I’m working on a product redesign where our UX team has gathered a mix of qualitative insights (from user interviews and usability testing) and quantitative analytics (from tools like Hotjar and Google Analytics).
While both data sources are valuable, they sometimes point in different directions:
Usability testing highlights pain points and emotions that analytics can’t capture.
Analytics data shows drop-offs and engagement patterns, but doesn’t always explain why users behave that way.
For example, in our recent redesign of a SaaS onboarding flow, qualitative interviews suggested that users found the onboarding process too guided, but analytics showed higher completion rates after introducing more guidance.
This makes it challenging to decide which insight should take precedence during design decisions.
My question:
What are proven frameworks or methods that help UX teams balance qualitative research findings with quantitative analytics when making design decisions during a product redesign?
I’m especially interested in:
Frameworks that integrate both data types into a unified decision-making process
Examples of prioritization or weighting between “what users say” and “what data shows”
Real-world techniques (e.g., mixed-method UX research, triangulation, data-driven design workshops)
