Personalization Palooza 2016
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Personalizing Television. #ppalooza - Personalization Palooza 2016 - NYC MEDIA LAB
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Transcript of Personalization Palooza 2016
Personalizing Television.
#ppalooza - Personalization Palooza 2016 - NYC MEDIA LAB
Screens User
Marketing & Editorial Team
ONE SIZEFITS ALL UX
Brand
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 themLower ARPU
Lower retention
Users tend to ignore or even “mute” sources
that are not relevant.E-mail and mobile notifications
contain generic suggestionsLow ROI on marketing to
subscribers
At the end of the free trial most of the prospects didn’t see the full
value of the offeringThe free trial period provides a
generic user experienceLow conversion ratio of
free trials
One-size-fits-all Impact
Screens User
Marketing & Editorial Team
ONE SIZEFITS ALL UX
Brand
Screens User
Marketing & Editorial Team
UXUX Autopilot
ONE-TO-ONE
Actionable Analytics
Brand
Marketing & Editorial Team
UX Autopilot
Actionable Analytics
EDITORIAL CURATION BUSINESS RULES
A/B TESTING
SELF-TUNING
Hints KPIs
UX
USAGE TRACKING
ContentWise
Automate the Digital Storefront
Deliver a Personalized, One to One User Experience
Assist the Content Curation Process
Personalization of TV & Video Services
Search, Discovery, Prediction ➜ UX Autopilot
Multi-catalog, Multi-language, Multi-screen
Analytics, A/B Testing, Metadata Management
EFFECT: Widening the Catalog Coverage
Catalog portion watched by users
OTT Service
80%
42%
No Personalization
With ContentWise
EFFECT: The Long Tail That Really Works
Playback Distribution
Content Assets
ContentWise uplift
Popular content
No Personalization
Playbacks
? ? ? ? ? ?
HOW TO: Targeted Promotions
You have 20 movies to promotebut space on screen for 3 elements only.
You’d like to display the relevant ones to each user.
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
HOW TO: Next-to-play & Binge-viewing
Episode 5
Episode 5Ep.6 Alternative
content
News 1 News 2 News 3 …
Prediction Discovery
HOW TO: Next-to-play & Binge-viewing
Personalize Through NavigationContent in a flat list. No visual help to
process what’s on the screen.Collections and micro-genres:
easily scannable
Surface a personalized set of collections
including content with one or more relevant
“features” (micro-genres)
LIVE EVENT LIVE EVENT
EPG
AppsSports Highlights
LIVE EVENTLIVE EVENT
Cross-domain
LIVE EVENT VIDEO CLIP APP LIVE EVENT
EPG
AppsSports Highlights
LIVE EVENTLIVE EVENT
Surfacing elements from other catalogs
Cross-domain
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
Hybrid Algorithm Blends collaborative and content-based models to balancetaste-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
12am 4am 8am 12pm 4pm 8pm
TV ScreenMobile
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
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
sequelOffranchise
James Bond
franchise
Schedule
interviewedIn
UX Engine
Consumer UI
Admin Console
You are in control
One API for all client platforms
Don’t fly blind: Analytics on UX PerformanceEXAMPLE: effect of a rule change on the relevant KPIs
On-boarding Trial Training Retention
Convert to paying userSign-up 2nd payment Monthly
renewal
Cold start
WIP: Shift Gears as User Relationship Matures
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
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
Thank you!
www.contentwise.tv
Visit our website or contact us
