A proposed solution that aims solve for user fatigue on Netflix. I do not represent Netflix in any way and the ideas & research are strictly my own.

1. DON’T SNOOZE YET!

3. 5 min.. 10 min.. 15 min.. You find yourself still scrolling but don’t hit play

4. Time’s Up!

5. We’ll talk about 5 things.
Understanding Netflix
Understanding user pain points
Proposing a solution
Measures of success & Launch
Way forward

6. Understanding Netflix

7. Mission
“Entertaining everyone & making the world smile.”
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

8. Company Shareholders care about?
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward
Subscribers!
MORE SUBSCRIBERS!
Huge Competitive
Threat

9. Loyal & Growing
Customer Base
Reed Hastings cares about?
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward
Content for everyone Accurate Recommendations &
Personalised Experience

10. Guiding Metrics
Discovery Time
Viewing Time
Drop-off Rate
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

11. Netflix
10%
Not
Netflix
90%
TV Viewership
Annual TV viewership: 1B Hours*
Number of TV households: 120 M*
Average TV viewing hours/day per HH: 8.33
Netflix users (2018): 66 M
Average Netflix viewing hours per
household per day: 1.65
In the US
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward
Interesting Facts To Note
Source: Netflix ’18 Q4 Earnings Call
We can estimate
*Source: Neilson Research

12. Interesting Facts To Note
6000+ titles on Netflix (in the US alone)
In 2018 alone Netflix added 700+ titles
It could take upto 3.88 years to watch everything on Netflix
(if watched 24/7 without breaks)
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

13. THERE IS STILL MORE THAN 6 HOURS WORTH OF
OPPORTUNITY PER HOUSEHOLD!
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

14. Who are the users?
On the Dimension of Time Spent Watching Netflix
The Daily The Hardly Evers
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward
The Weekenders

15. Understanding User Pain
Points

16. Let’s talk to some users.

17. Survey
Sample Set includes:
100 Respondents
US based
Age group: 21 - 40
Equal gender representation
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward
Aim: Validating if users experience fatigue on Netflix.
In person
interviews
Observing
users

18. Finding #1
User Behaviour:
Average Discovery Time (DT) for most users = 15 min+
0 0.15 0.3 0.45 0.6 0.75
Less than 15 min
10-15 min
30 min
More than 45+ min
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward
Note: Total avg. viewing hours on Netflix per user: 1.65 hours

19. User Behaviour (when they don't find anything to watch):
Most Switch to another platform / Don’t watch anything
Finding #2
0 0.1 0.2 0.3 0.4
Quit to another platform
Quit and don't watch anything
Message a friend for recommendations
Watch something from NF recommendations
]
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

20. Finding #3
User Behaviour Frequency (when they don't find anything to watch):
Most users said they switch/quit frequently
Never
3%
Quite
Frequently
80%
Sometimes
12%
Almost
Always
5%
Note: Quite Frequently defined as: 2 / 5 or 40% of the times
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

21. Finding #4
0 0.15 0.3 0.45 0.6
Friends
Recommendations
Netflix
Recommendations
Own Curated
Lists
Social Media, Blogs,
Online Reviews
Users Trust:
Recommendations from friends > Recommendations from Netflix
]
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

22. 0 0.1 0.2 0.3 0.4
Quit to another platform
Quit and don't watch anything
Message a friend for recommendations
Watch something from NF recommendations
Finding #5
User behaviour (when they don't find anything to watch):
Most don’t text a friend in the moment
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward
]

23. Summary of Company & User Research
More Titles +
Imperfect
Recommendations
Increase in
Discovery Time
User Fatigue
Drop
Off/Switch
• Netflix content is growing at a very fast pace.
• Users spend significant amount of total avg. viewing time on discovery.
• Netflix recommendations haven’t been perfect.
• Friends are better trusted source for recommendations.
Summary of the User’s Problem
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

24. My Conclusion
Since machine based recommendations are
great but not perfect, yet,
user fatigue is real.
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

25. Proposed Solution

26. Identifying the user fatigue point is crucial.
Adding trusted human aspect - at the points of fatigue
- to machine based recommendations can increase
viewership and decrease drop offs.
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

27. Meet Neil - The Explorer
Software Engineer, 32
Lives in Mountain View, CA
A Netflix Daily User
Loves watching & finding out about new shows &
movies
Also subscribes to Amazon Prime, Hulu, HBO
On weekends, he enjoys hanging out with friends
Pain Point:
“I watch anything and everything. But my Netflix feed has too much content
similar to what I watched but didn't like. My friends & I discuss interesting new
content all the time that I don’t see on my feed. A fresh set of unbiased
recommendations would be helpful at times when I’m just tired of scrolling.”
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

28. Introducing
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

29. Don’t Snooze Yet – A Netflix Original
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

30. How will it work?
2 Key Aspects:
• Identifying unique fatigue point for every user.
• Leverages on ‘The Social Effect’ at fatigue point.
(Facebook is used as an example here)
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

31. Unique Fatigue Point
Estimated per user based on machine learning algorithms
built on data models that aggregates and captures signals*
as:
• Drop off time: Amount of time a user spends looking for shows before abandoning
the platform without playing anything.
• Viewing time per session: How long did the user watch the show, and did he
interrupt to go back to browsing.
• Discovery time per session : Amount of time user spends to browse shows before
watching something.
• Content Watched : What did the user end up watching, something we
recommended, something random and new, or shows he’s watched already but
repeats frequently.
*Based on an assumption that Netflix already captures the above usage logs
Understanding Netflix User Pain Points Proposing A Solution Measuring success Way forward

32. Link to Feauture Demo
The Social Effect

33. Measuring Success

34. Measuring success
Discovery Time
Viewing Time
Drop-off Rate
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward
At Product Level

35. Fatigue Detection
Don’t Snooze Yet Screen
Social Media Plugin
Measuring success
At Feature Level
Activation Retention
My List
How many times does Don’t
Snooze Yet screen show up
for Neil per viewing hour?
Did Neil drop off before Don’t
Snooze Yet screen showed up?
How many times does Neil play
from Don’t Snooze Yet screen?
How many times does Neil play
from what a followed friend
watched?
Has Neil integrated his
social media to Netflix?
Does he add friends’ shows
to his own list?
Is he adding more
shows/movies to his lists? Is
adding categories?
Is Neil interacting with Don’t
Snooze Yet Screen?
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward

36. Launch
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward
Beta Test MVP
Phase 1: Internal Test with Netflix employees
Phase 2:
Users who have agreed to Participate in Tests.
US based
100k users (standard Netflix testing sample size)
Don’t Snooze Yet Screen on TV only
Social Plugin enabled across all devices

37. Share your Netflix
stories with your friends now!
Expect lots of
memes!
• Social Media
• Push Notifications
• In-app / Web notifications
• Blog
• Email
Go To Market
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward
Across all devices

38. Don’t Snooze Yet!
Share your Netflix stories now!
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward
3 of your friends are watching Narcos Mexico
• Social Media
• Push Notifications
• In-app / Web notifications
• Blog
• Email
Go To Market

39. See what your friends are watching now.
Add their shows to your list. And Watch Better.
See what your friends are watching now.
Create your own recommendations. Watch
better.
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward
• Social Media
• Push Notifications
• In-app / Web notifications
• Blog
• Email
Go To Market

40. Don’t Snooze Yet!
Share your Netflix stories now!
Adding recommendations from trusted friends to the Netflix
experience.
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward
• Social Media
• Push Notifications
• In-app / Web notifications
• Blog
• Email
Go To Market

41. Don’t Snooze Yet!
Share your Netflix stories now!
3 of your Friends Have Been Watching Narcos Mexico, Neil!
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward
• Social Media
• Push Notifications
• In-app / Web notifications
• Blog
• Email
Go To Market

42. Estimated Roadmap
Phase 1 (MVP): Don’t Snooze Yet
Product
Design/UX
Engineering
Sprint 1
Wireframe
Prototype:
1. DSY Screen
2. User Profiles, Social Plugin
3. My List Update
Sprint 2
Fatigue
Identification / Data
Model
Sprint 3
Back End/Server-side
Implementation
UI Development for all screens
Integration
Sprint 4
QA
Launch
User Testing; User Feedback
Coordination with
Marketing to prep for
announcement
Firefighting Across Teams
Test!
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward
Design Validation
Beta Testing

43. Way Forward

44. Phase 2
Main Feed
Further down the road: Short Term
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward

45. • Making fatigue point identification 100% accurate.
• Training data for better machine learning based
recommendations model.
• Capturing data on kind of content users prefer / don’t prefer to
watch.
Other Benefits
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward

46. Further down the road: Long Term
Phase 1 Phase 2 Phase 3
Don't Snooze Yet
Main Feed
Pattern Based
Recommendations
Neil likes watching Netflix when he is getting dinner. He usually
picks something out from his Dinner Time shows. Recommend
something from there to him on his main feed.
Right recommendations to the
right user at the right time.
Understanding Netflix User Pain Points Proposing A Solution Measuring success & Launch Way forward

47. "I think about it really as us winning time away,
entertainment time, from other activities. Instead of doing
Xbox or Fortnite or YouTube or HBO or a long list, we want
to win and provide a better experience.”
- Reed Hastings

48. Thank You!
By: Sneha Sultania

49. Hi, I am Sneha
Die-Hard Netflix Fan.
A Product Enthusiast
About me
sul.sneha@gmail.com