Curated Content or Constrained Choices?
Unveiling Netflix's User Experience
Understanding how hyper-personalization influences user trust and satisfaction to design a more
transparent and ethical Netflix experience.
Ever wondered how Netflix knows what you want to watch?
Picture this: you open Netflix, and it greets you with a curated lineup that feels tailored just for you. With 80% of users following these recommendations, Netflix’s personalization is a key driver of engagement. But beneath the convenience lie deeper questions, How much does Netflix know about you? Are its suggestions transparent and accurate?
My Role
Responsible for secondary research and Analysis
Team
4 Designers
(Academic research)
How fixing Netflix’s Algorithm could make your binge nights better!
Retention Rate Improvement
Users expressed dissatisfaction with repetitive or irrelevant recommendations, leading to disengagement.
Business Impact: Improved retention rates by optimizing the recommendation system to provide diverse and accurate content. Addressing the "filter bubble" effect could encourage longer engagement and loyalty, especially among users likely to churn.
User Trust and Engagement
Lack of transparency in how recommendations are generated erodes trust and reduces user satisfaction.
Business Impact: Building trust through features like explainable AI (e.g., “Why this is recommended for you”) and user control fosters long-term engagement and customer advocacy.
Revenue Uplift via Content Discovery
Users missed opportunities to discover and engage with new or less-promoted content due to limited personalization options.
Business Impact: Increasing discoverability of diverse content leads to higher viewership of underutilized catalogue titles, improving ROI on content production and acquisition.
Well, not all users are happy about their recommendations...
Netflix’s personalised recommendations keep users engaged but often show similar content, don’t explain how suggestions are made, and give users little control. This can hurt trust and satisfaction over time.
(As personalisation and data-driven decision-making become increasingly prevalent, there is a growing need to examine how users perceive and interact with personalized content and recommendations.)
and why does it matter to Netflix and its users?
Solving these challenges is key to building user trust, keeping viewers coming back, and making the most of Netflix’s content investments. By offering diverse, relevant choices, Netflix can create a better user experience while staying ahead in the competitive streaming market.
From Binge-Watchers to Chill Seekers...
Age and Preference Diversity: Users come from various age groups and have different content preferences.
Discovery-Oriented: Enjoy finding new shows but also appreciate access to familiar favourites.
Everyday Netflix Subscribers: People who use Netflix to relax after work or during leisure time.
Desire for Balance: Seek a viewing experience that feels both curated and flexible, without feeling confined by the algorithm.
Shared Expectations: Prefer relevant and personalised recommendations. Value transparency and trust in suggestions.
During the survey and interviews, users expressed low awareness and mistrust regarding how Netflix algorithms operate. This led to prioritising transparency by introducing features like "Why this is recommended" explanations.
Mix-method Research
1
Thematic analysis revealed that users desired more control over their recommendations.
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They value diverse, fresh, and accurate recommendations that match their interests.
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A sense of control over data and recommendations enhances satisfaction.
Analysis
3
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Many users feel trapped in a "filter bubble," seeing repetitive or predictable suggestions.
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Users want to know how Netflix algorithms work and why specific recommendations are made.
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Some users are uneasy about the extent of data collection and the lack of clarity about how their data is used.
Insights
2
Analysis of user complaints about repetitive and predictable content led to the decision to enhance diversity in recommendations by tweaking algorithms to include less popular but relevant titles.
Addressing Content Repetition Through Algorithmic Tweaks
4
How Users Navigate Netflix’s Personalised Experience
How Netflix Thumbnails Shape What You Watch
"Netflix personalizes not just recommendations but also visuals, tailoring thumbnails to different users based on viewing history and engagement patterns. This image illustrates how the same show, Stranger Things, is presented with varied thumbnails to different users, influencing their likelihood of clicking.
Finally, my top learnings
Results..
Use this space to promote the business, its products or its services. Help people become familiar with the business and its offerings, creating a sense of connection and trust. Focus on what makes the business unique and how users can benefit from choosing it.
This project reinforced the importance of user-centered design by uncovering real frustrations Netflix users face with hyper-personalization. It showed me that while personalization keeps users engaged, lack of transparency and control can erode trust, a balance that’s critical in UX design.
I learned how to turn data into action, using insights from surveys and interviews to propose solutions that not only improve user experience but also drive business impact whether it’s boosting retention, enhancing engagement, or increasing content discovery.
Another key takeaway was the role of transparency in design. Users want to understand why they see certain recommendations, which highlights the need for explainable AI and ethical UX practices. This applies to any product that uses personalization, from e-commerce to fintech.
I also explored how visual design influences behavior, particularly through Netflix’s use of dynamic thumbnails. This reinforced the power of UI elements in shaping user decisions, making me more mindful of the intersection between interaction design and psychology.
Most importantly, this case study strengthened my ability to tell a compelling story with research connecting user pain points to solutions in a way that makes sense to both users and businesses.