This document discusses the need for personalized television experiences and outlines ContentWise's solution. ContentWise automates the digital storefront and delivers personalized, one-to-one user experiences through search, discovery, prediction and UX automation. It assists with content curation using analytics, testing and metadata management to surface relevant content and promotions to each individual user. This widens catalog coverage and increases viewership of long-tail content.

1. Personalizing Television.
#ppalooza - Personalization Palooza 2016 - NYC MEDIA LAB

2. Screens User
Marketing & Editorial Team
ONE SIZE
FITS ALL UX
Brand

3. Dangers
Most of the audience
does not find relevant content
with low effort.
When it’s time to optimize bills, a
portion of subscribers doesn’t see
enough value.
Users don’t see the content offers
suitable for them.
The screen is small and people
don’t scroll or dig enough to
find what’s good for them
Lower ARPU
Lower retention
Users tend to ignore
or even “mute” sources
that are not relevant.
E-mail and mobile notifications
contain generic suggestions
Low ROI on marketing to
subscribers
At the end of the free trial most of
the prospects didn’t see the full
value of the offering
The free trial period provides a
generic user experience
Low conversion ratio of
free trials
One-size-fits-all Impact

4. Screens User
Marketing & Editorial Team
ONE SIZE
FITS ALL UX
Brand

5. Screens User
Marketing & Editorial Team
UXUX Autopilot
ONE-TO-ONE
Actionable Analytics
Brand

6. Marketing & Editorial Team
UX Autopilot
Actionable Analytics
EDITORIAL CURATION
BUSINESS RULES
A/B TESTING
SELF-TUNING
Hints
KPIs
UX
USAGE TRACKING

7. ContentWise
Automate the Digital Storefront
Deliver a Personalized, One to One User Experience
Assist the Content Curation Process

8. Personalization of TV & Video Services
Search, Discovery, Prediction ➜ UX Autopilot
Multi-catalog, Multi-language, Multi-screen
Analytics, A/B Testing, Metadata Management

9. EFFECT: Widening the Catalog Coverage
Catalog portion
watched by users
OTT Service
80%
42%
No Personalization
With ContentWise

10. EFFECT: The Long Tail That Really Works
Playback Distribution
Content Assets
ContentWise uplift
Popular
content
No Personalization
Playbacks

11. ? ? ? ? ? ?
HOW TO: Targeted Promotions
You have 20 movies to promote
but space on screen for 3 elements only.
You’d like to display the relevant ones to each user.

12. Let’s say that we have 20 movies to promote
but space on screen for only 3 elements.
We want to display the relevant ones for each user.
HOW TO: Targeted Promotions

13. HOW TO: Next-to-play & Binge-viewing
Episode 5

14. Episode 5
Ep.6
Alternative
content
News 1 News 2 News 3 …
Prediction Discovery
HOW TO: Next-to-play & Binge-viewing

15. Personalize Through Navigation
Content in a flat list. No visual help to
process what’s on the screen.
Collections and micro-genres:
easily scannable

16. Surface a personalized
set of collections
including content with
one or more relevant
“features” (micro-genres)

17. LIVE EVENT LIVE EVENT
EPG
AppsSports
Highlights
LIVE EVENTLIVE EVENT
Cross-domain

18. LIVE EVENT VIDEO CLIP APP LIVE EVENT
EPG
AppsSports
Highlights
LIVE EVENTLIVE EVENT
Surfacing elements
from other catalogs
Cross-domain

19. Classic Recommender System
Collaborative Filtering
Suggests content that has been relevant for other users
with a viewing history similar to mine.
WHO LIKED THIS ALSO LIKED…
Tends to surface popular content
Cannot suggest new additions (“cold start problem”)
Content-based
Suggests content similar to what I watched in the past.
Tends to stay confined in the user’s comfort zone
Limits true catalog exploration

20. Hybrid Algorithm
Blends collaborative and content-based models to balance
taste-matching, popularity and serendipity.
New content items are assigned an initial score based on each
user’s taste and content metadata.
This puts them “in motion” and, if they are watched and become
popular in certain audience segments, the collaborative
component starts prevailing.
PROBLEM: new content is “cold”
SOLUTION

21. 12am 4am 8am 12pm 4pm 8pm
TV Screen
Mobile
Contextual User Habits
Learns user’s habits in the context of time, location and device.
Predicts user’s intentions by surfacing content typically watched
in the specific context.
For example:
- around 3pm of a Sunday, on the living room TV
- at 6pm of a Wednesday, on the smartphone, out of
home
Very effective for shared devices with no user login.
PROBLEM: Shared Devices Without Login
SOLUTION

22. Semantic Enrichment and Knowledge Graph
Movie Episode
Gossip Video
Talk Show
Clip
spouse
2015..
spouse
2000..2005
Gossip Video
appearsIn appearsInactorOf appearsIn
Season
Series
Special
spinOff
appearsIn Channel
BrandTalk Show
Brand
Movie
sequelOf
franchise
James
Bond
franchise
Schedule
interviewedIn

23. UX Engine
Consumer UI
Admin Console
You are in control
One API for all
client platforms

24. Don’t fly blind: Analytics on UX Performance
EXAMPLE: effect of a rule change on the relevant KPIs

25. On-boarding Trial Training Retention
Convert to
paying user
Sign-up 2nd payment
Monthly
renewal
Cold start
WIP: Shift Gears as User Relationship Matures

26. WIP: Think in Two Dimensions Vertical Layouts
Personalized Selection
TOP PICKS FOR YOU
TRENDING SERIES
ACTION MOVIES
COMEDIES SET IN NEW YORK
SPY MOVIES BASED ON BOOKS
Alice
TOP PICKS FOR YOU
MOST VIEWED
NEW ARRIVALS
WHAT’S TRENDING
TRENDING SERIES
FAMILY MOVIE NIGHT
DS: <GENRE> MOVIES
DS: COMEDIES SET IN <CITY>
DS: <EDITORIAL COLLECTION>
WATCH IT AGAIN
BECAUSE YOU LIKED…
OSCAR WINNERS
ENABLED STREAMS VERTICAL LAYOUT
MANUALLY PINNED
ALGORITHMIC SELECTION

27. THE FUTURE?
Own your knowledge:
audience behavior & content performance
Know your users through
the stories they like:
emotional traits
lifestyle traits
social traits
Own the ability to expose
YOUR users to the
relevant brand messages

28. Thank you!
www.contentwise.tv
Visit our website or contact us
pan@contentwise.tv