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Personalization Palooza 2016

ContentWise evolved from Recommender Engine to UX Personalization Autopilot: a complete solution to automate the digital storefront and assist the editorial curation process.

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Personalization Palooza 2016

  1. 1. Personalizing Television. #ppalooza - Personalization Palooza 2016 - NYC MEDIA LAB
  2. 2. Screens User Marketing & Editorial Team ONE SIZE
 FITS ALL UX Brand
  3. 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. 4. Screens User Marketing & Editorial Team ONE SIZE
 FITS ALL UX Brand
  5. 5. Screens User Marketing & Editorial Team UXUX Autopilot ONE-TO-ONE Actionable Analytics Brand
  6. 6. Marketing & Editorial Team UX Autopilot Actionable Analytics EDITORIAL CURATION BUSINESS RULES A/B TESTING SELF-TUNING Hints KPIs UX USAGE TRACKING
  7. 7. ContentWise Automate the Digital Storefront Deliver a Personalized, One to One User Experience Assist the Content Curation Process
  8. 8. Personalization of TV & Video Services Search, Discovery, Prediction ➜ UX Autopilot Multi-catalog, Multi-language, Multi-screen Analytics, A/B Testing, Metadata Management
  9. 9. EFFECT: Widening the Catalog Coverage Catalog portion watched by users OTT Service 80% 42% No Personalization With ContentWise
  10. 10. EFFECT: The Long Tail That Really Works Playback Distribution Content Assets ContentWise uplift Popular content No Personalization Playbacks
  11. 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. 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. 13. HOW TO: Next-to-play & Binge-viewing Episode 5
  14. 14. Episode 5 Ep.6 Alternative content News 1 News 2 News 3 … Prediction Discovery HOW TO: Next-to-play & Binge-viewing
  15. 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. 16. Surface a personalized set of collections including content with one or more relevant “features” (micro-genres)
  17. 17. LIVE EVENT LIVE EVENT EPG AppsSports Highlights LIVE EVENTLIVE EVENT Cross-domain
  18. 18. LIVE EVENT VIDEO CLIP APP LIVE EVENT EPG AppsSports Highlights LIVE EVENTLIVE EVENT Surfacing elements from other catalogs Cross-domain
  19. 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. 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. 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. 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. 23. UX Engine Consumer UI Admin Console You are in control One API for all client platforms
  24. 24. Don’t fly blind: Analytics on UX Performance EXAMPLE: effect of a rule change on the relevant KPIs
  25. 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. 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. 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. 28. Thank you! www.contentwise.tv Visit our website or contact us pan@contentwise.tv

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