In order to improve audience engagement, media companies must deal with vast amounts of raw data from web, social media, devices, catalogs, and back-channel sources. This session dives into predictive analytic solutions on AWS: We present architecture patterns for optimizing media delivery and tuning overall user experience based on representative data sources (video player clickstream, web logs, CDN, user profiles, social media sentiment, etc.). We dive into concrete implementations of cloud-based machine learning services and show how they can be leveraged for profiling audience demand, cueing content recommendations and prioritizing delivery of related media. Services covered include Amazon EC2, Amazon S3, Amazon CloudFront, and Amazon EMR.

3. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
How AWS Cloud Analytics Drives Audience
Engagement and Revenue
Overview and Demos
Deepak Khullar

4. • AWS Media Workloads
• AWS architecture for big data workloads
• Media Analytics – Use Cases
• AWS Media Partner Analytics Ecosystem
• Partner Speaker & Demo
• Pronam Chatterjee (CEO)
• Blue Pi Technologies
• Customer Speaker & Demo
• Retesh Gondal - Head Technology, APB News
• Anuj Sharma – Tech Lead, ABP News
Agenda

5. AWS Media Workloads
Content
Production
Content
Distribution &
Consumption
Processing &
Management
Content
Storage
 Modelling
 Rendering
 Video editing
 Post production
 Broadcast signal
acquisition
 Streaming of live
and VOD content
 B2B distribution of
content
 Insertion of Video
advertising for
live/on demand
content
 High speed ingest
 Library storage and
archiving
 Tier management
 Content/asset
management
 En/Transcode
 Packaging
 Encryption,
watermarking
 Digital Rights
Management
Consumer
Insight and
Analytics
 Analytics,
reporting, log
analysis
 Real-time
monitoring
 Content discovery
 Content
recommendations
Shared IT Services
NetworkSecurity OperationsInfrastructure
Partner Solutions

6. • Netflix: Over 75% of what people watch comes from recommendations [1]
• Nielsen: Social media activity [Tweets] drive higher broadcast TV ratings for
48% of shows [2]
• Google: 70% of the variation in box-office performance can be explained with
movie-related search volume [3]
Content discovery … and the conversation around it …
matter!
[1] http://www.slideshare.net/AmazonWebServices/maximizing-audience-engagement-in-media-delivery-med303-aws-reinvent-2013-28622676
[2] http://www.nielsen.com/content/corporate/us/en/press-room/2013/new-nielsen-research-indicates-two-way-causal-influence-between-.html
[3] http://www.google.com.au/think/research-studies/quantifying-movie-magic.html
Audience Engagement Signals

7. Search
Watch
Listen
Play
Download
Purchase
Contact sales
Subscribe
Contact support
Cancel
Rate It
Review It
Upgrade It
Sharing
Tagging
Bookmarking
Social Sentiment
Audience Engagement Signals

8. • Descriptive
• Retrospective
• What happened or is happening
• Simple aggregations and counters
• Predictive
• Statistical forecast
• Predict a value in a dataset
• Machine learning
• Prescriptive (emergent)
• What should I do about it?
Descriptive
Predictive
Prescriptive
The Analytics Spectrum

9. Common M&E Analytic Topics of Interest
Content
•Top Content
•Engagement
•Plays per
session
•Drop-off
• Referral path
• Recommendation
Audience
• Acquisitions
• Churn
• Where, when, who
• Segmentation
• Cohorts
Operations
• How much
buffering
• Best CDN paths
• Top devices
• Uniques per
platform
Monetization
• Monetization
• Ad Spend
• Social Media
• Mentions
• Sentiment
Analysis

10. Amazon S3
Amazon Kinesis
Amazon DynamoDB
Amazon RDS (Aurora)
AWS Lambda
Amazon
EMR
Amazon
Redshift
Amazon Machine
Learning
Collect Process Analyze
Store
Data Collection
and Storage
Data
Processing
Event
Processing
Data
Analysis
Data/
Audience
Engagement
Signals
Answers
Typical (Big Data Analytics) Pipeline

11. Typical (Big Data Analytics) Pipeline
Data/
Audience
Engagement
Signals
Answers
Collect Process AnalyzeCollect
Store

12. Data/
Audience
Engagement
Signals
Answers
Collect Process AnalyzeCollect
Store
Typical (Big Data Analytics) Pipeline

13. SQL
Analytics UI
AWS QuickSight
Collect Process AnalyzeCollect
Store
Data/
Audience
Engagement
Signals
Answers
Typical (Big Data Analytics) Pipeline

14. • Branch of Artificial Intelligence and Statistics
• Programming computers based on historical experience
• Focuses on prediction based on known properties learned from training data
Signals Predictions
Machine Learning for Predictive Analytics

15. Scale
Adaptability
Agility
Why Cloud?

16. Use Cases

17. Use Case Scenarios
• Analyze content (articles, video, music, images) consumed
• Understand content better and design content strategy
• Understand consumption behavior/profiling of consumers
• Recommend products, movies, music, events
• Based on similar users
• Based on similar items
• CDN selection
• Optimize monetization of content
• Ad / marketing campaign targeting
• Classify audience into target segments
• Mine opinions and sentiment
• Predict audience churn

18. Related Content

19. Related Content – Case Study

20. • Business Impact:
• 20% traffic increase within 1 month
• 45% of story based traffic came after “related” content widget was placed
• Bounce rate reduced from 63.07% to 53.7%
• Pages per session increased to 3.08 Vs 2.53
• Average session duration increased to 4.09 mins from 3.05 mins
Case Study – Punjab Kesari Group

21. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved

22. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved

23. Video/Content Analytics

24. • User Acquisition and Churn
• Content Performance
• Monetization and Revenue
• Cohort Analysis
• Subscriber Lifecycle
Video/Content Analytics

25. Video/Content Analytics

26. Video/Content Analytics – Live/Concurrent Users

27. Video/Content Analytics – Engagement

28. Video/Content Analytics – Churn

29. Video/Content Analytics – Acquisition

30. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
Recommendations

32. • Types of recommender systems
• Content-based recommendations
• Collaborative filtering recommendations
• User-user recommendations
• Item-item recommendations
• Applications
• Products and services you would like to buy
• People you might want to connect with
• Recommending movies, videos, songs, games,
and events you might like
Recommendations

33. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
Sentiment Analysis

34. “I thought Star Wars Episode 29 was not without merit ”

35. Is this Positive or Negative?

36.  Capture social media signals
 Tweets, Comments on FB, IMDB, YouTube
 Push through Sentiment Analyzer
 Tokenize (words capture) create words from those streams
 Classify (estimate) as Positive | Negative
 Provide actionable insight
 Improve sort order of recommendations
 Alert / advise Digital Marketing team
Recommendations

37. Training
Positive Negative
Knowledge
Base

38. Segment Classify
Segment Classify
Segment Classify
Model
Training
Positive Negative
Stream
Ingest
Stream
Ingest
Stream
Ingest
Knowledge
Base

39. Segment Classify
Segment Classify
Segment Classify
Model
Training
Positive Negative
Stream
Ingest
Stream
Ingest
Stream
Ingest
Knowledge
Base
“I adored this
movie”
“adore” =
POSITIVE

40. GNIP
Datasift
Other
Positive Negative
Amazon
Kinesis
Amazon
Kinesis
Amazon
Kinesis
Model Training &
Storage
Stream
Ingest
Sentiment
Classification
Amazon
Machine Learning
Trending Sentiment
Neutral Negative Positive

41. GNIP
Datasift
Other
Positive Negative
Amazon
Kinesis
Amazon
Kinesis
Amazon
Kinesis
“I adored this
movie”
Extract
Features
Classify
Extract
Features
Classify
Extract
Features
Classify
Model
Training
“adore” =
POSITIVE

42. • In addition to traditional ABR strategies, anticipate consumer demand for
media downloads
• Based on viewing patterns
• Time of day / season
• Initiate retrieval process ahead of time
• Restore from Amazon Glacier
• Retrieve from Amazon Simple Storage Service (S3) / Amazon CloudFront and
cache on device (STB, Mobile/Native Player App)
Other Applications: Minimize Buffering

43. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
AWS Partner Eco-system for
Content Analytics

44. AWS Partners: Analytics Eco System

45. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
IMAGINEERING
TRANSFORMED
TOMORROWS!

46. www.pi-stats.com
Turnkey
Solutions
Media & Entertainment

47. www.pi-stats.com
Analytics &
Recommendation
CMS
Digital Asset
Management
Mobile Apps

48. www.pi-stats.com
CMS
SCALABLE, SERVER-LESS
ARCHITECTURE
Cost reduced
>50%
300+ mn
pageviews
6mn
unique user
6 different languages
MULTILINGUAL

49. www.pi-stats.com
MOBILE APPS

50. www.pi-stats.com
Cost reduced >90%
Android and iOS
Beautiful modern
design
More than 6 million
downloads 67% new users
acquired
MOBILE APPS

51. DIGITAL ASSET MANAGEMENT
DAM
Content Aggregation
& PACKAGING
OTT – VoD /
Catch-up /
LiveTV
DRM
Multi-format
ENCODING

52. www.pi-stats.com
ANALYTICS

53. www.pi-stats.com
Real Time dashboards
Easy integration for Mobile and Web
Smart push notifications
Predict Trend and Virality
CMS based Analytics
ANALYTICS

54. www.pi-stats.com
RECOMMENDATIONS
Real Time recommendations
Personalized content via API
Multilingual capabilities
Hybrid Pluggable

55. www.pi-stats.com
Elastic
Cache
S3
Amazon
ElasticSearch
EC2 RDS Elastic
Transcoder
RedShiftKinesis Lambda
EMR
TECHNOLOGIES

56. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
ABP LIVE
OTT & VIDEO ANALYTICS

57. INTRODUCTION
ABP News network
▸ ABP NEWS previously known as STAR NEWS
▸ Largest regional news network in India
▸ Available in 5 Indian languages Hindi, Punjabi, Marathi, Bengali,
Gujarati
▸ Weekly reach of 48 million users

58. INTRODUCTION
▸ Digital initiative by ABP NEWS
▸ News (abplive.in), Entertainment (FilmyMonkey.com), Cricket
(WahCricket.com)
▸ 300 million views a month across web and mobile
▸ 14 million monthly Video views
▸ Mobile Apps for iOS, Android, Windows
ABP Live

59. OTT AND VIDEO ANALYTICS
▸ In house developed OTT platform
▸ Manage, publish, measure, monetize video content on web and
mobile
▸ Deployed over AWS services
▸ EC2, S3, Elastic Transcoding, Elastic Search
ABP Live: OTT Platform

60. OTT AND VIDEO ANALYTICS

61. OTT AND VIDEO ANALYTICS

62. OTT AND VIDEO ANALYTICS

63. OTT AND VIDEO ANALYTICS

64. OTT AND VIDEO ANALYTICS
▸ In house developed Video Analytics system
▸ Measure reach, usage and Quality of Service across devices
▸ Deployed over AWS services
▸ API Gateway, Kineses, S3, Redshift, EMR
ABP Live: Video Analytics

65. OTT AND VIDEO ANALYTICS

66. OTT AND VIDEO ANALYTICS

67. OTT AND VIDEO ANALYTICS

68. OTT AND VIDEO ANALYTICS

69. OTT AND VIDEO ANALYTICS
▸ Quality of services across devices
▸ Measure daily reach over web and mobile devices
▸ Realtime analytics of Live and VoD users
▸ Realtime content categorisation of trending videos
Video Analytics

70. OTT AND VIDEO ANALYTICS
▸ Scalability
▸ Elasticity
▸ Realtime Video Encoding
▸ Easy deployment
▸ Cost effective: Pay only for what you use
Why AWS

71. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved

72. ©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved