Kano Study

Tools & Methods: Kano model survey, attractiveness scoring, pairwise parametric testing, follow-up quantitative deep dive.

The Petline roadmap had many features sitting in "next" and "later" boxes with little structured guidance on which to build first. Historically, roadmap decisions were shaped by one-off conversations between PMs and large accounts — not research. I introduced this pilot as a data-driven alternative.

I designed a Kano study covering 17 potential features — planned and unplanned — to tell the team which features would delight, which would be tolerated, and which would backfire. Feature selection drew on PM discussions, existing roadmap parking lots, and features surfaced in unrelated research. Cognitive testing with internal employees validated that descriptions read clearly to practice staff before launch.

Key findings: downloadable rabies certificates ranked highest in attractiveness; AI text summaries ranked lowest and showed reverse responses in a significant minority; a waitlist feature could book into open slots with as little as 1 hour of lead time with the right guardrails. Most features had no overall reverse responses — implementation risks were about how features were built, not whether to build them.

AI text summaries were approached with substantially more care than they otherwise would have been. Downloadable vaccination records were immediately recognized as high-value. The study demonstrated that structured prioritization data could productively shape roadmap conversations previously decided by ad hoc PM/account discussions.