[AWS LA Media & Entertainment Event 2015]: Cloud Analytics for Audience Engagement
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Transcript of [AWS LA Media & Entertainment Event 2015]: Cloud Analytics for Audience Engagement
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©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved
Cloud Analytics for Audience Engagement
Mike Limcaco | AWS Principal Solutions Architect Mick Bass | 47Lining CEO
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http://mashable.com
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Search
Watch
Listen
Play
Download
Purchase
Rate It
Review It
Sharing
Tagging
Bookmarking
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GB TB PB
ZB
EB
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65M+ Subscribers 50 Countries
1000+ Devices 10B+ Hours/Quarter
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Netflix: Over 75% of what people watch come from recommendations [1]
[1] http://bit.ly/1WIInoh
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Machine Learning for Predictive Analytics
• 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
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A Few Machine Learning Tasks
Recommendations
Clustering
Classification
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Why Cloud?
• Scale • Adaptability • Agility
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(Some) Big Data Services
Amazon S3
Internet scale storage
Amazon Machine Learning
Hosted predictive analytics service
Amazon EMR
Hosted Hadoop framework
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Maximizing Audience Engagement
• Recommendations
• Ad / Marketing Campaign Targeting – Sentiment analysis – Automated segmentation
• Predict audience churn
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Examples
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Recommendations
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The Concept
ü Capture Audience Signal Data ü Create history of user and item preferences
ü Estimate similar users and items ü Record these in Search Engine ü Query Search Engine with User History
ü Enjoy recommendations!
http://www.slideshare.net/tdunning/recommendation-techn
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Log Storage
ETL
User Interface
Serving Layer
users
Recommend Engine
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users
Media platforms
Mobile
Search Play Buy Rate
Recommendations
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Apache Mahout on Amazon EMR
• Library of scalable machine-learning algorithms
• Single-node as well as distributed capabilities
– Hadoop Map-Reduce (BATCH | OFFLINE) – Evolving to support other execution platforms
(NEAR-REALTIME) • Apache Spark • H20
Spark H20
Recommendation Clustering Classification
Math Library
Hadoop
Map-Reduce
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mike,view,movie-a mike,view,movie-b mike,view,movie-c mike,buy,movie-b chris,view,movie-b chris,buy,movie-d …
movie-b movie-c:2.772588722239781 movie-a:2.772588722239781
movie-d ….Indicators
(“Items Similar To This….”)
% mahout spark-itemsimilarity -i input-folder/data.txt -o output-folder/ --filter1 buy -fc 1 -ic 2 --filter2 view
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Step 1: Logs à History Matrix
User1 Thing1 User2 Thing2 User3 Thing3 User2 Thing4 User5 Thing1 User1 Thing2 User1 Thing3
Mike
Jon
Mary
Phil
Kris
Logs History Matrix
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Step 2: Estimate Similar Things
History Matrix
2 8
2 4
8
4
Item-Item Matrix
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Step 3: Reduce to Interesting Pairs
2 8
2 4
8
4
Item-Item Matrix
LLR
Indicators (“Items Similar To This….”)
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Step 3: Reduce to Interesting Pairs
Indicators (“Items Similar To This….”)
Items Similar To This
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Step 4: Store Indicators in a Search Engine (BATCH)
Superman Highlander, Dune
Star Wars Raiders, Minority Report
Highlander Superman Mulan Home Alone,
Mermaid Star Trek … … …
4587 223, 5234 748 5345, 235 12 8234 245 9543, 7673 3456 4587 … …
Index
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Indicators
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Step 5: Query Search Engine w/ User History (REALTIME)
748 Star Wars 45, 235 12 Highlander 8234 245 Mulan 9543,
7673 4587 Superman 12, 5234 3456 Star Trek 2458 …
Query
“12”
5345
3456
12
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Sentiment Analysis
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“I thought Episode 29 was not without merit J”
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Is this Positive or Negative?
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The Concept
ü Capture social media signals ü NRT Streams (Tweets, Comments on FB, IMDB, YouTube …)
ü Push through Sentiment Analyzer ü Tokenize ü Classify (estimate) as Positive | Negative
ü Provide actionable insight ü Improve sort order of recommendations ü Alert / advise Digital Marketing team
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Training
Positive Negative
Knowledge Base
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Segment Classify
Segment Classify
Segment Classify
Model Training
Positive Negative
Stream Ingest
Stream Ingest
Stream Ingest
Knowledge Base
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Segment Classify
Segment Classify
Segment Classify
Model Training
Positive Negative
Stream Ingest
Stream Ingest
Stream Ingest
Knowledge Base
“I adored this movie”
“adore” = POSITIVE
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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
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Sentiment Training Sets
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Case Study: OTT Predictive Analytics
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UsingDataforStrategicAdvantage
Proof-of-ConceptOTTservicesprovidetheopportunitytoestablisha“conversa.onwiththecustomer.”BigdatatechniquescanbeappliedtofusemassiveamountsofOTTlogs,3rdpartydemographics,andsocialmediasen<menttogaininsightintotheaudienceanddeveloppredic.vecapabili.es.47LiningandAmazonWebServicespartneredtodevelopaPoCforanOTTcustomertouseamachine-drivenapproachtoaccuratelypredictuserbehaviorwithintheirconsumervideoapplica<on.
100MConsumerinterac9ons
MillionsofUsers
3rdPartyDemographics
10KTitles
SocialMedia
Fuse/Visualize
5DataSources*
Per-SegmentRecommenda9ons
Predictors Enablers
71%AccurateChurn
Enhanceaudience
engagementthroughrelevantoffers
ResultsScalability
Automa9on
Agility
Cost
effec9veness
MachineLearning
*OrderofMagnitudeforPoCScope
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Speed,Agility,Scalability
WhyCloud?
38
AWSMachineLearning.Delivered71%predic=veaccuracyforuserchurnduringPoC.
Hardware.Cloudeconomicsallowustoonlypayforwhatweuse.
RedshiC.Petabyte-scaledatawarehouse.Effec9velyfeedsMachineLearningorEMR.
Elas=cMapReduce.Unparalleledspeedandscaleforbigdatarecommenda9ons.
Managedservicesthat“justwork”providingspeed,agilityandscale
Pre-CloudChallengesNeedtoprocure
hardwareforpeaks
ProprietarymachinelearningsoTwareonlyDatascien9stscanuse
Needtoprocuredata
warehouse
Always-on,on-premisehadoopclusterswith
highmanagementcosts
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PoCArchitecture
39
OTTApp
S3
RedshiT
MachineLearning
Transforma9ons
PredictorServices
BusinessIntelligenceToolsVisualiza9ons&Dashboards
Perio
dic
3rdPartyDemographicData
Sen9mentAnalysis/SocialData
S3
TitleData
AWSMLAPIMahout
ALSRecommender
Elas9cMapReduce
RAnalysisSandboxes
Lessthan~$1Kininfrastructure
AWS CloudFormation
template stack
Logs
Periodic
Automa<onEnvironmentcanbereplicatedusingNucleator,AnsibleandCloudForma9on
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ThePowerofKnowingyourUsers
40
Per-AccountSignatures
ClusteringDimensions
DistanceHeuris9c
ProfilingDimensions
ClusterAnalysishierarchical|model-based
SegmentAnalysis
…
…
…
1
2
n
segments çCohortsè
100MConsumerinterac9ons
MillionsofUsers
3rdPartyDemographics
10kTitles
SocialMedia
AccountSignatureDefini9ons
6dis<nctuserpersonasemerged
*OrderofMagnitudeforPoCScope
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TurningUserKnowledgeintoEngagement
41
1
2
n
segments
Per-SegmentRecommenda9ons
Predictors
71%AccurateChurn
AccountChurnPredictor.Usingtheiden9fiedSegmentswithAmazonMachineLearning,wedevelopedamodeltopredictwhenanaccountisatriskforchurn.Accuratepredic9onsenablemeaningfulofferstousersbeforethisoccurs.
ViewedContentPredictor.
Usingtheiden9fiedSegments,weusedRedshiC,RandAmazonElas=cMapReduce/Mahouttopredictcontentthatusersmayfinddesirablethatalsoachievesengagementobjec9ves.Suchrecommenda9onsenableproac9vesculp9ngofuserbehavior.
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Realizinga“S9ckiness”StrategythroughData
42
AWS’speed,agilityandscalabilityareanaturalfitforOTTpredic9veanaly9cs.
CustomerStrategyAc.onWithcapabili<esdemonstratedandde-risked,customerisintegra<ngpredictors
intotheir2016userexperience.De-riskingcost~$1kininfrastructure
71%accuracyinchurnpredic9on Reten9onoffersin2016
Per-PersonaRecommenda9ons Keepusersgluedin2016
PoCwasonlypossibleintheCloud
Knowyourcustomer
S9ckiness=abilitytosteertheconversa9onwithyourcustomer
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Summary
43
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Summary
• Audience demands increasingly personalized content • We want to understand and predict these needs and
adapt discovery & delivery of media • Audience interactions can be analyzed to
– Surface patterns of common behavior – Estimate or predict audience demand / churn
• AWS enables tools & techniques for scalable machine learning