AI Chatbot
Cut time‑to‑play with a context‑aware, on‑demand AI assistant
COMPANY
Netflix
ROLE
UX Researcher/Designer
EXPERTISE
UX/UI Design
YEAR
2025
Years after retiring from their formidable ninja lives, a dysfunctional family must return to shadowy missions to counteract a string of looming threats.
Play
i
More Info
Home
TV Shows
Movies
New & Popular
My List
Browse by Languages
Netflix Assistant
Popular on Netflix
Macbook Pro
Recently Added
Leaving Soon
TOP
10
Recently Added
TOP
10
TOP
10
Recently Added
New on Netflix
Recently Added
Leaving Soon
TOP
10
Recently Added
TOP
10
TOP
10
Recently Added
TOP
10
New Season
Continue Watching for Sunjay
Hello, I’m the Netflix Assistant Chatbot.
How may I help you today?
June 3 2025, 2:09 PM
Type a message...
Surprise me.
I just want to relax.
Find me something for the background.
Find me something short and easy
Find me a movie.
I’m in the mood to laugh.
Netflix Assistant
i
Macbook Pro
Project description
This project was to envision and reimagine what the Netflix experience looks like for users who are looking for what show they want to watch.
Background
I wanted to take something that users are already familiar with and see if I could add a creative spin to it. For this DesignLab capstone project, I wanted to take on a core experience at Netflix. For many users, Netflix has become overwhelming and often users do not know what to watch, which leads to decision paralysis. I wanted to see if I could add an AI chatbot to the Netflix web experience to see if it genuinely made sense as a proof of concept.
This project was part of Design Lab’s bootcamp curriculum. Working as the solo designer, I acted as both the UI/UX designer and UX researcher, and led every phase of the project.
Timeline
From explorations to final designs in 4 weeks while working with multiple projects at the same time
Tools Used
Figma, Figjam, Google Docs, Phantom AI
Problem
Netflix is one of the world's most utilized streaming services for entertainment. Over the years, it has grown exponentially. This has been both a blessing and a curse. With the large growth has come a large influx of content for the company. This has created a unique problem. There is simply too much content to consume and far too many streaming services out there. This creates decision fatigue for the user and burnout and peple end up leaving without ever picking a show to watch.
POV
I approached this project as someone overwhelmed by the watching experience and wants a low friction system that makes watching shows easier. I wanted to design an experience that felt intuitive, helpful, and frictionless.
Process
This category details the step-by-step approach taken during the project, including research, planning, design, development, testing, and optimization phases.
Research & Planning
Conducted market research & user interviews to identify existing user viewing challenges and user preferences.
Define & Design
Synthesized the research and began defining the problems that the interviews and research presented. Defined user persona, outlined key features based on user needs and market trends, and created user and task flows.
Ideation & Implementation
With all of the knowledge, I iterated and built the visual design and brand. I built the logo, component library, and started building & testing low-fidelity wireframes.
Testing & Optimization
Conducted rigorous user testing across various devices and platforms to ensure compatibility and performance. Had the user go through the profile flow, and share feedback. Gathered user feedback and iteratively optimized the app based on usability testing, metrics, and user satisfaction.
Research & Planning
Since I was focused on creating a chatbot, I wanted to understand what some of the biggest pain points were for users. I wanted to learn how users interacted with Netflix so that I could build a system that complimented how users used the app.
For this project, I focused on these main questions:
(1) How do users interact with the Netflix home page?
(2) What are user’s main intentions when they use Netflix?
(3) What about the Netflix watching experience feels overwhelming?
Methodology
In order to learn more about users, I thought through different research methods such as user interviews, customer surveys, and contextual inquiries. For the purposes of this exercise, I went with user interviews.
I categorized and conducted five different user interviews, conducted secondary research on the different competitors in the space, and synthesized information to understand why and where a chatbot would be useful and I built this experience.
Key Insights from
User Research
Users want personalized and convenient streaming experiences, better transparency and variety in content discovery, and a chatbot that provides truly contextual and adaptive recommendations rather than just search support.
1. Personalized & Convenient Streaming
Viewers often choose what to watch based on mood, time of day, or ease of access. They appreciate personalized playlists and cross-device continuity but can feel overwhelmed by too many options or difficulty in finding the right content.
2. Transparency & Variety in Content Discovery
While Netflix’s algorithm plays a role in discovery, many users rely on friends, social media, and trending lists. They want to understand why something is recommended and feel Netflix should do more to highlight niche or hidden content that otherwise goes unnoticed.
3. Chatbot as an Adaptive Companion
There is interest in a chatbot, but users are skeptical unless it provides value beyond the current UI. The most appealing use cases involve personalized, context-aware recommendations (e.g., based on mood, group vs. solo viewing) and simplifying discovery when users “don’t know what to watch.”
Competitive Analysis
Did a competitive analysis on major players in this space to learn more about their strengths, weaknesses, opportunities, and threats.
Through my research, I saw that people loved Netflix’s personalization but often felt overwhelmed by too many choices. They trusted friends more than algorithms, and they were curious about a chatbot — but only if it could feel truly helpful. From these insights, I narrowed in on the core problems: decision fatigue, lack of transparency in recommendations, and skepticism about chatbot value.
Define & Design
From my user interviews, we validated the desire and use of a Netflix chatbot. The users expressed how surprised they were by the fact that there wasn’t already a feature like this available. They also expressed that the chatbot would do well if it could go beyond basic search, offering adaptive, context-aware suggestions that make discovery easier and more engaging.
Problem Statement: How might we build a chatbot that can help users confidently discover content that they want to watch without feeling too overwhelmed by the amount of content offered?
User Persona
I created two personas by the end of my research. The personas were based on the differing behavior patterns that I noticed when I was interviewing users. I classified the users like Mathias into active users and users like Danny into passive users. Active users would look for shows and would scroll through content or engage with genres to find shows. Passive users would have a preconceived notion of what show they wanted to watch but would watch more passively or based on their mood or activity level preference.
Affinity Map
To bridge the gap between research and design, I used an affinity map to group interview insights into four key areas. The main purpose of this affinity map was to analyze user preferences, challenges, and opportunities around Netflix streaming, in order to inform product improvements—especially for content discovery and chatbot experiences. It helped me consolidate qualitative research into clear themes that can guide design and strategy decisions. These themes include user’s streaming preferences were when they logged into Netflix, how users found new movies and shows, what would they want from a chatbot, and finally what would enhance their Netflix experience.
User Flows
I created a series of user flows to visualize how a user might navigate Netflix's core watching experience. These flows illustrate two different ways users can engage with a chatbot while trying to find a show on Netflix—one triggered through scrolling and the other through search.
In the scroll flow, the user starts on the Netflix home page and scrolls to find a show. If they scroll for more than 30 seconds without success, the chatbot is automatically triggered, offering help. The user can then continue interacting with the chatbot until a suitable show is found, at which point they are directed to the show page. This design helps reduce frustration from excessive browsing by providing timely assistance.
In the search flow, the user begins on the home page and initiates a search. They can either directly type in a query or choose to click on the chatbot for assistance. Once inside the chatbot, the user looks for a show and, if they haven’t found it yet, continues the conversation until the chatbot provides a relevant recommendation. As with the scroll flow, the end goal is reaching the show page. This path gives users more control, offering chatbot support as an optional enhancement to search rather than an automated trigger.
Overall, both flows aim to make content discovery smoother and less overwhelming, with the chatbot acting as either a supportive intervention (when scrolling) or an on-demand assistant (when searching).
From there, I refined and move into branding and wireframing. I wanted to make sure that I had the strongest ideas to turn into prototypes. I focused on creating lightweight interactions—like mood buttons and quick shortcuts—that felt easy to use while still addressing trust through transparency. These designs aimed to show how a chatbot could move beyond a novelty and actually become a meaningful part of the Netflix experience. This project taught me how important it is to bridge delight with trust, and that even small interaction details can make or break user confidence—insights I carried into my final design
Ideation & Implementation
For this project, I took an already well established design system and added to it with the chatbot.
Netflix’s branding is rooted in simplicity, boldness, and cinematic immersion, which creates a strong foundation for its product experience. The iconic red-on-black palette is instantly recognizable and evokes the feeling of a theater, setting the stage for content to take the spotlight. Visuals are prioritized over text, with high-quality artwork and motion previews designed to spark curiosity and engagement. This brand consistency is carried seamlessly across devices, reinforcing trust and familiarity whether users are on mobile, web, or TV.
Branding
From a UX perspective, Netflix’s interface is a study in reducing friction while maximizing discovery. The design relies on large, visually driven cards, horizontal carousels, and auto-playing previews that encourage quick sampling and exploration. Core actions like “Play” and “Add to My List” are surfaced prominently to minimize decision fatigue, while personalization ensures each user’s experience feels uniquely tailored. Rows adapt dynamically to behavior, and trending sections tap into social relevance, blending individual preference with cultural moments. Overall, the UI balances efficiency, personalization, and visual storytelling, creating an experience that feels both intuitive and deeply engaging.
Low Fidelity Wire Frames
The low-fidelity wireframes focus on Netflix's flow with the user on the home page. After I did some user research, it was clear that the main flow that I wanted to focus on was the home page experience with the chatbot.
I focused on building two different experiences for the chatbot in the low-fidelity wireframes,. I created a mockup that had the chatbot near the search navigation, as well as, a chatbot as a standalone feature that would move with the page. I wanted to gain an understanding of what I could create with as little friction as possible for the user.
Netflix UI Kit
This is the design system I used that incorporated elements for the UI & component library
Netflix home page low fidelity wireframes
With my low fidelity file, I wanted to assess whether a top button or a floating button was more appealing for users for the chatbot
Testing & Optimiziation
After completing the low-fidelity wireframes, we conducted usability testing to see if the flow made sense and to gain better understanding of layout, navigation, and overall experience of the chatbot.
The usability test of the Netflix Assistant Chatbot showed that users generally appreciated its speed, lightweight UI, and mood-based shortcuts, but trust and control were recurring concerns. Mathias and Ashley valued the mood prompts yet struggled with trust in recommendations and restarting the flow. Danny liked the clean UI and typing flexibility but was frustrated by the lack of a clear “refresh/try again” option and occasional misunderstandings. Omar found the chatbot fun and exploratory, though he noted the absence of visible personalization (e.g., seen vs. unseen content). Shahim, focused on efficiency, completed his task fastest and wanted an even more seamless handoff to playback. Overall, the chatbot reduced browsing friction and offered engaging shortcuts, but users wanted clearer restart options, transparency in recommendations, and stronger signals of personalization.
Key improvements include tightening the responses for the chatbot, and adding a refresh button for the show that the chatbot recommended, so that the user felt like they had more control over their search.
Solution
My goal for this project was to design an AI chatbot that was an intuitive companion for the Netflix user faced with the paralysis of decision fatigue. The chatbot takes prompts from the user that is either predetermined or inputted and helps the user pick a show to watch.
My aim was to showcase how a chatbot could help users navigate the analysis paralysis of picking a show by showcasing the ability to recommend a show based on previous watch history. I did so by showing how chat could live on the home page.
How Might We (HMW):
1. Help Netflix users quickly and confidently discover content that matches their mood and context, especially when they feel overwhelmed by too many choices?
2. Create a more emotionally intelligent experience that understands and responds to user indecision/
Prototype
The final prototype delivered a sleek, and refined experience that supported users with intuitive steps, and simplified the process to find a show. I hope this showcased that while a chatbot may be difficult for Netflix to implement, it is something that I believe would provide immense value to the users.
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Macbook Pro
Intelligent prompting after time spent in app
The chatbot will prompt you automatically after a certain amount of rows scrolled or time spent in app
Macbook Pro
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Macbook Pro
Intelligent prompting after time spent in app
The chatbot will prompt you automatically after a certain amount of rows scrolled or time spent in app
Macbook Pro
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Macbook Pro
Intelligent prompting after time spent in app
The chatbot will prompt you automatically after a certain amount of rows scrolled or time spent in app
Macbook Pro
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Macbook Pro
Intelligent prompting after time spent in app
The chatbot will prompt you automatically after a certain amount of rows scrolled or time spent in app
Macbook Pro
Reflection
Here, the challenges, lessons, and next steps of the project are highlighted.
Challenges Faced
My main problem was helping users navigate through the overwhelming amount of content on Netflix and pick a show. This posed its series of challenges. The main challenges I encountered was figuring out where the chatbot would live within the UI and how thorough I should make the fidelity mockups. Since Netflix is massive and their database is vast, I knew designing for the entire interface was going to be an impossible task, so narrowing down the user flows and task flows to something feasible took some effort.I think time constraints made it difficult to really flesh out other experiences for the chatbot. There could be hundreds if not thousands of use cases but I had to think of something that would showcase the efficacy of the bot without simplifying it too much.
Learnings
This project has taught me to really explore design systems more thoroughly as I was working to build a chatbot inside of Netflix’s already well established UI and UX systems. I also noticed that I was able to work through to stage and build wireframes much more quickly this time around. I enjoyed using my imagination to understand what a chatbot could look like for Netflix. This project felt very open to express creativity and it was fun to build content and validate it against users.
Next Steps
I would focus on building out more of the chatbot. I think figuring out different use cases for it would be something that would be fun to learn about. Working on thinking about how to incorporate this into multi devices is something that I was also thinking about. This experience is mostly web but the mobile and TV experiences are also important and would pose their own set of unique challenges.
Thank you for reading!
Want to talk about Netflix or other projects? Lets chat!
View Alaska Airlines
Case Study
AI Chatbot
Cut time‑to‑play with a context‑aware, on‑demand AI assistant
Years after retiring from their formidable ninja lives, a dysfunctional family must return to shadowy missions to counteract a string of looming threats.
Play
i
More Info
Home
TV Shows
Movies
New & Popular
My List
Browse by Languages
Netflix Assistant
Popular on Netflix
Macbook Pro
Recently Added
Recently Added
Leaving Soon
Leaving Soon
TOP
10
Recently Added
TOP
10
Recently Added
TOP
10
TOP
10
TOP
10
Recently Added
TOP
10
Recently Added
New on Netflix
Recently Added
Recently Added
Leaving Soon
Leaving Soon
TOP
10
Recently Added
TOP
10
Recently Added
TOP
10
TOP
10
TOP
10
Recently Added
TOP
10
Recently Added
TOP
10
New Season
TOP
10
New Season
Continue Watching for Sunjay
Hello, I’m the Netflix Assistant Chatbot.
How may I help you today?
June 3 2025, 2:09 PM
Type a message...
Surprise me.
I just want to relax.
Find me something for the background.
Find me something short and easy
Find me a movie.
I’m in the mood to laugh.
Netflix Assistant
i
Macbook Pro
Project description
This project was to envision and reimagine what the Netflix experience looks like for users who are looking for what show they want to watch.
Background
I wanted to take something that users are already familiar with and see if I could add a creative spin to it. For this DesignLab capstone project, I wanted to take on a core experience at Netflix. For many users, Netflix has become overwhelming and often users do not know what to watch, which leads to decision paralysis. I wanted to see if I could add an AI chatbot to the Netflix web experience to see if it genuinely made sense as a proof of concept.
This project was part of Design Lab’s bootcamp curriculum. Working as the solo designer, I acted as both the UI/UX designer and UX researcher, and led every phase of the project:
User interviews and affinity mapping
Persona development
UX flows and journey mapping
Wireframing and prototyping
UI design and interaction states
Usability testing and iteration
Visual branding and accessibility
Timeline
From explorations to final designs in 4 weeks while working with multiple projects at the same time
Tools Used
Figma, Figjam, Google Docs, Phantom AI
Problem
Netflix is one of the world's most utilized streaming services for entertainment. Over the years, it has grown exponentially. This has been both a blessing and a curse. With the large growth has come a large influx of content for the company. This has created a unique problem. There is simply too much content to consume and far too many streaming services out there. This creates decision fatigue for the user and burnout and peple end up leaving without ever picking a show to watch.
POV
I approached this project as someone overwhelmed by the watching experience and wants a low friction system that makes watching shows easier. I wanted to design an experience that felt intuitive, helpful, and frictionless.
Process
This category details the step-by-step approach taken during the project, including research, planning, design, development, testing, and optimization phases.
Research & Planning
Conducted market research & user interviews to identify existing dating app challenges and user preferences.
Define & Design
Synthesized the research and began defining the problems that the interviews and research presented. Defined user persona, outlined key features based on user needs and market trends, and created user and task flows.
Ideation & Implementation
With all of the knowledge, I iterated and built the visual design and brand. I built the logo, component library, and started building & testing low-fidelity wireframes.
Testing & Optimization
Conducted rigorous user testing across various devices and platforms to ensure compatibility and performance. Had the user go through the profile flow, and share feedback. Gathered user feedback and iteratively optimized the app based on usability testing, metrics, and user satisfaction.
Research & Planning
Since I was focused on creating a chatbot, I wanted to understand what some of the biggest pain points were for users. I wanted to learn how users interacted with Netflix so that I could build a system that complimented how users used the app.
For this project, I focused on these main questions:
(1) How do users interact with the Netflix home page?
(2) What are user’s main intentions when they use Netflix?
(3) What about the Netflix watching experience feels overwhelming?
Methodology
In order to learn more about users, I thought through different research methods such as user interviews, customer surveys, and contextual inquiries. For the purposes of this exercise, I went with user interviews.
I categorized and conducted five different user interviews, conducted secondary research on the different competitors in the space, and synthesized information to understand why and where a chatbot would be useful and I built this experience.
Key Insights from
User Research
Users want personalized and convenient streaming experiences, better transparency and variety in content discovery, and a chatbot that provides truly contextual and adaptive recommendations rather than just search support.
1. Personalized & Convenient Streaming
Viewers often choose what to watch based on mood, time of day, or ease of access. They appreciate personalized playlists and cross-device continuity but can feel overwhelmed by too many options or difficulty in finding the right content.
2. Transparency & Variety in Content Discovery
While Netflix’s algorithm plays a role in discovery, many users rely on friends, social media, and trending lists. They want to understand why something is recommended and feel Netflix should do more to highlight niche or hidden content that otherwise goes unnoticed.
3. Chatbot as an Adaptive Companion
There is interest in a chatbot, but users are skeptical unless it provides value beyond the current UI. The most appealing use cases involve personalized, context-aware recommendations (e.g., based on mood, group vs. solo viewing) and simplifying discovery when users “don’t know what to watch.”
Competitive Analysis
Did a competitive analysis on major players in this space to learn more about their strengths, weaknesses, opportunities, and threats.
Through my research, I saw that people loved Netflix’s personalization but often felt overwhelmed by too many choices. They trusted friends more than algorithms, and they were curious about a chatbot — but only if it could feel truly helpful. From these insights, I narrowed in on the core problems: decision fatigue, lack of transparency in recommendations, and skepticism about chatbot value.
Define & Design
From my user interviews, we validated the desire and use of a Netflix chatbot. The users expressed how surprised they were by the fact that there wasn’t already a feature like this available. They also expressed that the chatbot would do well if it could go beyond basic search, offering adaptive, context-aware suggestions that make discovery easier and more engaging.
Problem Statement: How might we build a chatbot that can help users confidently discover content that they want to watch without feeling too overwhelmed by the amount of content offered?
User Persona
I created two personas by the end of my research. The personas were based on the differing behavior patterns that I noticed when I was interviewing users. I classified the users like Mathias into active users and users like Danny into passive users. Active users would look for shows and would scroll through content or engage with genres to find shows. Passive users would have a preconceived notion of what show they wanted to watch but would watch more passively or based on their mood or activity level preference.
Affinity Map
To bridge the gap between research and design, I used an affinity map to group interview insights into four key areas. The main purpose of this affinity map was to analyze user preferences, challenges, and opportunities around Netflix streaming, in order to inform product improvements—especially for content discovery and chatbot experiences. It helped me consolidate qualitative research into clear themes that can guide design and strategy decisions. These themes include user’s streaming preferences were when they logged into Netflix, how users found new movies and shows, what would they want from a chatbot, and finally what would enhance their Netflix experience.
User & Task Flows
I created a series of user flows to visualize how a user might navigate Netflix's core watching experience. These flows illustrate two different ways users can engage with a chatbot while trying to find a show on Netflix—one triggered through scrolling and the other through search.
In the scroll flow, the user starts on the Netflix home page and scrolls to find a show. If they scroll for more than 30 seconds without success, the chatbot is automatically triggered, offering help. The user can then continue interacting with the chatbot until a suitable show is found, at which point they are directed to the show page. This design helps reduce frustration from excessive browsing by providing timely assistance.
In the search flow, the user begins on the home page and initiates a search. They can either directly type in a query or choose to click on the chatbot for assistance. Once inside the chatbot, the user looks for a show and, if they haven’t found it yet, continues the conversation until the chatbot provides a relevant recommendation. As with the scroll flow, the end goal is reaching the show page. This path gives users more control, offering chatbot support as an optional enhancement to search rather than an automated trigger.
Overall, both flows aim to make content discovery smoother and less overwhelming, with the chatbot acting as either a supportive intervention (when scrolling) or an on-demand assistant (when searching).
From there, I refined and move into branding and wireframing. I wanted to make sure that I had the strongest ideas to turn into prototypes. I focused on creating lightweight interactions—like mood buttons and quick shortcuts—that felt easy to use while still addressing trust through transparency. These designs aimed to show how a chatbot could move beyond a novelty and actually become a meaningful part of the Netflix experience. This project taught me how important it is to bridge delight with trust, and that even small interaction details can make or break user confidence—insights I carried into my final design
Ideation & Implementation
For this project, I took an already well established design system and added to it with the chatbot.
Netflix’s branding is rooted in simplicity, boldness, and cinematic immersion, which creates a strong foundation for its product experience. The iconic red-on-black palette is instantly recognizable and evokes the feeling of a theater, setting the stage for content to take the spotlight. Visuals are prioritized over text, with high-quality artwork and motion previews designed to spark curiosity and engagement. This brand consistency is carried seamlessly across devices, reinforcing trust and familiarity whether users are on mobile, web, or TV.
Branding
From a UX perspective, Netflix’s interface is a study in reducing friction while maximizing discovery. The design relies on large, visually driven cards, horizontal carousels, and auto-playing previews that encourage quick sampling and exploration. Core actions like “Play” and “Add to My List” are surfaced prominently to minimize decision fatigue, while personalization ensures each user’s experience feels uniquely tailored. Rows adapt dynamically to behavior, and trending sections tap into social relevance, blending individual preference with cultural moments. Overall, the UI balances efficiency, personalization, and visual storytelling, creating an experience that feels both intuitive and deeply engaging.
Low Fidelity Wire Frames
The low-fidelity wireframes focus on Netflix's flow with the user on the home page. After I did some user research, it was clear that the main flow that I wanted to focus on was the home page experience with the chatbot.
I focused on building two different experiences for the chatbot in the low-fidelity wireframes,. I created a mockup that had the chatbot near the search navigation, as well as, a chatbot as a standalone feature that would move with the page. I wanted to gain an understanding of what I could create with as little friction as possible for the user.
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Netflix home page low fidelity wireframes
With my low fidelity file, I wanted to assess whether a top button or a floating button was more appealing for users for the chatbot
Testing & Optimiziation
After completing the low-fidelity wireframes, we conducted usability testing to see if the flow made sense and to gain better understanding of layout, navigation, and overall experience of the chatbot.
The usability test of the Netflix Assistant Chatbot showed that users generally appreciated its speed, lightweight UI, and mood-based shortcuts, but trust and control were recurring concerns. Mathias and Ashley valued the mood prompts yet struggled with trust in recommendations and restarting the flow. Danny liked the clean UI and typing flexibility but was frustrated by the lack of a clear “refresh/try again” option and occasional misunderstandings. Omar found the chatbot fun and exploratory, though he noted the absence of visible personalization (e.g., seen vs. unseen content). Shahim, focused on efficiency, completed his task fastest and wanted an even more seamless handoff to playback. Overall, the chatbot reduced browsing friction and offered engaging shortcuts, but users wanted clearer restart options, transparency in recommendations, and stronger signals of personalization.
Key improvements include tightening the responses for the chatbot, and adding a refresh button for the show that the chatbot recommended, so that the user felt like they had more control over their search.
Solution
My goal for this project was to design an AI chatbot that was an intuitive companion for the Netflix user faced with the paralysis of decision fatigue. The chatbot takes prompts from the user that is either predetermined or inputted and helps the user pick a show to watch.
My aim was to showcase how a chatbot could help users navigate the analysis paralysis of picking a show by showcasing the ability to recommend a show based on previous watch history. I did so by showing how chat could live on the home page.
How Might We (HMW):
1. Help Netflix users quickly and confidently discover content that matches their mood and context, especially when they feel overwhelmed by too many choices?
2. Create a more emotionally intelligent experience that understands and responds to user indecision/
Prototype
The final prototype delivered a sleek, and refined experience that supported users with intuitive steps, and simplified the process to find a show. I hope this showcased that while a chatbot may be difficult for Netflix to implement, it is something that I believe would provide immense value to the users.
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Macbook Pro
Intelligent prompting after time spent in app
The chatbot will prompt you automatically after a certain amount of rows scrolled or time spent in app
Macbook Pro
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Macbook Pro
Intelligent prompting after time spent in app
The chatbot will prompt you automatically after a certain amount of rows scrolled or time spent in app
Macbook Pro
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Macbook Pro
Intelligent prompting after time spent in app
The chatbot will prompt you automatically after a certain amount of rows scrolled or time spent in app
Macbook Pro
Intuitive Chatbot From home screen
Make your Netflix search experience more seamless and intuitive with an AI chatbot assistant
Macbook Pro
Intelligent prompting after time spent in app
The chatbot will prompt you automatically after a certain amount of rows scrolled or time spent in app
Macbook Pro
Reflection
Here, the challenges, lessons, and next steps of the project are highlighted.
Challenges Faced
My main problem was helping users navigate through the overwhelming amount of content on Netflix and pick a show. This posed its series of challenges. The main challenges I encountered was figuring out where the chatbot would live within the UI and how thorough I should make the fidelity mockups. Since Netflix is massive and their database is vast, I knew designing for the entire interface was going to be an impossible task, so narrowing down the user flows and task flows to something feasible took some effort.I think time constraints made it difficult to really flesh out other experiences for the chatbot. There could be hundreds if not thousands of use cases but I had to think of something that would showcase the efficacy of the bot without simplifying it too much.
Learnings
This project has taught me to really explore design systems more thoroughly as I was working to build a chatbot inside of Netflix’s already well established UI and UX systems. I also noticed that I was able to work through to stage and build wireframes much more quickly this time around. I enjoyed using my imagination to understand what a chatbot could look like for Netflix. This project felt very open to express creativity and it was fun to build content and validate it against users.
Next Steps
I would focus on building out more of the chatbot. I think figuring out different use cases for it would be something that would be fun to learn about. Working on thinking about how to incorporate this into multi devices is something that I was also thinking about. This experience is mostly web but the mobile and TV experiences are also important and would pose their own set of unique challenges.
Thank you for reading!
Want to talk about Netflix or other projects? Lets chat!