GetFeedback Suite by SurveyMonkey (Formerly Usabilla)

Problem: A variation wins on conversion rate, but support tickets and vague complaints increase. Teams can’t connect negative comments to specific test experiences, making it hard to understand trade-offs. Solution: Convert passes experiment and variation names into Usabilla as custom variables. Teams filter feedback by variation to see exactly what users say about each experience during the test. Outcome: Marketers keep the high-performing variant while quickly fixing the issues users report. They preserve uplift, reduce friction, and avoid rolling back a profitable change based on incomplete data.

Problem: Experiment owners ship bold messaging or UX changes but only see quantitative metrics. They lack fast, structured input from users actually exposed to the new experience. Solution: Usabilla campaigns are targeted only to visitors in specific Convert experiments or variations. Feedback prompts appear in-context, asking focused questions about the new design or copy. Outcome: Teams validate or refine hypotheses in days, not weeks. They understand user reactions behind performance shifts and can iterate on variations with confidence instead of guessing.

Problem: An A/B test shows no significant lift overall, but analytics hints at friction for certain segments. Without experiment-aware feedback, UX teams can’t pinpoint what’s going wrong in each variation. Solution: Convert’s experiment and variation data is attached to every Usabilla response. Researchers segment comments by variation to spot recurring issues—like confusing labels or broken flows—unique to one experience. Outcome: Teams identify and fix variation-specific UX problems that averages obscure. Future tests are cleaner, with fewer hidden defects, leading to clearer results and more reliable learnings.

Problem: Product and CRO teams see many ideas in the backlog but limited capacity. Quantitative test data alone doesn’t show which changes users actually love or hate enough to influence prioritization. Solution: By syncing Convert context into Usabilla, teams compare sentiment scores and themes across variations. They see which experiments generate enthusiastic comments versus frustration or confusion. Outcome: Roadmaps are guided by both impact and user sentiment. Teams double down on variations that delight users and deprioritize those that technically work but erode trust or satisfaction.

Problem: Generic on-site surveys hit all visitors, leading to low response rates and noisy feedback. High-value test cohorts get over-surveyed while others receive irrelevant prompts. Solution: Usabilla campaigns use Convert experiment and variation as targeting rules. Feedback is requested only from users in strategic tests or specific variations where insight is most needed. Outcome: Survey volume drops while relevance and response quality increase. Teams collect richer, more actionable feedback from the right users at the right test moments, without harming UX.

Problem: After a test ends, analysts manually cross-reference feedback tools and test logs. This slows down learning cycles and often misses nuance about why a variation underperformed. Solution: Every Usabilla response already includes the Convert experiment and variation as custom fields. Analysts instantly slice feedback by variation alongside conversion metrics in their reports. Outcome: Post-test reviews become faster and deeper. Teams move from “what happened” to “why it happened” in a single pass, accelerating iteration and improving the quality of future experiments.