Medallia

Problem: Teams run many A/B tests in Convert but only see top-line metrics. They lack granular, session-level behavior data to understand why a winning or losing variant performed the way it did. Solution: Convert passes experiment and variation identifiers into Medallia for every session. Medallia then links click paths, scroll depth, rage clicks, and other behavioral signals directly to each test variant. Outcome: Teams move beyond simple conversion rates to understand the behavioral drivers behind results. This leads to more confident decisions about rollouts, design patterns, and future test hypotheses.

Problem: Medallia reports are currently aggregated across all traffic, making it hard to isolate how specific experiments influence user journeys and frustration patterns. Solution: With the integration script installed once, Medallia receives Convert experiment and variation data automatically. Analysts build segments in Medallia for each variant to compare journeys and UX issues side by side. Outcome: Teams quickly see which variants reduce friction, errors, or abandonment in key flows. They prioritize experiences that improve both conversion and user satisfaction, not just short-term wins.

Problem: When a variation loses in an A/B test, teams often guess at the cause, lacking qualitative and behavioral evidence to refine the concept instead of discarding it entirely. Solution: Convert sends variation names into Medallia, enabling drill-down analysis on losing variants. Teams review heatmaps, session replays, and behavioral metrics specifically for users exposed to that variant. Outcome: Instead of abandoning ideas, teams identify precise UX issues—confusing elements, missed CTAs, or slow steps—and iterate smarter. This increases the success rate of subsequent experiments.

Problem: Some tests affect early steps in complex journeys, but standard A/B reports focus only on immediate conversions, missing downstream behavioral effects across multiple sessions and pages. Solution: By embedding Convert experiment context into Medallia sessions, teams track how each variant shapes navigation patterns, return visits, and engagement deeper in the funnel over time. Outcome: Organizations uncover variants that may not spike instant conversions but significantly improve journey quality and progression. They make more balanced decisions that support long-term growth and retention.

Problem: Analysts spend hours exporting data from Convert and Medallia, then manually joining datasets to understand experiment impact on behavior. This slows insight generation and introduces errors. Solution: A one-time integration script automatically attaches Convert experiment and variation identifiers to Medallia sessions. No recurring exports or custom ETL are needed to connect testing and behavior data. Outcome: Teams access ready-to-use experiment segments directly in Medallia, speeding up analysis cycles. They answer stakeholder questions faster and free up analytics resources for higher-value work.

Problem: Attribution of behavioral changes—like reduced rage clicks or improved form completion—to specific tests is unclear, making it hard to prove the UX value of experimentation. Solution: With Generic AB Testing enabled in Medallia and data anonymization off in Convert, human-readable experiment and variation names flow into Medallia. Behavioral improvements are attributed to exact variants. Outcome: CRO and UX teams clearly demonstrate how individual experiments improve digital experience metrics. This strengthens the business case for experimentation and guides investment toward the most impactful ideas.