Company Context

NotionFlow is a B2B SaaS workflow platform used by 25,000 small and mid-sized businesses for task management, approvals, and internal collaboration. The company has strong adoption in operations teams, but leadership believes product decisions rely too heavily on anecdotal customer requests and post-launch support tickets.

Problem

NotionFlow is preparing to launch a new AI-assisted workflow builder that helps users create automations from plain-language prompts. The team has 12 weeks to take the product from concept validation to beta launch. In prior launches, the company discovered major usability issues only after release, leading to low activation and expensive rework. For the last major feature, only 22% of users who started setup completed it, and support volume increased 30% in the first month.

The VP of Product wants a clear plan for how user feedback should be gathered and used at different stages of the product development cycle: discovery, definition, design, beta, launch, and post-launch iteration. The goal is to improve decision quality without slowing delivery.

Deliverables

1. Define how you would gather user feedback at each stage of the product development cycle for this feature.
2. Identify which user segments you would prioritize and why.
3. Explain how you would combine qualitative feedback with product usage data to make decisions.
4. Recommend how feedback should influence scope, prioritization, and launch readiness.
5. Describe the key trade-offs in building a lightweight but effective feedback system.

Constraints

• MVP beta must launch within 12 weeks.
• Research resources are limited: 1 product designer, 1 PM, and shared access to a central research ops team.
• Engineering can support only lightweight instrumentation before beta.
• The feature serves both admins who configure workflows and end users who trigger them, but the first release cannot fully optimize for both personas.