I went straight to impact and effort, which felt right in the moment, but I think I skipped over the part where you actually have to manage the relationships involved.
Start by framing prioritization as a structured, data-driven process that aligns with company strategy and OKRs. Emphasize collaboration with stakeholders to understand the 'why' behind each request, then use a transparent framework to evaluate and communicate trade-offs. Conclude with how you'd communicate decisions and manage expectations to maintain trust.
Pro tip: At LinkedIn, tie every prioritization decision back to member value and the company's vision—this shows you think beyond feature requests and consider the broader ecosystem. Also, mention that you'd document and share your prioritization criteria openly to reduce friction and build alignment.
Assess how each request aligns with LinkedIn's strategic priorities and OKRs. Requests that directly impact key metrics or member value should be weighted higher.
Quantify potential impact using data (e.g., reach, engagement, revenue) and effort (e.g., engineering resources, time). Use a scoring model like RICE or weighted scoring to compare objectively.
Discuss with requesting teams to understand urgency, dependencies, and underlying needs. Look for opportunities to combine requests or find alternative solutions.
Apply the prioritization framework to rank requests, considering trade-offs and opportunity costs. Make a decision that balances short-term wins with long-term strategy.
Transparently communicate the rationale behind decisions to all stakeholders. Set up a process to revisit priorities regularly as new information emerges.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.