Company Context

You’re joining QuillAI, a Series C B2B SaaS company that sells an AI writing assistant to mid-market and enterprise customers. QuillAI has 3.2M monthly active users across web and Chrome extension, with ~420K weekly active users. Revenue is primarily seat-based ($18–$35/user/month) with enterprise contracts. QuillAI competes with Grammarly Business, Microsoft Copilot, and Notion AI.

QuillAI’s leadership believes the company is hitting a “local maximum”: teams keep shipping features (tone controls, templates, meeting notes) but core user outcomes aren’t improving. The CEO wants QuillAI to become “the system of record for workplace writing,” but the product org lacks a consistent way to ensure research creates durable knowledge that compounds over time.

User / Market Scenario

QuillAI serves three primary personas:

Competitive pressure is rising: Copilot is bundled into existing Microsoft contracts, and Grammarly is pushing “AI governance” features. Enterprise buyers increasingly ask for auditability, consistency, and measurable productivity gains.

Problem / Opportunity

Over the last 2 quarters:

• Activation (user completes “first successful draft” within 24 hours of signup) fell from 41% → 34%.
• 8-week retention for new seats dropped from 52% → 45%.
• Feature adoption is fragmented: the median active user uses only 1 feature repeatedly.

Your research team runs many studies, but stakeholders complain:

1. Findings are not discoverable (buried in decks/Notion pages).
2. Insights are not comparable across quarters (different definitions, different segments).
3. Research is not decision-linked (teams ship anyway; no clear “so what”).

The VP of Product asks you to propose a plan that ensures research contributes to QuillAI’s internal knowledge base and drives better product decisions—while still moving fast.

Your Task (Deliverables)

In this interview, assume you are the PM owning Activation & Early Retention. Walk through how you would:

1. Define what “contributing to the knowledge base” means for QuillAI (what artifacts, what standards, what makes an insight reusable).
2. Design a research-to-decision system that turns studies into durable, searchable knowledge and influences roadmap choices.
3. Prioritize 3 research initiatives for the next 8 weeks that will most improve activation/retention and reduce repeated learning.
4. Propose how you’ll socialize and operationalize insights across Product, Design, Data Science, and Sales/CS.
5. Define success metrics for both (a) product outcomes and (b) knowledge-base outcomes.

Constraints

• Timeline: 8 weeks to show measurable progress.
• Team: 1 PM (you), 1 researcher (shared), 1 data scientist (50%), 4 engineers.
• You must support SOC 2 and enterprise privacy expectations; you cannot store raw customer content in internal docs without redaction.
• You cannot pause feature delivery entirely; at least one activation experiment must ship during the 8 weeks.
• Existing tooling: Amplitude, Looker, Gong (sales calls), Zendesk (support tickets), Notion, and an internal feature flag system.

Additional Data (What you know today)

• Users who connect QuillAI to Google Docs/Outlook in week 1 have +18pp higher 8-week retention.
• 27% of churned seats cite “not sure when to use it” in exit surveys.
• Enterprise admins complain that teams create inconsistent prompts and “shadow processes,” making ROI hard to prove.

You do not need to write PRDs. Focus on how you’d ensure research produces compounding learning and better prioritization under real constraints.