ibm audience analytics for broadcasters and tv networks
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© 2015 IBM Corporation
IBM Audience Insightfor Broadcast/Cable Networks
© 2015 IBM Corporation2
Agenda
introductions
industry highlights
audience insight opportunity
cable TV and broadcast views
the solution
why IBM?
© 2015 IBM Corporation3 © 2015 IBM Corporation3
Industry Insights
© 2015 IBM Corporation4
Dramatic Change
75% of people watchstreamed on-demand video
several times a week,
compared to 77% who watch
scheduled broadcast TV several times a week.
Average Net Promoter Score for OTT
on-demand services in the US is 39,
compared to just 12 for traditional
TV providers.
streaming is closing in on linear TV
strong preference emerging for on-demand
Source: http://www.ericsson.com/res/docs/2014/consumerlab/tv-media-2014-ericsson-consumerlab.pdf
© 2015 IBM Corporation5
marketing and advertising landscape is changing
of TV ad dollars will be spent via
programmatic TV products by 2018
20%
Gartner Hype Cycle 2015, eMarketier 2015
2015 spend on digital advertising by US
M&E industry
$6.2B
Make every advertising dollar count bypersonalizing the consumer experience
© 2015 IBM Corporation
© 2015 IBM Corporation6
TV and video ads need to become more relevant, more personalized, and less intrusive.
Personalized ads perceived as more helpful.
30% want tailored service
offerings
30% are willing to pay to get rid of ads
40% would like to
actively specify the
ads they want
30% want personalized
recommendations
for content
50% say removing
ads is very important
© 2015 IBM Corporation7
Gartner urges clients to continue to closely track the progression of social network analysis, social media distribution and other metrics, dynamic
video ad insertion, and automatic content recognition (ACR)
- Gartner 2014
Analyst Insights
Digital consumer spending will overtake traditional consumer spending in 2015 and will be 26 percent larger by 2018.
- McKinsey & Company 2014
© 2015 IBM Corporation8
Social media’s influence is growing• 85% of people who tweet during primetime hours reported tweeting
about TV• 72% tweet while the show is on live• 60% tweet about shows when they are not actually watching them• 58% tweet about TV shows while they watch on time-shifted platforms• 90% took subsequent action such as watching a show after seeing TV-
related tweets
Source: https://blog.twitter.com/2014/study-exposure-to-tv-tweets-drives-consumers-to-take-action-both-on-and-off-of-twitter © 2015 IBM Corporation
© 2015 IBM Corporation9
Consumer experience and the trail of data is changing
9
360º View of the
Consumer
ERP and CRM Systems
Traditional Structured Data
Marketing Data
3rd-Party Audience / Market Research
Non-Traditional Data
Consumptionvia STB, VOD, IPTV, DVR
Public Data
Online Purchases& Interactions
Social Media
Behavioral Textual /Reviews
Audio/Video
Non-TraditionalUn-Structured Data
Email/Chat Correspondence
© 2015 IBM Corporation10
In this environment, driving differentiation with consumers through trust and relevance
is important
collect feedback social media analytics+
analytics enables you to know and treat consumers as individuals
engage
+
© 2015 IBM Corporation11Source: Forbes
Consumer Centric Approach
11
Understand how distribution windows impact content and ad valueIncreased information transparency between departmentsReal time visibility into ad inventory and ad valueUnderstand content preferences to tailor recommendations and ads
Access to 360-degree consumer profiles
New profits from the highest value customers
© 2015 IBM Corporation12
$8M new revenue
Cable TV company in the U.S. generated more
than $8M USD in new revenue from enhanced
DVR services and increased advertising
reach by 12%
of advanced analytics…the results
Near 100% accuracy
Leading online advertising network increased
accuracy to nearly 100% by analyzing complete
data sets, giving customers improved
projection of campaign performance
80% more ad revenue
Major publisher uses advanced analytics to
provide real-time insights into audience preferences, increasing total advertising
revenue by 80% in one year
© 2015 IBM Corporation13 © 2015 IBM Corporation13
The Opportunity for Audience Insight
© 2015 IBM Corporation14
Analytics represents an opportunity to drive value across multiple business functions
Experience
What are my consumers saying?
Monetize
How much is my content worth?
Create
What innovative content appeals to each segment?
Life
cycl
e
Distribute
On what platforms should I distribute?
Market
Am I generating the right message for target consumers?
© 2015 IBM Corporation15
•Predict outcomes (sales, views)
•Optimize the media mix
•Predict behavior
• Improve reach to target segments
• Increase campaign conversion
• Enable personalization
•Identify consumption drivers•Drive engagement, reduce churn•Calculate lifetime value
• Know audience characteristics/preferences
• Realize increased content value
• Improve targeted advertising
Consumer Targeting
Audience Forecasting
360º Consumer Profiling
Engagement & Churn
Analytics
Audience Insight is enabled through a suite of advanced data integration and analytical capabilities
© 2015 IBM Corporation16
Capability: 360 Consumer Profiling
360º consumer profiles audience micro-segments
3rd Party Audience
Digital Interactions
3rd Party Consumption
CRM & Call Center
Social Media
extract data/build profiles
entity marketing micro-segmentation
segment mapping
audience data modeling
Develop multi-platform audience data model
Match/de-duplicate consumers across sources/platforms
Aggregate individuals to form micro-segments
Map segments across multiple sources
Extract data from unstructured data to build profiles
Move beyond basic demographics to deep psychographics, interests & preferences
© 2015 IBM Corporation17
Capability: Measuring Engagement & Churn
segment consumers
define/extract variables
develop model
predict and respond
integrate and model data
Integrate all relevant data into a common model
Identify predictive variables by segment & extract
Develop models to measure & predict engagement/churn
Run model by segment & identify optimal response
Use statistical methods to segment consumers
3rd Party Attributes
Mobile & Web Behavior
Historical Behavior
CRM & Call Center
Social Media
segments & variables engagement & churn modelUnderstand & predict behavior to drive growth & retention strategy
© 2015 IBM Corporation18
Capability: 360 Consumer Profiling
engagement by segment targeted content delivery
measure engagement
develop target models
determine best action
Integrate & delivery
define target segment
Define target segments based on 360º profiles
Develop propensity, look-alike and optimization models
Run segments through models to find best action
Integrate with ops systems to deliver targeted content
Measure historic engagement across platforms
Target consumers across platforms & enable dynamic content delivery
EMM & campaigns
Mobile app & geo location
multi-platform engagement
360º profiles
Social Media
© 2015 IBM Corporation19
Capability: Forecasting and Optimization
predictive variables forecast & optimization
define/extract variables
develop models Tune & train models
predict & monitor
integrate & model data
Integrate all relevant data into a common model
Develop forecasting & optimization models
Utilize data to train model & test scenarios
Run model to predict and monitor results
Identify predictive variables & extract by source/segment
historical behavior
external market
multi-platform engagement
360º profiles
Social Media
Utilize historic data & real-time market signals to predict performance
© 2015 IBM Corporation20 12
is a radical departure from business-as-usual &
game-changing capabilities across the
content lifecycle
The resultSmarter
MarketingSmarter Content
Smarter Distribution
Smarter Advertising
Differentiated Experience
© 2015 IBM Corporation21 © 2015 IBM Corporation21
Cable Industry Use Cases
© 2015 IBM Corporation22
Cable: core use casesUse Case Challenge Solution
Churn Reduction
Declining revenues due to spikes in churn in the first 90-days and at promotion roll-off.
Analyze data for indicators of churn; develop predictive models to identify candidates and target them with retention offers.
Targeted Cross-Sell & Up-Sell
Lack of revenue growth due to inability to identify candidates for cross-sell & up-sell and deliver relevant offers.
Build rich customer profiles based on consumption behavior & look-alike modeling to target cross/up-sell offers to segments.
Targeted Advertising
Operations and IT are not equipped to resolve data quality & PII issues required for targeted ads.
Establish a secure analytics environment to integrate subscriber, STB and third-party, enriched data.
Avoidable Truck Rolls
Dispatching trucks to customer locations has high operational cost and is not always necessary.
Develop a model to predict avoidable truck rolls using care, network and billing data and divert requests to alternative challenges.
First-Call Resolution
Customer communications are not integrated across channels leading to redundancy and poor service.
Implement intelligent call routing to diagnose root cause of inbound calls using IVR and other structured & unstructured sources.
© 2015 IBM Corporation23
Potential value exceeds $100M annually
Boost CPM Manage
yield & stewardship
Pote
ntia
l Val
ue to
MVP
D (M
illio
ns)
Increase Subs
Grow ARPU Total Annual
Value From Analytics
Reduce onboarding churn
Reduce churn at promotion roll-off
Increase cross-sell & up-sell
Improve campaign conversion rates
Assumptions:Based on analysis of publically available data from SEC filings (Comcast, TWC, Charter, DirecTV, Dish). Benchmarks based on IBM analysis, case studies and third-party research.
Boost Ad SalesReduce
OpEx Reduce
contact center costs
Eliminate avoidable truck rolls
© 2015 IBM Corporation24
reduced churn with targeted offersNeed•unusual spike in customer churn during onboarding & promotion roll-off•little visibility into which customers to target with retention offers
Benefits•Churn predictors identified•Put focus on high-value target segments•Integrated models into marketing system to dynamically deliver retention offers
© 2014 IBM Corporation
Case Study:Leading cable provider
© 2015 IBM Corporation25 © 2015 IBM Corporation25
Broadcast TV Industry Use Cases
© 2015 IBM Corporation26
Use Case Challenge Solution
Influencer Identification
Bulk of audience is unknown making it difficult to target campaigns that drive tune-in and the water-cooler effect
Integrate social media data to identify influencers & target them with campaigns
Fan Scoring & Targeting
Lack of visibility into audience engagement week-to-week (acquisition, retention, churn)
Combine 1st & 3rd party data sources with recency/frequency models to measure engagement, churn and target campaigns
Campaign ROI Analysis
Inability to gauge campaign effectiveness & ROI on promo media & marketing spend
Correlate direct & indirect stimuli from linear & non-linear sources over time to assess the impact of campaigns on tune-in
TV Everywhere App Analysis
Traditional measurement tools are not equipped to handle direct consumer interaction data from apps
Integrate app streams into measurement models to assess adoption, authentication, time spent and other KPIs
Audience Micro-Segmentation
Traditional sources of audience data do not cut across platforms or support the deep demographics that advertisers covet
Integrate multiple sources of 1st & 3rd party, enriched data, and social, to build 360º profiles and custom segments
Ad Yield Optimization
Lack of transparency & integration between linear & digital ad platforms results in yield inefficiencies
Manage inventory yield & stewardship to satisfy contract obligations & maximize effective CPMs
Broadcast TV: core use cases
© 2015 IBM Corporation27
Potential value exceeds $65M annually Boost
effective CPM Manage yield &
stewardship
Pote
ntia
l Val
ue to
Net
wor
k (M
illio
ns)
Improve Efficiency
Grow Audience
Total Annual Value From Analytics
Elimination of redundant platforms, tools, sources
Improved reporting cycle times
Target addressable audience
Drive increased tune-in on primetime
Assumptions:Based on analysis of publically available data from SEC filings (CBS, NBCU, Fox, Disney Networks, Viacom, Time Warner). Benchmarks based on IBM analysis, case studies and third-party research.
Maximize Value
© 2015 IBM Corporation28
built an audience insightintegrated platform Need•data sources stuck in organizational siloes & data fragmented across platforms •limited visibility into newly launched TV Everywhere app •long cycle times for audience research and sell sheet creation for ad sales
Benefits•integrated 40 viewing data sources •developed data discovery & analytics•enabled rapid sell sheet creation •eliminated spend on third-party data
Case Study:Leading US Broadcaster
© 2015 IBM Corporation29 © 2015 IBM Corporation29
Studios Use Cases
© 2015 IBM Corporation30
Studios: core use casesCaseUse Case Challenge Solution
Film Budget Allocation
Rising production & marketing costs require a more targeted approach for marketing strategies: mass-market or niche, genre release
Analyze ROI to optimize spend for production and marketing budgets
Creative Tuning
Increasing reliance on big budget tentpole releases means high stakes for studios
Analyze trend and chatter to adjust creative elements to increase commercial viability
Greenlight Analysis
Increasing reliance on big budget tentpole releases reduce number of films released and increase stakes associated with greenlighting
Analyze creative elements against historical data & current trends to understand demand for films
Word of Mouth Optimization
Rapid spread of positive or negative sentiment via social media and fan sites determine box office success, despite marketing efforts
Combine 1st and 3rd party data sources to identify and target key social influencers
Window Optimization
Falling home entertainment revenue means more pressure to succeed in the theatrical window in order to drive downstream sales
Manage content inventory yield across release windows and geographies to maximize lifetime revenues
Licensing Optimization
Digital and video-on-demand capabilities are changing consumer behavior, and challenging traditional release windows
Analyze Back Catalog to identify optimum pricing and bundles to increase revenue from all licensing channels
© 2015 IBM Corporation31
Potential value exceeds $80M annually or $5M per film real value is in ability to identify and avoid major flops
Drive demand for long tail content
Optimize distribution
Pote
ntia
l Val
ue to
Net
wor
k (M
illio
ns)
Improve Efficiency
Grow Audience Total Annual
Value From Analytics
Budget allocation optimization
Reduced costs and increased investment yield
Grow foot traffic through targeted advertising
Identify new audiences
Assumptions:Based on analysis of publically available data from SEC filings (Fox, Disney, Paramount, Warner Brothers, Universal Studios). Benchmarks based on IBM analysis, case studies and third-party research. Does not include uplift from Green Light Analysis, Creative Tuning, and Window Optimization
Maximize Value
© 2015 IBM Corporation32
uses social indicators to predict opening box officeNeed•Rising production and marketing costs created high stakes; must recoup costs•Increasing reliance on big budget tent pole films meant fewer movies with larger investments•Marketing efforts had limited targeting
Benefits•Analytics predicted opening weekend box office within with 72% accuracy •Identified most influential social channels to drive targeted media investment
Case Study:Top grossing film studio
© 2015 IBM Corporation33
Technical & operational limitations within current data architecture
Master Data Management
De-duplicated customer informationReference data & cross-system code mappings
Master Data Repository
Data Security & Governance
Data lineage & impact analysisData privacy & security
Warehousing
•High-concurrency historical queries
•Limited granularity
•Long-running data processing
EDW
Analytics & Reporting
Zone
Data Integration
•Batch (daily) movement•Only structured data
Batch Reporting
Limited, DisjointedSearch & Discovery
LimitedDescriptive& Predictive
Models
Metadata Repository
Marts
ODS
•Granular data•Limited history
Limited Business Actions
Siloed DataConnectors
CRM
Marketing
App usage
Social Media
Location
Behavioral
Limited Targeting
Mediocre Customer Experienc
e
Consumption
ERP
© 2015 IBM Corporation34 © 2015 IBM Corporation34
The SolutionBehavior Based Audience Insight
© 2015 IBM Corporation35
Predict and decide the best action
Cognitive computing
Intuitive analytics for everyone
Analytics as and when you need it
TRADITIONAL APPROACH
the realm of the specialist
BIG DATA & ANALYTICS APPROACH
embedded in everything
TRADITIONAL APPROACH
Scheduled
BIG DATA & ANALYTICS APPROACH
Real-time
TRADITIONAL APPROACH
Pre-programmed analysis on structured data
BIG DATA & ANALYTICS APPROACH
Learn to sense and predict using all types of information
TRADITIONAL APPROACH
What has happened and why
BIG DATA & ANALYTICS APPROACH
What will happen and what should you do
© 2015 IBM Corporation36
Embracing new paradigms to be more consumer-centric
360º Consumer Profiling
Engagement & Churn Analytics
Consumer Targeting
Forecasting & Optimization
Topic-Based Influencer Targeting
Real Time Mobile Campaigns
Multiplatform Measurement
Engagement Scoring
Ad Yield Optimization
Demand Forecasting
Profile Entity Matching
Psycholinguistic Profiling
Match millions of social media and CRM profiles to develop audience micro-segments
Normalize data across platforms to get integrated view of consumer behaviors
Classify viewers based on engagement to compare growing/waning passion
Build cross channel optimization models for targeted ads to maximize yield of sold ad inventory
Identify significant predictors of consumer demand in order to develop forecasts
on new media content
Trigger timely, targeted mobile promotions based on real-world events
Identify most influential audience members for specific topic and re-target look alikes
Analyze text posts to analyze & extract underlying intrinsic psychographic traits,
value, & needs
© 2015 IBM Corporation37
A Next Generation Advanced Analytics Platform New / Enhanced
ApplicationsAll Data
ERP
CRM
Marketing
App usage
Social Media
Location
Subscriber profile
Multi-Channel Measurement
Campaign Management
Ad Sales Optimization
Churn Management
Distribution Optimization
Content Recommendations
IBM Watson Foundations
Big Data & Analytics Strategy, Integration & Managed Services
Big Data & Analytics Infrastructure
What’s happening?Discovery
Why did it happen?Reporting &
analysis
What could happen?Predictive analytics
What’s best?
Cognitive
What action should I take?
Decisions
Information Integration & Governance
Landing, Exploration & Archive data zone
EDW & data mart
zone
Operational data zone
Real-time Data Processing & Analytics
Deep Analytics data zone
Consumption
© 2015 IBM Corporation38
Advanced Analytics Platform core technical componentsNew / Enhanced
ApplicationsAll Data
ERP
CRM
Marketing
App usage
Social Media
Location
Subscriber profile
Multi-Channel Measurement
Campaign Management
Ad Sales Optimization
Churn Management
Distribution Optimization
Content Recommendations
IBM Watson Foundations
Big Data & Analytics Strategy, Integration & Managed Services
Big Data & Analytics Infrastructure
What’s happening?Discovery
Why did it happen?Reporting &
analysis
What could happen?Predictive analytics
What’s best?
Cognitive
What action should I take?
Decisions
Information Integration & Governance
Landing, Exploration & Archive data zone
EDW & data mart
zone
Operational data zone
Real-time Data Processing & Analytics
Deep Analytics data zone
Consumption
Stream Computing
Hadoop Analytics
Data Collection/ Integration
Discovery Platform
Business Intelligence
Decision Management
Predictive Analytics
Cognitive NLQ
Data Warehouse
Marketing Optimization
© 2015 IBM Corporation39
Platform Capability IBM Products How It Powers IBM Audience Insight SolutionData Collection & Integration
Infopsphere Information Server
Tap into core content delivery networks to collect, integrate, and analyze click stream or other IP based data
Stream Computing & Analytics
InfoSphere Streams Provide real-time triggers for ad targeting or marketing campaigns based on audience media behavioral patterns
Hadoop Analytics System InfoSphere BigInsights Analyze millions of social media posts to extract and build audience demographic, lifestyle, and brand affinity profile attributes.
High Performance Data Management & Warehouse
IBM PureData System Extremely fast “slicing and dicing” of consumer/audience behavioral data and deployment of audience micro segmentation models
Discovery & Exploration Platform
Watson Explorer Enable business user search, browse, and filtering of audience segments to compare/contrast across media content (TV Shows, etc.)
Business Intelligence Cognos BI Analyze and track operational or sales data from marketing campaigns or ad operations or sales systems
Cognitive NLQ Watson Analytics Perform natural language queries to explore or identify evidence to formulate hypothesis for content performance or audience response.
Predictive Analytics SPSS Develop segmentation, clustering, affinity analysis and build models to support demand forecasting, targeting, and recommendations
Decision Management SPSS, iLog Determine suitable promotional campaign or ad for target audiences
Marketing Optimization IBM Campaign Design & execute marketing campaigns tailored to needs & preferences
IBM’s Analytics Platform includes these “best of breed” components
© 2015 IBM Corporation40
Data WarehouseDB2 with BLU Acceleration, BLU Acceleration for Cloud DB2 Analytic Accelerator PureData System for Analytics PureData System for Operational Analytics Industry Models
Landing, Exploration and Archive data zone InfoSphere BigInsights for Hadoop PureData System for Hadoop Content Manager Case Manager Content Navigator
Information Integration & Governance InfoSphere Optim InfoSphere Guardium InfoSphere Data Privacy for Hadoop InfoSphere Information Server InfoSphere Data Replication InfoSphere Federation Server InfoSphere Master Data Management Cognos Command Center
Reporting and analysis Cognos Business Intelligence Cognos Express Business Intelligence Pattern Cognos BI Pattern with BLU Acceleration
Real-time Data Processing & Analytics SPSS Modeler Gold Operational Decision Manager ILOG CPLEX Optimizer Decision Optimization Center InfoSphere Streams InfoSphere Sensemaking
Discovery & Exploration Watson Analytics Watson Explorer SPSS Analytic Catalyst & SPSS Analytic Server InfoSphere Business Information Exchange
Content Analytics Content Analytics SPSS Data Collection Social Media Analytics
IBM Watson Foundations Architectural Product Portfolio
Planning & Forecasting Concert Cognos TM1 Cognos Insight Cognos Express Cognos Controller
Operational data zone DB2 with BLU Acceleration IMS PureData System for Transactions Informix, Informix TimeSeries InfoSphere Master Data Management
Predictive analytics and modeling SPSS Statistics SPSS Modeler Concert Cognos TM1 Cognos Insight Cognos Express Cognos Controller
© 2015 IBM Corporation41 © 2015 IBM Corporation41
Why IBM?
© 2015 IBM Corporation42
Why choose IBM for media and entertainment solutions?
Industry expertiseBuilt for the M&E industry
Unparalleled analytics expertiseResearch Analyst Market leader
Advanced analytics and integrationdeliver a single view of analytics across the enterprise
Commitment to innovationIBM global research and service organizations
Flexible deployment options faster time to value and lower cost of ownership
Over $16B in information & analytics related acquisitions of “best of breed” technologies
Patented Unification of Descriptive, Predictive and Prescriptive Analytics APIs Enable Simplified Application Development
Spent $50B on R&D in the last decade
On-premise, SaaS, Cloud, Hybrid
Global clients representing $6b+ business in Telecom, ISPs, Cable TV, Direct to Home Satellite, Broadcast Networks, related segments
© 2015 IBM Corporation43
IBM Research unique inventions that will bring great business valueIntelligent Customer
ProfilesPsycholinguistic
AnalysisLife Event Detection
Behavioral Pricing
© 2015 IBM Corporation44
Let’s get started
Add to roadmap & secure investment
Design & implement at enterprise scale
Integrate with operational systems
Test performance Train/enable users Operational go-live /
bring to market
Implement / Scale
Define desired outcomes
Gather high-level requirements
Scope POC Quantify value
potential & estimate costs
Validate assumptions
Determine ROI & metrics for tracking
Go/no-go on POC
Scope & Value Assessment
Identify potential analytics initiatives
Define business value
Describe business & technical capabilities
Prioritize based on value and ease of execution
Assign initiative owners and SME’s
Analytics Discovery
Establish discovery environment
Define requirements & solution design
Ingest required data & develop POC
Test output against success criteria
Evaluate results and prove the value
Go/no-go on implementation
Pilot / POC
© 2015 IBM Corporation45
© 2015 IBM Corporation46
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