Flow By Netflix: Swipeable Discovery Feed Design
Manish Tomar
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Posted Jul 25, 2025
Designed a swipeable discovery feed for Netflix to reduce content discovery time.
Flow By Netflix: How I Designed a Solution to Humanity’s Greatest Modern Dilemma
12 min read
·
4 days ago
A Product Design/UX Case Study by Manish Tomar
You know that special kind of hell where you spend 45 minutes scrolling through Streaming platform, watching trailers for shows you’ll never watch, only to end up rewatching The Office for the 847th time? not 847th but Yeah, Haha we fixed that.”
Alright, alright, settle down folks!
Meet Manish Tomar your friendly neighborhood Product Designer from India. (Yes, we have Wi-Fi. No, I don’t ride elephants to work.)
I’m the guy who thinks a perfectly rounded button corner is worth losing sleep over. For me, design is about making your digital life suck just a little bit less.
This case study? Pure, unfiltered design work no shortcuts, no Dribbble copy-paste jobs. Just me, way too much coffee, and enough user research to make my introvert heart cry..
OKEY! Buckle up!
The Origin Story (The Story of How did i get this idea)
Picture this: It’s 11 PM on a Friday night. I’ve just finished watching Interstellar for what might be the 14th time (don’t judge me, Cooper’s cornfield chase scene hits different every time). My brain is floating somewhere in the fifth dimension, experiencing what scientists call “post-Nolan syndrome” that feeling when you need something equally mind-bending but have absolutely no clue where to find it.
So I did what any rational human would do: I started exploring to find something exactly like this mind-bending, space, time travel, Matthew McConaughey saving humanity vibes. You know the drill.
But here’s where things went sideways. What followed was a digital odyssey that would make Odysseus pack up and go home:
Prime Video → Algorithm suggested rom-coms (seriously, Amazon?)
JioHotsar→ Marvel movies (close, but no cosmic cigar)
YouTube → 7 trailers watched, brain more confused
Reddit → 3 threads deep into r/MovieSuggestions
Back to Prime → Still lost in the void
IMDB → 12 reviews read, 15 more questions born
9 browser tabs open → My laptop started heating up like a spaceship entering Earth’s atmosphere
Final result? I rewatched Interstellar. Again.
Fast forward to last month: I discovered Fromville — absolutely mind-boggling! My brain was doing cartwheels. Naturally, I needed MORE of this psychological horror goodness. So began The Great Web Series Hunt of 2024™:
50+ trailers watched. Countless reviews consumed. Multiple “Top 10 Mind-Bending Series” lists explored. My YouTube algorithm is now convinced I have commitment issues.
End result? Complete analysis paralysis. I was more confused than the monsters in Fromville trying to understand human logic.
And that’s when it hit me harder than a black hole’s gravitational pull: The problem isn’t what’s available. It’s how we find it.
NOTE- This is just my personal view and will not influence the design process. I’m aware of how to avoid personal biases, and I’ll only begin designing after completing thorough user research and analysis. OKEY! Let’s start the work”
Project Details
Duration: 5–6 Week (Self-initiated project)
Role: Solo UI Designer, UX Researcher, Part-time Therapist for Overwhelmed Streamers
Tools: Figma, FigJam, Notion, Google Forms, Miro, Claude, Excessive amounts of coffee
Executive Summary
The Problem: Users spend 15–25 minutes per session deciding what to watch, often abandoning streaming platforms due to choice overload and irrelevant recommendations. (That’s longer than most people’s attention spans these days!)
The Solution: Flow — a swipeable discovery feed designed specifically for movies and web series. Quick clips, trailers, and exclusive content served in bite-sized previews. No more hunting through endless grids or reading reviews. Just swipe, discover, watch. Think instant gratification meets smart curation.
Impact: Designed to reduce content discovery time by 70% through instant, swipeable content previews and intelligent recommendations. Because life’s too short to spend scrolling through “Because you watched The Office” for the 87th time.
Problem Statement
“How might we help streaming users quickly discover content that matches their current mood, Genre and available time, reducing decision fatigue and increasing viewing satisfaction?”
Or in plain English: “How do we stop people from spending their entire evening deciding what to watch instead of actually watching it?”
Business Goals
Reduce time-to-content consumption (because time is money, and money is… well, also money)
Increase user engagement and retention
Create a differentiated content discovery experience.
Success Metrics
Primary: Reduce average decision time from 20 minutes to 5 minutes
Secondary: Increase content completion rate by 40% (no more “started 47 series, finished 2”)
Tertiary: Achieve 85% user satisfaction score for recommendations (beat the algorithm at its own game)
Desk Research: User Pain Points & Platform Challenges
I explored Platforms : Reddit (r/Netflix, r/blankies, r/streaming), X/Twitter threads, Quora Q&A discussions, Medium posts, Fortune and NY Post reader surveys, industry reports (Nielsen, TiVo).
User’s Perspective
Endless browsing
According to a Fortune survey of 2,000 U.S. streaming subscribers, the average person spends 110 hours per year scrolling roughly 18 minutes per session — before hitting “play” FortuneNew York Post
.Axios (via Nielsen’s MediaTech Trender) finds that U.S. adults still spend just over 7 minutes per session deciding what to watchMany complain about algorithmic fatigue — seeing the same recommendations over and over.
Algorithmic fatigue
— 51% of viewers report feeling bombarded by repetitive recommendations — “I keep seeing the same four thumbnails over and over” New York Post.
On Reddit’s r/blankies, users lament that “the algorithm is basically that friend who only ever suggests the same restaurant”
Fragmentation & switching
61% of viewers switch between more than one app during a typical session — “my favorite show’s on the one service I don’t subscribe to” Yahoo Finance.
Quora threads consistently point out how exclusive licensing forces users to hop across three or four subscriptions just to follow a single franchise
Choice overload
Despite thousands of titles, 20% of users say it’s harder to pick something to watch today than it was a decade ago New York Post.
Common refrain on X/Twitter: “Scrolling Netflix feels like the digital equivalent of staring into a full fridge and still declaring ‘there’s nothing to eat.’”
Platform’s Perspective
Comfort re‑watches dominate
A recent Nielsen write‑up notes that 60% of streaming time in May 2023 went to shows that first aired on linear TV — viewers gravitate toward “old friends” rather than new releases Music Ally.
Discovery bottlenecks
Medium analysis (“How Discoverability is Actually the Reason that Streaming Is Failing”) argues that poor curation leaves promising originals buried under algorithmic noise Medium.
Platform‑team Slack threads (leaked to user‑review blogs) reveal constant A/B tests on rows, filters, and “skip intro” prompts to reduce browsing time — yet with only marginal gains.
Retention vs. exploration tension
On Quora, product‑managers admit their recommendation engines are optimized for watchtime (i.e. re‑watches), not discovery — so novel or niche titles rarely surface.
Key Findings:
2. Competitive Analysis
Key Discovery Gap Identified: None of these platforms offer quick, swipeable content previews or mood-based instant discovery — they all rely on traditional grid browsing and text-heavy interfaces.
3. User Survey
Sample Size: 50+ streaming users (18–45 years)
Method: Online survey via Google Forms https://forms.gle/AiBDD61rdqUwTgEU6
Key Insights:
Lengthy Browsing — 50% browse 5–15 min and 33% over 15 min before playing — only 17% decide in under 5 min.
High Drop‑Off — 83% have closed a streaming app without watching because they couldn’t choose.
Top Frustrations — Too many options (67%), mood‑mismatched recommendations (50%), and repetitive suggestions (33%) top the list.
Preferred Filters — Users want picks based on their current mood (83%), time available (67%), and past favorites (67%) before genre/actor or “surprise me.”
External & Comfort Viewing — 100% turn to YouTube for recommendations; 67% fall back on re‑watches when nothing new stands out.
4. User Interviews
Participants: 4 users (ages 22–28)
Method: 10 -30-minute remote interviews via Calls (where I learned way too much about people’s viewing habits)
Pain Points Identified:
Choice Overload: “Too many options make me anxious”
Irrelevant Recommendations: “Algorithm doesn’t understand my mood” (Netflix thinks I’m bipolar based on my viewing history)
Time Pressure: “I waste my limited free time browsing” (The irony is not lost on me)
External Validation: “I check YouTube reviews before watching” (Trust issues with algorithms)
Repetitive Content: “Same suggestions appear everywhere” (Netflix’s “Because you watched” is basically algorithmic stalking)
Some Common Users Patterns:
User Personas
Primary Persona: Aayushi — The Mood-Driven Explorer
Demographics:
Age: 24
Occupation: MBA Student (Professional stress accumulator)
Location: Delhi, India
Income: ₹45,000/month
Tech Savviness: High (Can navigate 14 streaming apps simultaneously)
Behavioral Patterns:
Watches content 4–5 times per week (When she’s not having existential crises about her future)
Prefers binge-watching on weekends (12-hour Netflix marathons are her cardio)
Uses multiple streaming platforms (Subscription collector, like Pokémon cards but more expensive)
Checks social media for recommendations (Crowdsourced decision-making)
Watches trailers, then hunts for YouTube reviews and Reddit threads
Goals:
Find content that matches current emotional state (Therapeutic binge-watching)
Minimize decision time
Quick access to hyper-specific genres
Get curated trailers in one feed instead of hunting through sections
Discover hidden gems
Discover content with friends/Baby remotely (Pandemic-era socializing)
Frustrations:
Spends too much time browsing (Analysis paralysis is real)
Repetitive algorithm suggestions (Netflix’s memory is both too good and too bad)
No emotional context in recommendations (Algorithms don’t understand feelings… yet)
Difficulty finding content for specific time slots (30-minute lunch break ≠ 3-hour movie)
Quote: “I want an app that understands whether I need to cry, laugh, or think tonight. Is that too much to ask from technology in 2025?”
Secondary Personas:
Rajesh — The Time-Conscious Professional
Age: 32, Software Engineer (Debugs code by day, debugging life by night)
Need: Quick content for 20–30 minute breaks (Efficiency is everything)
Frustration: Long discovery process wastes limited free time (“I spent longer choosing than watching”)
Priya — The Family Coordinator
Age: 28, Marketing Manager (Herding cats professionally and personally)
Need: Content suitable for family viewing (No awkward scenes during family movie night)
Frustration: Finding universally appealing content (“Something everyone can agree on? That’s not a movie, that’s a miracle”)
User Journey Mapping
Ideation & Solutions
Developing Solution:
Emotional Intelligence First: Understand user’s current emotional state (Be the therapist Netflix never was)
Clarity Over Choice: Curated options over endless scrolling (Less is more, paradox of choice is real)
Community-Driven: Leverage human insights alongside algorithms (Humans are still better at understanding humans)
Time-Conscious: Respect user’s limited free time (Time is the ultimate currency)
Transparent: Show reasoning behind recommendations (No more algorithmic black boxes)
Some Features Conceptualized
Mood + Genre Selection
Problem Solved: Generic recommendations don’t match your current vibe
Solution: Dual filtering system pick your mood AND preferred genre
2. Flow Feed (Swipeable Discovery)
Problem Solved: Browsing through endless grids is exhausting
Solution: TikTok-style feed with curated trailers and quick previews
User Value: Discover content effortlessly through intuitive swiping
3. Community-Driven Insights
Problem Solved: Algorithm recommendations lack human context
Solution: Real user comments and opinions integrated in the flow
User Value: Trusted, human-validated recommendations
MOODBOARD
Mood board? It’s where we throw colors, fonts, and images at a digital wall to see what “vibe” sticks. Think of it as Pinterest on espresso — a chaotic but crucial step to making your app look less like a spreadsheet and more like a party!
Information architecture
Mini User Flow
Low Fidelity Wireframes
These are the low-fidelity wireframes quick, raw, and straight from the brain to the page. I prefer skipping the fluff and jumping straight into structure and flow. Just enough to visualize the idea before turning it into a polished design.
Animation Glimpse During Wireframe
Here are some glimpses from the wireframing phase just a sneak peek of how the animations might look. And hey, don’t bother reading the text, it’s just placeholder stuff… wireframe vibes only!
Demo of animation
Left and Right Swipee
Design System: Components colors and Typo
Phycology behind this Bright Pink (#E00077):
Energy & Excitement: Bright pinks are often associated with high energy, enthusiasm, and vibrancy. They can be attention-grabbing and dynamic.
Passion & Love: Pink, in general, is linked to love, compassion, and playfulness. This particular shade leans towards a more intense, passionate energy rather than a soft, delicate one.
Modernity & Boldness: In design, vibrant pinks can convey a modern, cutting-edge, and bold aesthetic, especially when paired with neutrals.
Youthfulness & Fun: It can also evoke a sense of youthfulness, fun, and lightheartedness.
High Fidelity Final Design
Final Design…..
Reflection and Post thoughts
Key Learning:
Research Depth Matters: Comprehensive user research prevented major pivots later
Review Trust: Users value human recommendations over pure algorithmic suggestions
Translation: Algorithms are tools, humans are the craftsmen
Emotional Context: Adding emotional intelligence to recommendations significantly improves satisfaction
User Behaviour :
Decision Fatigue is Real: Users genuinely struggle with too many choices
Time Consciousness: Viewing time constraints are a major factor in content selection
Social Validation: Community features increase confidence in content choices
Translation: We’re all just looking for approval, even from strangers on the internet
Personal Growth:
Imposter Syndrome is a Liar: I actually know what I’m doing (sometimes)
User Feedback is Gold: Even negative feedback is valuable data
Design is Problem-Solving: Pretty pixels are nice, but solving real problems is better
Next Steps & Future Vision:
Community Hubs: Dedicated discussion threads for each film/series. Think fan theories, season speculations, and connecting with fellow enthusiasts.
Official Content Channels: Each show gets its own “social media” profile for exclusive updates, behind-the-scenes, and direct creator-fan interaction.
Monetized Engagement: Integrate strategic, contextual ads within these new community and official channels for a valuable new revenue stream.
What This Project Taught Me About Users:
Users are not broken: The current systems are
Users are not lazy: They’re efficient
Users are not stupid: They’re overwhelmed
Users are not picky: They know what they want, they just can’t find it
What This Project Taught Me About Myself
I can do research: Turns out I enjoy talking to people about their problems
I can handle criticism: User feedback is data, not personal attacks
I can iterate: First ideas are rarely best ideas
I can ship: Done is better than perfect (but good is better than done)
Contact & Portfolio
Manish Tomar
Product Designer, Problem Solver…
What I Do:
Turn user pain points into design solutions
Ask “why” a lot (annoyingly so)
What I Don’t Do:
Assume I know what users want
Take myself too seriously
Let’s Connect:
Portfolio: [ https://www.behance.net/aaravtomar2 ]
LinkedIn: [ https://www.linkedin.com/in/manish-tomar-869177244 ]
Email: [manishtomarux@gmail.com]
Twitter: [ https://www.linkedin.com/in/manish-tomar-869177244 ]
P.S. If you made it this far, you either:
Are considering hiring me (excellent choice)
Are procrastinating on your own case study (get back to work!)
Genuinely enjoyed my writing (we should be friendsss)
Thanku Guyzzzz…Good by.. See u all later!