5 Strategies for Product Managers to Prioritize Product Features

As a product manager, one of the most challenging tasks is deciding which features to prioritize. With limited resources and competing demands, making the right choices can make or break your product’s success. Below are five proven strategies, along with real-world examples, calculations, and the origins of each method to help you make data-driven decisions.

1. RICE Scoring Model

The RICE framework was introduced by Intercom, a customer messaging platform, to help their product teams prioritize features in an objective and data-driven way. It evaluates features based on Reach, Impact, Confidence, and Effort, assigning each a score to rank them effectively.

Breaking Down the RICE Components:

• Reach – How many users will be affected?

• Measured in absolute numbers (e.g., number of users per month).

• Example: If a feature is expected to impact 10,000 users per month, Reach = 10,000.

• Impact – How much will this feature contribute to key goals?

• Rated on a scale (e.g., 1 = minimal, 5 = massive).

• Example: If a feature will significantly improve user engagement, assign Impact = 3 (medium-high impact).

• Confidence – How certain are you about Reach and Impact estimates?

• Expressed as a percentage (100% = very confident, 50% = some uncertainty).

• Example: If the data is strong but some assumptions exist, set Confidence = 80% (0.8).

• Effort – How much work is required to build this feature?

• Measured in person-weeks (time required by one person to complete the task).

• Example: If a feature requires a team of five for two weeks, the total Effort = 10 person-weeks.

Example Calculation:

A team is considering adding dark mode to an app.

• Reach: 10,000 users

• Impact: 3 (medium)

• Confidence: 80% (0.8)

• Effort: 10 person-weeks

If another feature, customizable themes, scores 1,500, then dark mode is prioritized first.

2. MoSCoW Method

The MoSCoW method was developed by Dai Clegg in the 1990s while working at Oracle. It categorizes features into Must-have, Should-have, Could-have, and Won’t-have (for now) to help teams prioritize work effectively.

Example:

For an e-commerce mobile app, features might be categorized as:

• Must-have: Secure checkout process

• Should-have: Product reviews

• Could-have: Wishlist feature

• Won’t-have: Augmented reality (AR) previews (for now)

Since checkout is critical for launching, it takes precedence over other features.

3. Kano Model

The Kano Model was developed by Dr. Noriaki Kano, a Japanese researcher in customer satisfaction, in the 1980s. It categorizes features based on how they impact customer satisfaction:

• Basic needs (Must-haves) – Expected features that prevent dissatisfaction.

• Performance features (Nice-to-haves) – The more, the better.

• Delight features (Surprises) – Unexpected but highly appreciated.

Example:

For a video streaming platform, features are categorized as:

• Offline downloads – Must-have

• AI-powered recommendations – Performance feature

• Surprise celebrity Q&A sessions – Delight feature

A user survey confirms that offline downloads are non-negotiable, while other features can be phased in later.

4. Cost-Benefit Analysis

The Cost-Benefit Analysis (CBA) framework was popularized by Jules Dupuit, a French engineer and economist, in the 19th century. It weighs the expected benefits of a feature against its development costs to ensure a strong return on investment (ROI).

Example Calculation:

A SaaS company is deciding between two third-party integrations:

Feature Estimated Revenue Development Cost ROI Calculation

Integration A $100,000/year $30,000 (100,000 - 30,000) / 30,000 = 233%

Integration B $60,000/year $20,000 (60,000 - 20,000) / 20,000 = 200%

Since Integration A has a higher ROI (233%), it is prioritized over Integration B.

5. Customer Feedback & Data-Driven Insights

This approach is rooted in the concept of User-Centered Design (UCD), which dates back to Donald Norman and his work in cognitive science and usability in the 1980s. It combines qualitative feedback (surveys, interviews) with quantitative data (analytics, behavior tracking) to ensure prioritization aligns with real user needs.

Example:

An email marketing platform finds that 50% of users request an AI-powered subject line generator. Data also shows that users who A/B test subject lines see a 15% higher engagement rate.

Since both qualitative and quantitative data support the feature, it is prioritized for development.

Final Thoughts

There’s no one-size-fits-all approach to feature prioritization. The best strategy often combines multiple frameworks, depending on your product’s goals and market conditions. By using a structured approach, product managers can make better decisions, align teams, and deliver features that drive meaningful impact.

Which method do you find most effective? Share your thoughts in the comments!