# Example: PRD Created with PRD Template Skill

This is an example of what the PRD Template Skill produces.

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# PRD: Smart Notification Prioritization

## Overview

**Problem Statement**: Users currently receive an average of 47 notifications per day across email, Slack, and in-app channels, leading to notification fatigue and important updates being missed. 73% of users report having disabled notifications entirely, resulting in missed critical communications. Support tickets related to "missed important updates" have increased 45% quarter-over-quarter.

**Proposed Solution**: Implement an intelligent notification system that learns from user behavior to prioritize notifications based on importance, urgency, and personal relevance. The system will use ML to surface critical notifications while suppressing low-priority ones, reducing notification volume by 60-70% while ensuring no high-priority items are missed.

**Success Metrics**:
- Reduce average daily notifications from 47 to 15-20 (60-70% reduction)
- Increase notification click-through rate from 8% to 25%
- Decrease support tickets about missed updates by 50%
- Maintain or improve user NPS (currently 42)
- Achieve 60%+ adoption of smart notifications within 30 days of launch

## Context & Background

**Why Now**: 
- User satisfaction with notifications has dropped from NPS 35 to NPS 12 over past 6 months
- Competitors (NotifyHub, SmartFlow) have launched similar features in Q4 2025
- Q1 2026 company objective is "Improve user engagement by 20%"
- Technical foundation (ML platform) is now mature enough to support this

**Strategic Alignment**:
- Aligns with company mission to "reduce digital noise"
- Supports Q1 OKR: Increase DAU by 15%
- Enables future personalization features planned for 2026

**User Research Summary**:
We conducted 15 user interviews and surveyed 2,400 users in December 2025. Key findings:
- 73% of users have disabled some or all notifications
- Users check app 4.2x per day (down from 8.1x 6 months ago)
- #1 requested feature is "smarter notifications" (mentioned by 82% of interviewees)
- Users want control but not complexity - prefer "works automatically" over manual rules

## User Stories & Use Cases

**US1: Intelligent Prioritization**
As a busy professional, I want to receive only truly important notifications so that I can focus on my work without constant interruptions.

Acceptance Criteria:
- System learns from my behavior (which notifications I interact with)
- High-priority notifications are never suppressed
- Low-priority notifications are batched or suppressed
- I can override prioritization for specific channels/people
- System shows confidence level for prioritization decisions

**US2: Priority Override**
As a user, I want to mark certain people or channels as "always notify" so that I never miss updates from my manager or critical systems.

Acceptance Criteria:
- Can mark users, channels, or keywords as high priority
- Can mark users, channels, or keywords as low priority
- Priority overrides ML decisions
- Can manage overrides in settings
- Changes take effect immediately

**US3: Digest Mode**
As a user, I want to receive low-priority notifications in a daily digest so that I stay informed without being interrupted throughout the day.

Acceptance Criteria:
- Digest delivered at time I specify (default 8am)
- Contains all non-urgent notifications from previous 24 hours
- Grouped by source and topic
- Can click through to original context
- Can adjust digest frequency (daily, weekly, never)

**US4: Notification Explanation**
As a user, I want to understand why I received (or didn't receive) a notification so that I can trust and adjust the system.

Acceptance Criteria:
- Each notification shows why it was prioritized high/low
- Can view suppressed notifications in notification center
- Can provide feedback ("should have been high priority")
- System learns from feedback
- Transparency builds trust

## Requirements

### Functional Requirements

**P0 (Must-Have for MVP)**:
- ML model that learns from user behavior (open rate, click rate, dwell time)
- Notification priority classification (High/Medium/Low)
- User-configurable priority overrides (people, channels, keywords)
- Digest mode for low-priority notifications
- Settings page for notification preferences
- Ability to see suppressed notifications

**P1 (Should-Have for V1.1)**:
- Notification explanation/reasoning
- Smart frequency capping (no more than X high-priority per hour)
- Do Not Disturb mode with smart exceptions
- Notification scheduling (delay until specific time)
- Cross-device consistency

**P2 (Nice-to-Have for Future)**:
- Contextual notifications (based on what I'm doing)
- Team notification preferences (manager can set defaults)
- Integration with calendar (suppress during meetings)
- Natural language configuration ("notify me about urgent customer issues")

### Non-Functional Requirements

**Performance**:
- Prioritization decision in <100ms
- No impact on notification delivery speed
- Model inference <50ms

**Privacy**:
- All ML training happens on-device or with anonymized data
- User can opt out of ML training
- Clear privacy policy about data usage
- GDPR compliant

**Scalability**:
- Support 100k users in MVP
- Scale to 1M users by end of Q2
- Handle 10M+ notifications per day

**Accuracy**:
- 90%+ accuracy on priority classification
- <1% false negative rate on high-priority items
- Users can correct mistakes easily

**Accessibility**:
- Notification priority indicated via text, not just color
- Screen reader compatible
- Keyboard navigation support
- Respects system-level accessibility settings

## Design & User Experience

**Key Flows**:

1. **First-Time Setup** (30 seconds)
   - Welcome modal explains smart notifications
   - User chooses: Auto mode vs Manual configuration
   - If manual: Configure 2-3 high-priority sources
   - One-click enable

2. **Receiving Smart Notification**
   - High-priority: Immediate delivery (same as today)
   - Medium-priority: Delivered but not pushed to phone
   - Low-priority: Added to digest
   - Visual indicator of priority level

3. **Reviewing Digest**
   - Daily email/in-app digest
   - Grouped by topic
   - Quick actions (archive, star, open)
   - One-click feedback

4. **Adjusting Priorities**
   - Right-click any notification → "Always/Never notify from this source"
   - Settings page with list of overrides
   - Smart suggestions based on behavior

**Wireframes**: [Link to Figma - designs/smart-notifications-v2]

**Edge Cases**:
- User has never interacted with notifications (no training data)
  - Solution: Use heuristics for first 2 weeks, then switch to ML
- User marks everything high priority
  - Solution: Show warning, explain defeats purpose
- System makes obvious mistake
  - Solution: Prominent feedback option, apologize, learn

## Technical Considerations

**Architecture**:
- ML model: TensorFlow Lite for on-device inference
- Backend: Python service for model training
- Storage: User preferences in PostgreSQL, training data in BigQuery
- Real-time: Kafka for notification event stream

**Dependencies**:
- ML platform team (model training infrastructure)
- Notification service (existing system to extend)
- Mobile team (push notification handling)
- Data team (training data pipeline)

**Technical Risks**:
1. **ML model accuracy may be insufficient**
   - Mitigation: A/B test with 10% of users, fall back to rule-based if accuracy <85%
   - Backup: Manual rule configuration always available

2. **On-device inference may be too slow**
   - Mitigation: Benchmark on low-end devices early
   - Backup: Server-side inference with caching

3. **Cold start problem (new users)**
   - Mitigation: Use heuristic rules for first 2 weeks
   - Backup: Offer quick setup wizard with common patterns

**Data Requirements**:
- Need 4-6 weeks of notification interaction data for training
- Minimum 1000 notification interactions per user for good accuracy
- Will use existing data from past 6 months

## Implementation Plan

### Phase 1: MVP (8 weeks)
**Goal**: Smart prioritization working for 10% of users

- Week 1-2: ML model development and training
- Week 3-4: Backend service implementation
- Week 5-6: Frontend and mobile integration
- Week 7: Internal testing
- Week 8: Beta launch to 10% of users

**Deliverables**:
- ML model with 85%+ accuracy
- Priority classification (High/Low only, no digest)
- Basic user overrides
- Settings page
- Analytics instrumentation

### Phase 2: V1.0 (4 weeks)
**Goal**: Polish and scale to 100% of users

- Week 9-10: Add Medium priority and digest mode
- Week 11: Performance optimization and bug fixes
- Week 12: Ramp to 100% of users

**Deliverables**:
- Three-tier priority (High/Medium/Low)
- Digest mode
- Notification explanations
- Documentation and support materials

### Phase 3: V1.1 (6 weeks)
**Goal**: Advanced features and refinement

- Week 13-14: Frequency capping
- Week 15-16: DND mode with exceptions
- Week 17-18: Cross-device consistency

**Deliverables**:
- Smart frequency capping
- DND mode
- Refined ML model based on production data

## Open Questions

**Product Questions**:
1. Should we allow notification prioritization for external emails, or only in-app?
   - **Owner**: Tom (PM) to research email provider APIs
   - **Due**: Feb 1

2. What's the right default digest frequency? Daily or weekly?
   - **Owner**: Jennifer (Design) to user test both options
   - **Due**: Jan 28

3. Do we need a "learning period" message while model trains?
   - **Owner**: Tom (PM) to draft messaging options
   - **Due**: Jan 25

**Technical Questions**:
1. Can we achieve <100ms inference on older devices?
   - **Owner**: Mike (Eng) to benchmark on 5 device types
   - **Due**: Jan 27

2. What's our data retention policy for training data?
   - **Owner**: Sarah (CPO) to consult Legal team
   - **Due**: Feb 3

**Business Questions**:
1. Should this be a paid feature or included in free tier?
   - **Owner**: Sarah (CPO) to consult with Product Marketing
   - **Due**: Feb 10

## Success Criteria

### Launch Criteria (Before 100% Rollout)
- [ ] 85%+ ML accuracy in beta
- [ ] <1% false negative rate on high-priority
- [ ] User NPS score of 7+ (out of 10) in beta
- [ ] Zero critical bugs
- [ ] Performance <100ms 95th percentile
- [ ] Legal and privacy review complete
- [ ] Support team trained
- [ ] Docs and help center updated

### Post-Launch Success (30 days)
- Notification volume reduced 60-70%
- Click-through rate increased to 25%+
- Support tickets about missed updates down 50%
- User NPS maintained or improved (>42)
- 60%+ of users have smart notifications enabled
- <5% of users disable feature

### Long-Term Success (90 days)
- DAU increased 15%
- User engagement (sessions per day) up 25%
- NPS improvement to 50+
- Feature becomes top 3 in user satisfaction surveys

## Appendix

### Research & Resources
- User research synthesis: [Link to Notion doc]
- Competitive analysis: [Link to competitive landscape]
- ML model documentation: [Link to technical spec]
- Design system: [Link to Figma component library]

### Competitive Analysis
- **NotifyHub**: Launched similar feature in Nov 2025, 72% user adoption
- **SmartFlow**: Basic priority rules (manual only), no ML
- **Opportunity**: Neither has on-device ML or explanation features

### Related Documents
- Q1 2026 Roadmap: [Link]
- Notification service architecture: [Link]
- Privacy policy updates: [Link]

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**Document History**
- v1.0 - Jan 15, 2026 - Initial draft (Tom)
- v1.1 - Jan 18, 2026 - Added technical risks, updated timeline (Tom + Mike)
- v1.2 - Jan 20, 2026 - Final review comments incorporated (Sarah)

**Approvals**
- [x] Engineering: Mike Chen - Jan 20, 2026
- [x] Design: Jennifer Lee - Jan 20, 2026
- [x] Product: Sarah Johnson - Jan 20, 2026
- [ ] Security: Pending review - Due Jan 24, 2026
