AI Feature Prioritization Matrix
Stop guessing which features to build next. Use RICE, ICE, or Value-Effort frameworks to prioritize your product roadmap scientifically. Make data-driven decisions that maximize impact.
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Data-driven feature decisions
Build high-impact features first
Objective prioritization framework
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How many users will this feature reach per month?
How much will this impact each user? (1=minimal, 10=game-changer)
How confident are you in your Reach and Impact estimates?
How much time/resources needed? (1=hours, 10=months)
📊 How RICE Scoring Works:
RICE = (Reach × Impact × Confidence) ÷ Effort
- Reach: How many users affected per time period
- Impact: How much it helps each user (1-10 scale)
- Confidence: How sure you are (percentage)
- Effort: Resources needed (person-months)
Higher RICE score = higher priority. Used by Intercom, Airbnb, Dropbox.
Prioritized Features (0)
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Why We Wasted 6 Months Building the Wrong Features
February 2025. Our SaaS had 50 feature requests. The loudest customer wanted custom reporting. The CEO wanted AI integration. Engineering wanted to rebuild the architecture.
We built the custom reporting first. Took 6 months. Cost $120K in dev time. When we launched it, only 12 customers used it. Meanwhile, competitors shipped dark mode (requested by 300+ users) and we lost market share.
We were building features based on who shouted loudest, not what actually mattered. Then we started using RICE scoring. Everything changed.
What Changed With RICE Prioritization:
• Built custom reporting (1 loud customer) - 6 months, 12 users
• Ignored dark mode (300 quiet requests) - competitors shipped it
• Started AI features (CEO excited) - never finished
• Result: Wasted time, lost customers, team morale down
• Dark mode scored highest (Reach: 1000/mo × Impact: 7 × Conf: 90% ÷ Effort: 2 weeks) = RICE: 315
• Shipped in 2 weeks, used by 847 customers first month
• Email notifications next (RICE: 245) - 1 week, huge engagement boost
• Custom reporting? RICE: 8. Put on backlog.
• Result: Shipped 5 high-impact features in 3 months vs 1 low-impact in 6 months
The 3 Prioritization Frameworks Explained:
1. RICE Score (Most Comprehensive)
Formula: (Reach × Impact × Confidence) ÷ Effort
Best for: Product teams with user data, B2C/B2B SaaS
Example: Dark mode feature:
• Reach: 1,000 users/month will use it
• Impact: 7/10 (nice-to-have, not game-changer)
• Confidence: 90% (we have user surveys)
• Effort: 0.5 months (2 weeks)
• RICE = (1000 × 7 × 0.9) ÷ 0.5 = 12,600
Used by: Intercom, Airbnb, Dropbox
2. ICE Score (Quickest)
Formula: (Impact + Confidence + Ease) ÷ 3
Best for: Growth experiments, early-stage startups, A/B tests
Example: Add social proof badges:
• Impact: 8/10 (could boost conversions)
• Confidence: 6/10 (based on case studies, not our data)
• Ease: 9/10 (1 day to implement)
• ICE = (8 + 6 + 9) ÷ 3 = 7.67
Used by: Growth teams, marketing, fast iteration teams
3. Value vs Effort (Simplest)
Formula: Impact ÷ Effort
Best for: Small teams, limited data, quick decisions
Example: 2×2 matrix:
• High Value, Low Effort = Do First (quick wins)
• High Value, High Effort = Do Next (major projects)
• Low Value, Low Effort = Do Later (nice-to-haves)
• Low Value, High Effort = Don't Do (time wasters)
Used by: Lean teams, agencies, consultants
Common Prioritization Mistakes:
- Building for the loudest customer: One customer screams loudly doesn't mean feature has high reach
- Building what CEO wants: Founders have biases. Data > opinions.
- Ignoring effort: High-impact feature that takes 2 years is worse than medium-impact feature that takes 2 weeks
- Not tracking confidence: Guessing reach/impact without data leads to wrong priorities
- Building everything: Saying yes to all features = shipping nothing important
How to Gather RICE Data:
- Reach: User surveys, analytics (how many use related feature), sales data
- Impact: User interviews, A/B tests, competitor analysis
- Confidence: Rate how much data you have (surveys = high, gut feeling = low)
- Effort: Engineering estimate (story points, person-weeks, complexity)
Use this matrix to prioritize your roadmap scientifically. Stop building features nobody uses. Start shipping high-impact features that grow your product.
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