tv3.0 new tv frontiers
TRANSCRIPT
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Consumer eXperienceNew TV frontier
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Present the actual status of the Proof of Concept for IPTV customer, based on ScaleOut technoogy
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Proof of Concept status• Manage STB behaviour events
– STBOn– STBChangeChannel– VoDEnter– VoDSelectItem– VoDExit– STBOff
• Alarms– Manage STB alarms– Manage Network alarms
• Monitor Channel Statistics– Channel visualizations global and per postal code (number users)– VoD visualizations (number users)
• Monitor STB– STB state– STB real time channel and session visualizations– STB Session Ads printings– STB recommended Items– STB online user with same likes
• Monitor Alarms– Monitor network alarms – Monitor node status and linked STB statuses
• Batch (MapReduce analysis)– Channels Programs share– Ads prints per channel, per brand, per time stamp as user printed– Item recommendations maps– User with similar likes map
• Recommendations engines– User based recommendation engine ( online user similar to you are now watching …)– Item based recommendations (who bought this item also bought …)
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PoC: Architecture
ConsumereXperience
CDN alarms
STB alarms
STB configuration
TV Guide.xml
Ads scheduling.xml
VoD.xml
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PoC: Tracking STBs• Real Time management of STB state, user behaviour …• Real Time channel visualization tracking, ads printing visualization tracking• Enter/Exit Video On Demand catalogue
TV Guide
Ads scheduling
Events:• STBOn• STBChangeChannel• VoDEnter• VoDSelect• VoDExit• STBOff
VoD
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PoC: Store STBs data
• Store in repository all events produced by every STB for batch analysis
ID_290384, STBOn, 12/2/215-10:00ID_290384, STBChannelChange, 12/2/215-10:02…
Events:• STBOn• STBChangeChannel• VoDEnter• VoDSelect• VoDExit• STBOff
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In-Memory Data Grid intelligently manages IPTV bandwidth allocation for set-top boxes based on updates from CDN on bandwidth usage:• IMDG tracks active set-top
boxes, including viewer’sparameters, events, and box’s CDN node.
• IMDG can apply policies (e.g.,allowed b/w) and notify boxafter viewer event.
• CDN delivers periodic node-specific updates on bandwidthusage and overload condition.
• For each CDN update, IMDGupdates all affected boxes,applies policies, and notifiesbox to adjust b/w if necessary.
• All affected boxes are notified.
PoC: Managing STBs configuration
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PoC: Store CDN events
• Store in repository all alarms produced by CDN and STB alarms for batch analysis
ID_290384, STBAlarm, Type1 ,Level1,12/2/215-10:00ID_290374, STBAlarm, Type1 ,Level2,12/2/215-10:20…
Alarms:• STBAlarm
CDN alarms
NodeID_84, NodeAlarm, Type1 ,Level1,12/2/215-10:00…
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Simple GUI with REAL TIME monitoring of individual and aggregated statistics• Individual STB state• STB Ads printing• STB Channel visualization• STBs alarms• Aggregated Channel statistics• CDN Node state• …
PoC: Monitoring
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PoC: Monitoring
Real Time Programs statistics
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PoC: Monitoring
Advertising tracking
Program Tracking
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PoC: MonitoringCDN alarms
management
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Advance batch analysis with Map/Reduce Big Data techniques• Channel visualization statistics• Program share statistics• Ads printing counting• …
PoC: Batch analysis
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PoC: Batch analysis• Ej: Program audience share report
ID_290384, STBOn, 12/2/215-10:00ID_290384, STBChanneChange, 12/2/215-10:02…
ID_290384, STBOn, 12/2/215-10:00ID_290384, STBChanneChange, 12/2/215-10:02…
ID_290384, STBOn, 12/2/215-10:00ID_290384, STBChanneChange, 12/2/215-10:02…
STB events stored in Log files or
Per STB session Program
visualization analysis
Map
Reduce Per Program reduce
Per STB session Program
visualization analysis
Per STB session Program
visualization analysis
Per STB session Program
visualization analysis
Per Program reduce
Per Program reduce
Programs share report
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PoC: Batch analysis• Ej: Ads printing visualization
ID_290384, STBOn, 12/2/215-10:00ID_290384, STBChanneChange, 12/2/215-10:02…
ID_290384, STBOn, 12/2/215-10:00ID_290384, STBChanneChange, 12/2/215-10:02…
ID_290384, STBOn, 12/2/215-10:00ID_290384, STBChanneChange, 12/2/215-10:02…
STB events stored in Log files or
Per STB session Ad
printing analysis
Map
Reduce Per Ad reduce
Per STB session Ad
printing analysis
Per STB session Ad
printing analysis
Per STB session Ad
printing analysis
Per Ad reduce
Per Ad reduce
Ads visualization report
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PoC: Batch analysis• Ej: Ads printing visualization. Number of
impresions (user that have seen the Ad)
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Discursion of several posibilities for Innovative Services and their possible implementation in the PoC
Some services are not implemented in actual PoC release.User based recomendations and Item based recommendations are partialy released
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Two user scenarios
TV channel watching Video On Demand watching
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Consumer eXperience
The sum of experiences at all interaction point between the customer and the TV service during all the duration of a TV session.
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Consumer eXperience services
• One2One marketing• One2One recommendations• Users based recommendation• Item based recommendation • Online pop-up notifications• Rates&Reviews• Gamification• Social TV
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Value Added services
• Ads personalization• Ads bidding• Identity Management• Big Data Analitycs
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One-to-one marketing
• Propose a different user experience for EACH USER, and EACH TIME the user enters.
• Total personalization of the user interface• Personalization of marketing campaigns– Set dozens/hundreds of campaign rules by
marketing– Analyzed on real time for each user
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One-to-one marketing
ConsumereXperience
Marketing Rules engine
1. If condition offer item2. If … offer … 3. If … offer … 4. If … offer … 5. If … offer … 6. If … offer … 7. If … offer …
All data from each user:- User Profile- Past consumer actions
Marketing Rules
Condition any logical combination of user profile, past consumer experience and online actions
Item any product, film, program, discount … from internal products catalog or from external sources
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One-to-one marketing
Hello Sara, we have this offering for you
In your next film
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One-to-one marketing
Hello Sara, we recommend to Buy first
HDSEE HD UPGRADE TO
CAR ISSURANCE CUPON
174095E6T63
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One-to-one recommendations
• This is a recommendations model based on marketing interests, so is marketing who decides under specific circumstances which item from catalogue to to recommend the user.
• Can be based on items characteristics (ej: type of film, director, ..)
• Can be based on customer past behavior (ej: if the last film watched was a Disney one offer new Disney films)
• or both types … combined or not
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One-to-one recommendations
ConsumereXperience
Recommendations engine
All data from each user:- User Profile- Past consumer actions
Recommendations relationships
X X
X
X
Relations between items base on catalogue section, item characteristics, actors etc
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One-to-one recommendations
Other similar filmsBrowsing
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One-to-one recommendations
ConsumereXperience
Recommendations engine
All data from each user:- User Profile- Past consumer actions
Recommendations engine (combined)
Batch Hadoop process.Based on the previous customer events, last films watched and the relations catalogue
X X
X
X X
X
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One-to-one recommendations
FREE with 5600p
Our recommendations for you
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Collective Intelligence
• Offer not what marketing wants to sell but what the user wants to buy
• No marketing interaction• Only based on users behavior• Most algorithm parts done in batch processing
best with Map/Reduce • Algorithms does know nothing about users
profiles, or products characteristics• Persistence storage could be build NoSQL database
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Collective Intelligence
(ID_USER1, ID_ITEM4)
USERID ITEMID
245345 3264
262456 45654
262456 4546
262456 456
345343 45
132312 324
132312 234
ConsumereXperience
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Online users based recommendation
• Use of Collective Intelligence• Is based on what is call neighborhood, which
are the users that have similar “likes” to the one selected user
• Each user has its own neighborhood, as many different neighborhood as users
• Respond to the question“ What are other users like me watching now ?”
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Online users based recommendation
ConsumereXperience
Recc. EngineStep 2
All data from each user:- User Profile- Past consumer actions
Batch Hadoop step 1
UserNeighborhood object
Neighborhoodalgorithm…
3,2 6,7 8,2 3,2 4,7 2,3
6,7 4,7 6,8
8,2 2,3 6,8
USERID ITEMID
245345 3264
262456 45654
262456 4546
262456 456
345343 45
132312 324
132312 234
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Online users based recommendation
• Batch Hadoop Step 1• Batch process to find for each user the most
similar users based on previous buying experience (neighborhood)
• NxN/2 algorithm• The result is for each user a weighted distance
with the rest of users
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Online users based recommendation
• Real time recommendation engine Step 2• Or one online user, the step 2 gets the
neighborhood (ej: 100) of this user and in real time asks those neighborhood users what are watching now.
• Collects, weights, normalizes and order results to offer a small set of recommended programs
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Online users based recommendation
Other people (similar to you ) are watching now
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Item based recommendation
• Use of Collective Intelligence• Creates a neighborhood of items for each item• Respond to the question“ Who bought this item also bought …”
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Item based recommendation
ConsumereXperience
All data from each user:- User Profile- Past consumer actions
Batch Hadoop step 1
ItemNeighbourhood database
Neighborhoodalgorithm
Rec. Engine step 2
3,2 6,7 8,2 4,7 2,3
6,8
USERID ITEMID
245345 3264
262456 45654
262456 4546
262456 456
345343 45
132312 324
132312 234
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Item based recommendation
• Hadoop Step 1 process to find based on the user neighborhood the relation of most close items to each other
• NxN/2 algorithm• The algorithm converts the user neighborhood
in a item neighborhood • The result is for each item a weighted distance
with the rest of item
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Item based recommendation
• Recommendation Engine Step 2 – when the user selects one item (ej: film) the in memory representation objects passes that info to Recommendation Engine who asks for ej: 4 recommended items for the selected one.
• Is a real time query to batch Map/Reduce prepared information
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Item based recommendation
Who viewed this film also viewed
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Recommendation refinements
• All presented recommendation engines should have refinements, probably the most important is NOT to recommend any product that the user has actually bought in the past.
• So item recommendations engines should be combined with a “filter” engine that for a user and a set of items removes the the items previously bought.
• This could be a recommendation engine inside component or maybe an external component
• This filtering is done in Real Time
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Recommendation refinements
ConsumereXperience
Any recommendation
engine
All data from each user:- User Profile- Past consumer actions
Items setTo recommend
Items filter
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Recommendation refinements II
• Other refinement to algorithms could come from when the commendation is queried (time/date/day of week)
• Up to now same person would receive same recommendation Friday night 9:00, than Saturday morning 10:00, and that may not have sense, so for more relevant recommendations, day of week and time could be consider as a key factor in recommendation algorithm
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Recommendation refinements II
(ID_USER1, ID_ITEM4)
USERID Time/Date ITEMID
245345 10/10:00 3264
262456 10/11:00 45654
262456 10/11:01 4546
262456 10/12:01 456
345343 11/12:01 45
132312 12/18:01 324
132312 12/18:05 234
ConsumereXperience
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Recommendation refinements II
ConsumereXperience
Recc. EngineStep 2
All data from each user:- User Profile- Past consumer actions
Batch Hadoop step 1
Time frame based UserNeighborhoods
Neighborhoodalgorithm…
3,2 6,7 8,2 3,2 4,7 2,3
6,7 4,7 6,8
8,2 2,3 6,8
USERID Time/Date
ITEMID
245345 10/10:00 3264
262456 10/11:00 45654
262456 10/11:01 4546
262456 10/12:01 456
345343 11/12:01 45
132312 12/18:01 324
132312 12/18:05 234
3,2 6,7 8,2 3,2 4,7 2,3
6,7 4,7 6,8
8,2 2,3 6,8
3,2 6,7 8,2 3,2 4,7 2,3
6,7 4,7 6,8
8,2 2,3 6,8
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Item based recommendation
ConsumereXperience
All data from each user:- User Profile- Past consumer actions
Batch Hadoop step 1
ItemNeighbourhood database
Neighborhoodalgorithm
Rec. Engine step 2
3,2 6,7 8,2 4,7 2,3
6,8
USERID Time/Date
ITEMID
245345 10/10:00 3264
262456 10/11:00 45654
262456 10/11:01 4546
262456 10/12:01 456
345343 11/12:01 45
132312 12/18:01 324
132312 12/18:05 234
3,2 6,7 8,2 4,7 2,3
6,8
3,2 6,7 8,2 4,7 2,3
6,8
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More recommendation refinements
• More and more sophisticated engines could be implemented based in more sophisticated algorithms introducing– Behavioral aspects (recommendation based on
actual navigation and searches)– User profile analysis (Identity360)– Aggregated Geo Information or other statistical
information– ...
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Online pop-up notifications
• Consider that one user is browsing the Catalogue or watching a program or a film.
• The system has determined that in your past watching experience you always see a program in a channel and is about to start right now
• The system will send you a notification alert that the program you always see is going to start in that moment and recommend to switch to that channel and not lose the beginning of the program
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Online pop-up notifications
Hi Sara, you always watch and is going to start right now, want to switch ?
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Online pop-up notifications
• Consider that one user is browsing the Catalogue or watching a program or a film.
• The algorithm could determine that in your neighbourhood of similar users, people is switching at this right moment massively to a specific program in other channel
• The system will send you a notification alert that the program other similar users to you are moving to that TV program (maybe MasterChef) that is starting right now
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Online pop-up notifications
Hi Sara, many people is switching to right now, do you want to switch too ?
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Online pop-up notifications• Consider that one user watching a soccer match. The first
period ends and ads will take 15 minutes. The user starts zapping.
• The algorithm could determine that the users is watching the match and after 15 minutes the second period starts
• If when the second period starts the users is watching other program, the system will send a notification alert that the match is stating second period and ask if watch to switch
• In general is applicable to films, programs etc
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Online pop-up notifications
Hi Sara, the second period of the soccer match is to start, do you want to switch ?
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Rates&Reviews
• People buys based on other users reviews• In music or films is a key decision point• Independent evaluation of films by other users
helps a more trustable buying decision• Rates could be general or weighted by user
neighbourhood • Reviews could be selected online or offline by
call center independent personnel
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Rates&Reviews
ConsumereXperience
R&R engine
All data from each user:- User Profile- Past consumer actions
3 4 1 8
5 6
3
2 3
8
User ratings
Batch Hadoop process
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Rates&Reviews refinement
• While rating of items could be calculated with the total community of user rating (which is the normal case in all rating models) as an arithmetic average, is more relevant id we consider only the rating of the neighbourhood of the user.
• So this refinement only consider the arithmetic average of the user that has similar likes to the one to be given item rates.
• So 2 different users will see different ratings for same product
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Rates&Reviews
ConsumereXperience
R&R engine
All data from each user:- User Profile- Past consumer actions
5 6
3
2 3
8
3,2 6,7 8,2 4,7 2,3
6,8
Recommendation engineneighbourhood
User ratings
3 4 1 8
Batch Hadoop process
Personalized rating
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Rates&Reviews
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Gamification• Introduces gaming engagement concepts to consumer
experience• Those may include reputation point, budgets, virtual money,
rankings …• User may receive those benefits from, buying a film, rating a
program, …• Make programs visible based on reputation point or budgets
or virtual money …• Make gamification info social• Gaming info can be include in one to one marketing
campaigns and the opposite…
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Gamification
ConsumereXperience
Gamification engine
All data from each user:- User Profile- Past consumer actions
• Points for buy• Points for recomm• Points for rating• Expert point …• Master points…• …• …
Gamification Rules
Marketing Rules engine
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Gamification
Need 2100
point to open this
film
You have 1800 points
FREE with 5600p
Congrats you can watch this film
Only Grand Master
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Gamification
You have 1800 points
Last films opened
Your friends points
4000 points
500 points
2200 points
800 points
3400 points
2200 points
1200 points
1900 points
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Social TV
• People likes to expose on social networks their likes, actions and share those info with friends
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Facebook Integration
• So Facebook integration is a first must in social TV.– Connect TV with user Facebook account– Share films seen to friends– Share point and gadgets– Share films reviews– See friends actions (programs or films they have
seen with their opinion)
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Facebook Integration
ConsumereXperience
Facebook integration
All data from each user:- User Profile- Past consumer actions
19/02/15
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Facebook Integration
Sara recommends you the film
Sara reached 1800 point
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Facebook IntegrationYour Facebook friends online are watching :
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Social TVYour Facebook friends recommendations for you:
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IMDb Integration
• Other social integrations could also be possible , for example IMDb integration
• IMDb is the world best database of films information
• Maybe al alternative/complement for the presented Rates module could be used instead the IMDb rates & reviews
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IMDb Integration
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Twitter Integration
• People likes to know what other people thinks on a specific matter or program
• So Twitter integration is a must in social TV.– Connect TV with user Twitter– A TV program is linked to a Twitter tag – See Twits on real time on TV linked to a program
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Twitter Integration
ConsumereXperience
Twitter integration
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Twitter Integration
75
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Social Chat TV
• Real time communication with other users• In a similar way to Whatsapp or thought
Facebooks groups a user could establish online communication channel with contacts, Facebook friends or whatever groups or contacts platforms defines as available
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Social Chat TV
ConsumereXperience
All data from each user:- User Profile- Past consumer actions
Peer-To-Peer communication
Hi friend, now starting Master Chef in channel 6
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Social TV
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Hi friend, now starting Master Chef in channel 6
Hi friend, now starting Master Chef in channel 6
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All services working together in REAL TIME
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All services running together
Gamification engine
Personalization engine
Recommendations engines
Rates&Reviews engine
Social engines
ConsumereXperience
Online communications
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All services togetherYou have 1800 points
Hello Sara, we have this offering for you
Our recommendations for you today
Other people is watching now
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All services together
You have 1800 points
Your ratings
Want to know more
Last programs you have seen
Specials offers for you
Our recommendations for you today
Other people is watching now Your friends are watching
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All services together
You have 1800 points
SEE REVIEWS
YOU WILL RECIVE 250 POINTS
Who bought this film also bought
HD
TO SEE HD UPGRADE TO
See more films from
Our recommendations for you
Your second Disney film
Want to see what is happening in
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All services together
You have 2050 pointsYou have added 250 POINTS
Here is your cupon: 2gn3487634g48
Want to rate this film? Get 200 point
50% off Your next Disney film
Want to recommend this film to your friends? Get 100 point
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All services together
85
Hi Sara, the second period of the soccer match is to start, do you want to switch ?
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Value Added services
A set of Value Added Services for the TV operators that increase the benefits and open a new set of strategic posibilities
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Value Added services
• Ads personalization• Ads bidding• Identity Management• Batch Big Data Analitycs
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Ads personalization• Google AdWorks model has disrupted the way publishing ads• In a similar way, TV advertising model could be based on:
– Pay per print– Personalization
• That means that EACH person watches a different Ad at the same time, based on different factors like its profile, Identity information, past behaviour, current scenario, date/time etc…
• So we need three main elements:– A Real Time powerfull personalization Engine that should be able to select
the best fit Ad for each user each time– A Real Time tracking engine to count ads printing per user– A behaviour engine to track the user behaviour in Ads to feedback system
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Ads personalization
89
TV Guide
Ads scheduling
Ads catalogAds tracking
Adds personalization
engine
The Ads personalization engine selects the BEST matching Ad for every user every time
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Ads bidding
• The possibility to track and visualize in Real Time the number of user watching a TV channel allows to create and offer a Real Time bidding platform for Real Time ads insertion.
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Identity management• Identity Management means Record, Connect and Analyze
ALL available information from users to create a complete user profile called Identity360– Contract information– User behavioral information
• What, when are user preferences and interests (programs, films…)• Watching, zapping and TV usage behavior• Navigation and search behavior• Buying behavior
– Social information (Facebook, twitter…)– Service contacts and call center iterations– …
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Big Data analytics
Big Data analysis opens a broad range of new possibilities• Customer analytics– Customer better understanding, marketing One2One, better
Ads insertion, UpSelling, CrossSelling …• Strategic statistics– Aggregated customers behavior, GeoAnalysis, Behavioral
Analysis, Better Segmentation Analysis• Infrastructure– Better CDN provisioning and better customer quality
experience and support