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.

  1. 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

  1. 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

  1. 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.
  2. 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.
  3. 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

&

  1. 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.
  2. 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.
  3. 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.

Full Team Learning