Problem Space & Competitive Analysis
Background & Context
Choosing what to watch on Netflix can be overwhelming. Many users experience decision fatigue, spending more time scrolling than watching. This issue is most prominent among:
- Decision-Fatigued Viewers (55% impact) – Primary group affected, often abandoning the platform due to choice overload.
- Casual Viewers (25%) – Experience occasional frustration but engage more with simplified recommendations.
Competitive Analysis: How Netflix Differs from Existing Co-Watch Features
Platform
Co - Watching feature
Limitations
How Netflix AI improves it
Twitch
Live-streamed
Co-watching with chat
No personalized content matching, user-driven
AI-powered group matching for shared interests
Discoord
Screen sharing for group watching
Requires manual content selection, no content discovery
AI-driven watch rooms that reduce decision fatigue
Hulu Watch Party
Limited co-watching with friends
Restricted to select titles, no real -time recommendation
Netflix AI dynamically curates content for watch groups
Problem Statement: How might we enhance Netflix’s recommendation system to reduce decision fatigue and increase engagement by offering AI-powered group recommendations?
2. Goals & Secondary Goals
Primary Goals:
- Reduce decision fatigue by 25% (measured by reduction in scrolling time before watching).
- Increase engagement rates by 15% through group-based recommendations.
- Improve retention rates among Decision-Fatigued Viewers by 10%.
Secondary Goals:
- Facilitate social viewing experiences for users who enjoy co-watching.
- Enhance content discovery for niche audiences.
- Encourage passive content exploration through group dynamics.
3. Personas Affected & Impact
Key User Personas
Decision-Fatigued Viewer (55% impact)
- Pain Points: Too many choices, indecisiveness, wasted time scrolling.
- Needs: A seamless way to find something enjoyable quickly.
- AI Solution Impact: Reduces time spent choosing by offering curated group recommendations based on preferences and real-time popularity.
Casual Viewer (25% impact)
- Pain Points: Watches occasionally, struggles with content discovery.
- Needs: A way to effortlessly join trending or group-recommended content without thinking too much.
- AI Solution Impact: Encourages more engagement by suggesting easy-to-watch, socially-driven content.
Social Streamer (20% impact)
- Pain Points: Wants shared viewing experiences but struggles to coordinate with others.
- Needs: A way to watch with like-minded users in real time.
- AI Solution Impact: AI-generated watch rooms based on common interests enhance engagement.
4. Business & Customer Impact
Projected Business Impact:
Metric
Current
Projected
Expected Gain
Engagement Rate
72%
83%
+15 %
Rentention Improvement
Baseline
+10%
Lower churn
Revenue Growth
N/A
$50M annually
Based on user retention
Monetization Strategy:
- Ad Revenue Expansion: Sponsored watch rooms (e.g., brands promoting films in group sessions).
- Premium Tiers: Exclusive AI-powered recommendations or VIP watch rooms for Netflix Premium subscribers.
- Engagement-Based Upsells: Personalized content bundles based on AI recommendations.
5. Solution Overview & User Flow
AI-Powered Group Recommendations Workflow
How It Works:
- User opens Netflix → Receives AI-powered group-based recommendations.
- AI scans user behavior → Matches users with similar preferences.
- User joins a watch room → Content updates in real-time based on engagement.
- Post-viewing feedback collected → Refines future recommendations.
6. Functional Requirements & Prioritization
Feature
Description
Priority
Complexity Estimate
Projected cost
AI group Matching
Matches users by shared interests
P0
Medium
$500k
Real-time Adaptive suggestions
Updates recommendations during viewing
P0
High
$800k
Instant Watch Party Join
One-click entry into group sessions
P1
Medium
$400k
Social Context cues
Shows why a recommendation was made
P2
low
$200k
Total Estimated Cost: $1.9M
MVP Priority: AI Group Matching + Real-Time Adaptive Suggestions
7. Success Metrics & Goals
Primary KPI:
Reduce average scrolling time by 25%.
Additional KPIs:
Increase group recommendation participation by 15%.
Improve retention rates by 10% among Decision-Fatigued Viewers.
Measurement Plan:
A/B testing for AI-powered vs. standard recommendations.
Engagement tracking for AI-driven group selections.
- Milestones & Timelines
Milestone
Timeline
Exit Criteria
Prototype Development
Weeks 1 - 4
AI model for group matching built
Internal Testing
Weeks 5 - 6
Validate AI-generated recommendation
Beta Rollout
Weeks 7 - 8
Test engagement metrics with select users
Full Development
Week 9+
Feature launched to a wider audience
- AI Ethical Considerations & Privacy
Addressing Bias in AI Recommendations:
- Avoiding Content Silos: AI must balance new discoveries with user preferences to prevent repetitive suggestions.
- Inclusivity in Content Matching: Ensuring diverse representation to prevent algorithmic bias.
- User Control & Transparency: Users can opt-out of AI recommendations or adjust content filters.
Privacy & Data Protection:
- AI does not store personal viewing history beyond aggregated trends.
- Watch rooms use anonymous matching rather than direct profile links.
- Strict moderation policies to prevent abuse in public watch sessions.
10. Frequently Asked Questions (FAQ)
Q1: How does the AI ensure group recommendations are relevant?
The AI model analyzes watch history, engagement trends, and popular content to create dynamic recommendations.
Q2: Can users opt out of AI-powered group recommendations?
Yes! Users can disable AI-driven group suggestions and continue with standard Netflix recommendations.
Q3: Will this feature replace Netflix’s current recommendation system?
No, it enhances the current system by adding an optional social-viewing layer.
11. Conclusion
The AI-powered Group Recommendations feature aims to:
- Reduce decision fatigue by simplifying content discovery.
- Increase engagement & retention through AI-curated watch groups.
- Create a more dynamic Netflix experience that fosters interaction & discovery.
Link to User Experience Flowchart - https://miro.com/app/board/uXjVIbUaBLU=/
Learnings
Product Manager Learnings:
elizabeth ebenezer
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
&
As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
&
As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
&
Developer Learnings:
Maurquise Williams
&
- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
&
All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
&
I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.