SessionCam now part of Glassbox

Problem: A homepage variant wins on sign‑ups but support tickets and NPS suggest something in the new flow is confusing or frustrating users, and analytics alone can’t reveal what’s going wrong. Solution: Convert tags every SessionCam recording with experiment and variation names. Teams filter replays for the winning variation and watch how users navigate, hesitate, or rage‑click through the new flow. Outcome: UX issues are spotted quickly, fixes are prioritized, and the team keeps the conversion lift while reducing friction, support load, and long‑term churn risk from a flawed ‘winner’ experience.

Problem: A variation underperforms in an A/B test, but the aggregate metrics don’t explain why. Stakeholders are reluctant to kill it without understanding what specifically failed. Solution: Using Convert’s experiment and variation labels in SessionCam, analysts filter to only the losing variation’s sessions and review recordings to see where users drop off, get stuck, or misinterpret UI elements. Outcome: Teams identify concrete UX and messaging issues, salvage strong ideas from the losing variant, and design better follow‑up tests instead of discarding concepts based purely on topline numbers.

Problem: Radical redesign experiments show mixed or neutral impact on conversions, leaving teams unsure whether to roll out, revert, or iterate because they can’t see how behavior actually changed. Solution: Convert pushes experiment and variation data into SessionCam, enabling side‑by‑side behavioral analysis. Teams compare recordings of control vs. redesign to validate hypotheses about navigation, content discovery, and task completion. Outcome: Decisions are based on observed user journeys, not just aggregate KPIs. Teams confidently iterate on redesigns, focusing on behaviors that correlate with long‑term value, not just short‑term conversion swings.

Problem: An experiment shows overall neutral impact, but some user segments may be winning while others are struggling. Standard analytics can’t easily reveal segment‑specific friction inside each variation. Solution: With Convert’s custom variables available in SessionCam, teams segment recordings by experiment, variation, and additional attributes (e.g., device type, traffic source) to inspect behavior for each micro‑segment. Outcome: UX fixes and personalization ideas are prioritized for the segments with the highest friction and value, turning a ‘flat’ test into targeted improvements that drive incremental revenue from key audiences.

Problem: Checkout experiments change button labels, field order, or trust elements, but funnel reports only show drop‑off rates, not the precise moments or interactions that cause abandonment. Solution: Convert automatically tags checkout sessions in SessionCam by experiment and variation. CRO teams filter to specific test groups and watch recordings to see form hesitations, validation errors, and distraction points. Outcome: Concrete UX issues in each variation are identified and fixed, leading to smoother checkout experiences, higher completion rates, and more confident rollouts of high‑impact checkout changes.

Problem: After a series of A/B tests, the team has results but limited insight into user motivations, making it hard to craft the next wave of high‑leverage experiments with clear hypotheses. Solution: By connecting Convert with SessionCam, every past experiment’s sessions can be filtered and replayed by variation. Teams mine these recordings for recurring behavioral patterns, objections, and navigation shortcuts. Outcome: Future tests are grounded in real behavioral insights, with sharper hypotheses and stronger variants. This accelerates the experimentation cycle and increases the win rate of subsequent tests.