PRIORITIZATION

Product prioritization frameworks help product managers make data-backed decisions about feature development and resource allocation.

Books:

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MUST READ BLOGS:

• Intercom: RICE: Simple prioritization for product managers

The original post that introduced the RICE framework (Reach, Impact, Confidence, Effort). Explains how to score features objectively, avoid personal bias in decision-making, and implement a systematic approach that scales across teams. Essential foundation for data-driven prioritization. Read More.

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Prioritization Frameworks Table:

Framework	Core Components	Best Used For	Strengths	Limitations	Example
RICE	Reach, Impact, Confidence, Effort	Roadmapping, feature ranking	Quantitative scoring, helps reduce bias	Requires good estimation accuracy	Feature A affects 10K users, has 80% confidence, low effort → high RICE
MoSCoW	Must, Should, Could, Won’t	MVP planning, team discussions	Simple, intuitive for stakeholders	No scoring or trade-off evaluation	Login = Must-have, Profile Picture = Could-have
Kano Model	Basic, Performance, Delighter	Understanding user delight	Focuses on emotional drivers	Requires deep user input	Free delivery = Delighter, Fast checkout = Performance
ICE	Impact, Confidence, Ease	Quick decisions in growth experiments	Fast, lightweight	Shallow for strategic decisions	Idea B has high impact, easy to test → top ICE score
Value vs Effort Matrix	2×2 Quadrant (High/Low Value vs. Effort)	Visual prioritization workshops	Great for teams new to prioritization	Oversimplified, subjective quadrants	High value & low effort = “Quick Win”
Opportunity Scoring	Importance vs Satisfaction (user ratings)	User-centric prioritization	Highlights unmet needs	Needs reliable survey input	Feature X is important but unsatisfying → key priority
Weighted Scoring	Custom weights (e.g., revenue, retention, urgency)	Custom strategic prioritization	Highly tailored, transparent math	Subjective and time-consuming to build	Revenue (40%) + Retention (30%) weighted scores