5 Feature Prioritization Frameworks That Actually Work (With Templates)

The Prioritization Problem

Every product team faces the same challenge: too many ideas, too little time. According to Intercom's research, the average product team has 5x more feature requests than they can build.

The solution? A systematic approach to prioritization that removes emotion and politics from the equation.

Framework 1: RICE Scoring

RICE is one of the most popular frameworks, developed by Intercom. It stands for:

The Components

• Reach: How many customers will this impact? (per quarter)
• Impact: How much will it impact each customer? (0.25x to 3x)
• Confidence: How confident are you in these estimates? (0-100%)
• Effort: How many person-months will this take?

The Formula

RICE Score = (Reach × Impact × Confidence) / Effort

When to Use RICE

RICE works best when:

You have quantitative data about customer reach

Your team can estimate effort in person-months

You need to compare many different feature ideas

RICE Example

Framework 2: ICE Scoring

ICE is a simpler alternative to RICE:

The Components

• Impact: Potential impact on the key metric (1-10)
• Confidence: How sure are you? (1-10)
• Ease: How easy is it to implement? (1-10)

The Formula

ICE Score = Impact × Confidence × Ease

When to Use ICE

ICE is ideal for:

• Early-stage startups with limited data
• Quick prioritization sessions
• Teams without dedicated analysts

Framework 3: MoSCoW Method

MoSCoW categorizes features into four buckets:

The Categories

Must Have: Critical for launch/release

Should Have: Important but not critical

Could Have: Nice to have if time permits

Won't Have: Explicitly out of scope (for now)

When to Use MoSCoW

MoSCoW excels at:

• Scope management for fixed deadlines
• Communicating priorities to stakeholders
• MVP definition for new products

Framework 4: Value vs. Effort Matrix

The classic 2x2 matrix for quick decisions:

The Quadrants

• Quick Wins (High Value, Low Effort): Do first
• Big Bets (High Value, High Effort): Plan carefully
• Fill-Ins (Low Value, Low Effort): Do if time permits
• Time Sinks (Low Value, High Effort): Avoid

Framework 5: Kano Model

The Kano Model categorizes features by customer satisfaction:

The Categories

Basic Needs: Expected features (absence = dissatisfaction)

Performance Needs: More is better (linear satisfaction)

Delighters: Unexpected features (presence = delight)

Choosing the Right Framework

By Team Size

By Product Stage

• Pre-PMF: MoSCoW + ICE for rapid iteration
• Growth: RICE for data-driven decisions
• Mature: Kano for differentiation

Automating Prioritization

Modern tools like reBacklog automate much of this process:

• Automatic reach estimation from analytics data
• Impact prediction based on competitor analysis
• Effort estimation from similar past projects

Learn more about AI-powered prioritization →

Getting Started

Ready to implement a prioritization framework?

Start simple: Begin with ICE or Value/Effort

Gather data: Use competitor analysis tools to inform estimates

Iterate: Refine your process based on outcomes

For teams looking to automate this process, try reBacklog to see how AI can augment your prioritization decisions.

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