidence. Marketers embed these into reports to show real user reactions. Outcome: Stakeholders see both the metrics and the human behavior behind them, improving understanding and support for CRO. This unlocks more resources, faster approvals, and a stronger experimentation culture.