Beyond the Backlog - Modern Product Prioritization Frameworks

August 2017 finds product teams facing an increasingly common challenge: with limited resources and seemingly unlimited opportunities, how do you decide what to build next? Simple backlog grooming sessions and gut-feel prioritization are giving way to more structured frameworks that help teams make better decisions with greater confidence and alignment.

The Evolution of Product Prioritization

Product prioritization has evolved significantly:

Early Agile Era: Simple stack-ranking of user stories
Mid-2010s: Basic scoring models like RICE and weighted scorecards
Current Approaches: Sophisticated frameworks that incorporate strategic alignment, customer value, and business impact

Prioritization Evolution

This evolution reflects the growing complexity of product decisions and the need for more rigorous approaches to resource allocation.

Popular Prioritization Frameworks in 2017

Several frameworks have gained significant traction among product teams:

1. RICE Scoring Model

Developed by Intercom, RICE evaluates features based on four factors:

Reach: How many users will this impact?
Impact: How much will it affect those users?
Confidence: How certain are we about our estimates?
Effort: How much time will it take to implement?

The RICE score is calculated as (Reach × Impact × Confidence) ÷ Effort, providing a single number for comparison.

This framework excels at comparing tactical features within a product but may not adequately capture strategic considerations.

2. Kano Model

The Kano Model categorizes features based on customer satisfaction:

Basic Expectations: Features that cause dissatisfaction when absent but don't increase satisfaction when present
Performance Features: Features where more functionality linearly increases satisfaction
Delighters: Features that create disproportionate satisfaction but don't cause dissatisfaction when absent

Kano Model

This model helps teams balance must-have functionality with innovative features that can differentiate the product.

3. Opportunity Scoring

Based on Anthony Ulwick's Jobs-to-be-Done framework, Opportunity Scoring identifies high-value opportunities by measuring:

Importance: How important is a particular job to customers?
Satisfaction: How satisfied are customers with current solutions?

The opportunity score is calculated as Importance + (Importance - Satisfaction), highlighting jobs that are important but poorly served.

4. Cost of Delay

Originating from lean product development, Cost of Delay quantifies the economic impact of delaying features:

User Business Value: What's the value to users and the business?
Time Criticality: How does the value decay over time?
Risk Reduction/Opportunity Enablement: What future options does this create or risks does it mitigate?

By dividing the Cost of Delay by the duration, teams can calculate CD3 (Cost of Delay Divided by Duration) to prioritize work.

5. Impact Mapping

A strategic planning technique that visualizes the connection between business goals and deliverables:

Why: Business goals and objectives
Who: Actors who can help or hinder achieving the goal
How: How actors' behavior needs to change
What: Deliverables that support the required changes

This approach ensures that features are directly connected to strategic objectives.

Selecting the Right Framework

No single framework works for all situations. Leading product teams are selecting frameworks based on:

1. Decision Context

Strategic Decisions: Frameworks like Impact Mapping that connect to business goals
Tactical Decisions: Scoring models like RICE for comparing similar features
Customer-Centric Decisions: Kano Model or Opportunity Scoring to focus on user needs

2. Available Information

Data-Rich Environments: Quantitative frameworks like RICE or Cost of Delay
Data-Poor Environments: More qualitative approaches like Impact Mapping
Customer Insight Availability: Kano Model when you have good customer feedback

3. Organizational Culture

Engineering-Driven: Frameworks with clear numerical outputs
Design-Driven: Approaches that emphasize customer experience
Business-Driven: Methods that connect directly to revenue or strategic goals

Implementation Best Practices

Successfully implementing prioritization frameworks requires more than just selecting the right model:

1. Combine Multiple Perspectives

The most effective teams use multiple frameworks in combination:

Strategic frameworks for high-level direction
Tactical frameworks for execution-level decisions
Customer-centric frameworks as a reality check

2. Involve Cross-Functional Stakeholders

Prioritization should not be done in isolation:

Include engineering to assess technical feasibility and effort
Involve design to evaluate user experience implications
Consult sales and customer success for market feedback
Engage executives for strategic alignment

3. Iterate and Refine

Prioritization frameworks should evolve based on results:

Track the outcomes of prioritization decisions
Refine estimation techniques based on actual results
Adjust framework parameters based on changing business conditions

Common Pitfalls to Avoid

Several common mistakes undermine the effectiveness of prioritization efforts:

1. Overcomplicating the Process

Adding too many factors or making scoring too complex:

Reduces transparency and understanding
Creates a false sense of precision
Makes the process too time-consuming

2. Ignoring Qualitative Factors

Relying solely on quantitative measures:

Misses important strategic considerations
Undervalues innovative or disruptive opportunities
Fails to capture emotional or experiential factors

3. Treating Prioritization as a One-Time Event

Viewing prioritization as a periodic activity rather than an ongoing process:

Fails to adapt to changing market conditions
Creates rigid roadmaps that resist new information
Misses opportunities for continuous improvement

Case Studies: Prioritization in Practice

Several companies have developed innovative approaches to prioritization:

Spotify: The DIBB Framework

Spotify uses a framework called DIBB:

Data: What do we know?
Insights: What do we believe?
Beliefs: What do we think we should do?
Bets: What are we going to do?

This approach connects data to insights, insights to beliefs, and beliefs to concrete actions.

Airbnb: Experiment-Driven Prioritization

Airbnb combines prioritization with experimentation:

Initial prioritization identifies promising opportunities
Small experiments validate assumptions
Results inform subsequent prioritization decisions
Resources flow to validated opportunities

Looking Ahead: The Future of Prioritization

As we progress through 2017, several trends are emerging:

AI-Assisted Prioritization: Using machine learning to analyze patterns and predict outcomes
Real-Time Reprioritization: Moving from periodic reviews to continuous adjustment
Customer-Inclusive Prioritization: Directly involving customers in prioritization decisions
Value Stream Optimization: Focusing on end-to-end customer journeys rather than isolated features

Conclusion: From Framework to Philosophy

The most successful product teams are moving beyond rigid frameworks to develop a prioritization philosophy that:

Aligns decisions with strategic objectives
Balances customer needs with business goals
Adapts to changing conditions
Builds organizational trust through transparency

While frameworks provide valuable structure, the ultimate goal is developing the judgment to make good decisions consistently. By combining rigorous frameworks with contextual understanding and continuous learning, product teams can make better decisions about where to invest their limited resources for maximum impact.

As product development continues to evolve, we can expect prioritization approaches to become more sophisticated, data-informed, and closely tied to measurable business outcomes.

This article was written by Nguyen Tuan Si, a product management specialist with experience implementing various prioritization frameworks across different organization types and sizes.