The document outlines the status of a proof of concept (POC) for IPTV customer experience using technologies such as scaleout technology, providing management and monitoring of set-top box (STB) behavior, alarms, and channel statistics. It describes the implementation of a recommendation engine based on user interactions and preferences, as well as features for real-time monitoring and batch analysis of data. Additionally, it discusses innovative advertising and marketing strategies tailored to individual user experiences for enhancing engagement.

1. Consumer eXperience
New TV frontier

2. Present the actual status of the Proof
of Concept for IPTV customer, based
on ScaleOut technoogy
2

3. 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 …)
3

4. PoC: Architecture
Consumer
eXperience
4
CDN alarms
STB alarms
STB configuration
TV Guide.xml
Ads scheduling.xml
VoD.xml

5. 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
5
TV Guide
Ads scheduling
Events:
• STBOn
• STBChangeChannel
• VoDEnter
• VoDSelect
• VoDExit
• STBOff
VoD

6. PoC: Store STBs data
• Store in repository all events produced by
every STB for batch analysis
6
ID_290384, STBOn, 12/2/215-10:00
ID_290384, STBChannelChange, 12/2/215-10:02
…
Events:
• STBOn
• STBChangeChannel
• VoDEnter
• VoDSelect
• VoDExit
• STBOff

7. 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’s
parameters, events, and
box’s CDN node.
• IMDG can apply policies (e.g.,
allowed b/w) and notify box
after viewer event.
• CDN delivers periodic node-
specific updates on bandwidth
usage and overload condition.
• For each CDN update, IMDG
updates all affected boxes,
applies policies, and notifies
box to adjust b/w if necessary.
• All affected boxes are notified.
PoC: Managing STBs configuration
7

8. PoC: Store CDN events
• Store in repository all alarms produced by
CDN and STB alarms for batch analysis
8
ID_290384, STBAlarm, Type1 ,Level1,12/2/215-10:00
ID_290374, STBAlarm, Type1 ,Level2,12/2/215-10:20
…
Alarms:
• STBAlarm
CDN alarms
NodeID_84, NodeAlarm, Type1 ,Level1,12/2/215-10:00
…

9. 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
9

10. PoC: Monitoring
10
Real Time Programs
statistics

11. PoC: Monitoring
11
Advertising
tracking
Program Tracking

12. PoC: Monitoring
12
CDN alarms
management

13. Advance batch analysis with Map/Reduce Big
Data techniques
• Channel visualization statistics
• Program share statistics
• Ads printing counting
• …
PoC: Batch analysis
13

14. PoC: Batch analysis
• Ej: Program audience share report
14
ID_290384, STBOn, 12/2/215-10:00
ID_290384, STBChanneChange, 12/2/215-10:02
…
ID_290384, STBOn, 12/2/215-10:00
ID_290384, STBChanneChange, 12/2/215-10:02
…
ID_290384, STBOn, 12/2/215-10:00
ID_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

15. PoC: Batch analysis
• Ej: Ads printing visualization
15
ID_290384, STBOn, 12/2/215-10:00
ID_290384, STBChanneChange, 12/2/215-10:02
…
ID_290384, STBOn, 12/2/215-10:00
ID_290384, STBChanneChange, 12/2/215-10:02
…
ID_290384, STBOn, 12/2/215-10:00
ID_290384, STBChanneChange, 12/2/215-10:02
…
STB events stored in
Log files or
Per STB
session Ad
printing
analysis
Map
Reduce
Per Ad
reduc
e
Per STB
session Ad
printing
analysis
Per STB
session Ad
printing
analysis
Per STB
session Ad
printing
analysis
Per Ad
reduc
e
Per Ad
reduc
e
Ads visualization report

16. PoC: Batch analysis
16
• Ej: Ads printing visualization. Number of
impresions (user that have seen the Ad)

17. Discursion of several posibilities for
Innovative Services and their possible
implementation in the PoC
17
Some services are not implemented in actual PoC release.
User based recomendations and Item based recommendations are partialy released

18. Two user scenarios
18
TV channel watching Video On Demand watching

19. 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.
19

20. Consumer eXperience services
• One2One marketing
• One2One recommendations
• Users based recommendation
• Item based recommendation
• Online pop-up notifications
• Rates&Reviews
• Gamification
• Social TV
20

21. Value Added services
• Ads personalization
• Ads bidding
• Identity Management
• Big Data Analitycs
21

22. 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
22

23. One-to-one marketing
Consumer
eXperience
1. If condition offer item
2. 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
23

24. One-to-one marketing
Hello Sara, we have this offering for you
In your next film
24

25. One-to-one marketing
Hello Sara, we recommend to Buy first
HD
SEE HD UPGRADE TO
CAR ISSURANCE CUPON
174095E6T63
25

26. 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
26

27. One-to-one recommendations
Consumer
eXperience
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
27

28. One-to-one recommendations
Other similar films
Browsing
28

29. One-to-one recommendations
Consumer
eXperience
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
29

30. One-to-one recommendations
FREE with 5600p
Our recommendations for you
30

31. 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
31

32. Collective Intelligence
32
(ID_USER1, ID_ITEM4)
USERID ITEMID
245345 3264
262456 45654
262456 4546
262456 456
345343 45
132312 324
132312 234
Consumer
eXperience

33. 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 ?”
33

34. Online users based recommendation
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
User
Neighborhood
object
Neighborhood
algorithm
…
3,2 6,7 8,2
3,2 4,7 2,3
6,7 4,7 6,8
8,2 2,3 6,8
34
USERI
D
ITEMID
245345 3264
262456 45654
262456 4546
262456 456
345343 45
132312 324
132312 234

35. 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
35

36. 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
36

37. Online users based recommendation
Other people (similar to you ) are watching now
37

38. 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 …”
38

39. Item based recommendation
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
Item
Neighbourhood
database
Neighborhood
algorithm
3,2 6,7 8,2
4,7 2,3
6,8
39
USERI
D
ITEMID
245345 3264
262456 45654
262456 4546
262456 456
345343 45
132312 324
132312 234

40. 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
40

41. 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
41

42. Item based recommendation
Who viewed this film also viewed
42

43. 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
43

44. Recommendation refinements
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
44
Items set
To recommend

45. 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
45

46. Recommendation refinements II
46
(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
Consumer
eXperience

47. Recommendation refinements II
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
Time frame
based User
Neighborhoods
Neighborhood
algorithm
…
3,2 6,7 8,2
3,2 4,7 2,3
6,7 4,7 6,8
8,2 2,3 6,8
47
USERID Time/Da
te
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

48. Item based recommendation
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
Item
Neighbourhood
database
Neighborhood
algorithm
3,2 6,7 8,2
4,7 2,3
6,8
48
USERID Time/Da
te
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

49. 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
– ...
49

50. 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
50

51. Online pop-up notifications
51
Hi Sara, you always watch and is going to start right now, want to switch ?

52. 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
52

53. Online pop-up notifications
53
Hi Sara, many people is switching to right now, do you want to switch too ?

54. 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
54

55. Online pop-up notifications
55
Hi Sara, the second period of the soccer match is to start, do you want to switch ?

56. 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
56

57. Rates&Reviews
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
57
3 4 1 8
5 6
3
2 3
8
User ratings

58. 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
58

59. Rates&Reviews
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
59
5 6
3
2 3
8
3,2 6,7 8,2
4,7 2,3
6,8
Recommendation engine
neighbourhood
User ratings
3 4 1 8
Personalized rating

60. Rates&Reviews
60

61. 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…
61

62. Gamification
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
62
• Points for buy
• Points for recomm
• Points for rating
• Expert point …
• Master points…
• …
• …
Gamification Rules

63. Gamification
Need
2100
point to
open this
film
You have 1800 points
FREE with 5600p
Congrats
you can
watch this
film
Only Grand
Master
63

64. Gamification
You have 1800 points
Last films opened
Your friends points
64
4000 points
500 points
2200 points
800 points
3400 points
2200 points
1200 points
1900 points

65. Social TV
• People likes to expose on social networks their
likes, actions and share those info with friends
65

66. 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)
66

67. Facebook Integration
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
19/02/15 67

68. Facebook Integration
68
Sara recommends you the film
Sara reached 1800 point

69. Facebook Integration
Your Facebook friends online are watching :
69

70. Social TV
Your Facebook friends recommendations for you:
70

71. 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
71

72. IMDb Integration
72

73. 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
73

74. Twitter Integration
Consumer
eXperience
74

75. Twitter Integration
7575

76. 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
76

77. Social Chat TV
Consumer
eXperience
All data from each user:
- User Profile
- Past consumer actions
77
Hi friend, now starting
Master Chef in channel 6

78. Social TV
7878
Hi friend, now starting
Master Chef in channel 6
Hi friend, now starting
Master Chef in channel 6

79. All services working together in REAL TIME
79

80. All services running together
80
Consumer
eXperience

81. All services together
You have 1800 points
Hello Sara, we have this offering for you
Our recommendations for you today
Other people is watching now
81

82. 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
82

83. 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
83

84. All services together
You have 2050 points
You 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
84

85. All services together
8585
Hi Sara, the second period of the soccer match is to start, do you want to switch ?

86. 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
86

87. Value Added services
• Ads personalization
• Ads bidding
• Identity Management
• Batch Big Data Analitycs
87

88. 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
88

89. Ads personalization
8989
TV Guide
Ads scheduling
Ads catalogAds tracking
Adds
personalization
engine
The Ads personalization engine selects the BEST matching Ad for every user every time

90. 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.
90

91. 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
– …
91

92. 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
92

93. 93
THANKS for your time
www.qualityobjects.com
César Carralero
ccarralero@qualityobjects.com