This document summarizes an executive webcast about extracting value from big data in digital media. It discusses how big data analytics can help digital marketers achieve their goals of a single customer view, increased targeting precision, improved relevance, and higher campaign profitability. The challenges of fragmented customer data from multiple online and offline sources are also outlined. It promotes IBM's Netezza big data platform and analytic solutions for consolidating, segmenting, matching, and optimizing large amounts of structured and unstructured customer data in real time to drive better marketing outcomes.

1. Extracting Big Value from Big Data in Digital Media
An Executive Webcast with Aberdeen Group, June 2012
Nathaniel Rowe Krishnan Parasuraman
Research Analyst CTO, Digital Media
Enterprise Data Mgmt. Netezza and Big Data Solutions
@kparasuraman
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7. Digital Marketer’s Goals Key Challenges
1 Single view of customer Customer information fragmented
across channels – Online, Apps,
Social, Mobile
2 Increase Targeting Precision
Behavior is a much better indicator
of intent than demographics
3 Improve Relevance Customers expect personalized services,
have greater access to devices
4 Drive up campaign profitability Audience attention is fragmented yet
contention for eyeballs is increasing
© 2012 IBM Corporation

8. Digital Marketer’s Goals Solution Capabilities
Clickstream
Consolidation
1 Single view of customer
Transactions
All in one
Social Events
place
CRM
Support calls
Increase Targeting Precision
Segmentation
2 Clustering
Scoring
Feature Selection
Associations
3 Improve Relevance Personalized message
Matching
Matching algorithms
Matrix computations
Single Value Decomp.
4 Drive up campaign profitability
Optimization
Forecasting
Predictive algorithms
Decision trees
Linear Regression
© 2012 IBM Corporation

9. Digital Marketer’s Goals Solution Capabilities
Clickstream
Consolidation
1 Single view of customer
Transactions
All in one
Social Events
place
CRM
Support calls
Increase Targeting Precision
Segmentation
2 Clustering
Scoring
Big Data Feature Selection
Associations
3 Improve Relevance
Platform Personalized message
Matching
Matching algorithms
Matrix computations
Single Value Decomp.
4 Drive up campaign profitability
Optimization
Forecasting
Predictive algorithms
Decision trees
Linear Regression
© 2012 IBM Corporation

10. Big Data Platform Requirements
Analyze Extreme Volumes of Data
Impressions
Online, Offline, Social, Behavior, First Party &
Cookies Third Party across multiple channels
Online
Registrations
Purchase Transactions Analyze Wide Variety of Data
In-Market Intent Structured – POS, 3rd Party, Transactions
Unstructured – Social, Video, Blogs
Influence
Semi-Structured – Cookies, Impressions
Sentiments
BIG DATA
Social
Followers
Analyze Data in Real Time
Recommendations
Likes
PLATFORM Product Recommendations, Real Time offers,
Targeted Ads in Real Time
Psychographic surveys
Geo-Demographic Discover & Experiment
3rd Party
Segments Ad-hoc analytics, data discovery &
experimentation
Offline Transactions
Responses
Governance
Enforce data structure, integrity and
control to ensure consistency
© 2012 IBM Corporation

11. IBM Netezza Big Data
Impressions
Cookies Appliance
Online
Registrations
Purchase Transactions
In-Market Intent
• Purpose-built analytics engine
• Integrated database, server and storage
Influence
Sentiments
• Standard interfaces
Social
Followers • Low total cost of ownership
Recommendations
Likes
 Speed: 10-100x faster than traditional systems
Psychographic surveys  Simplicity: Minimal administration and tuning
Geo-Demographic
3rd Party
Segments
 Scalability: Peta-scale user data capacity
Offline Transactions  Smart: High-performance advanced analytics
Responses
© 2012 IBM Corporation

12. Behavior Segmentation Use Case
Product Life-
style
Sensitivity to Preference Preferred
Markdown Channel
Levels
Engagement in
Recency + Loyalty Program
Frequency +
Value
Use of Private
?
Response to Label Credit
Media Card
Use of Service
Estimated Time Departments
until Next Arrival
Breadth of Return /
Entities Shopped Exchange Predictive
Behavior Customer
Analytics Analytics
IBM Netezza
Unica Unica
Customer Predictive
Insight Insight
© 2012 IBM Corporation

13. Audience Campaign Yield Attribution Ad Sales Website
Ad Targeting
Optimization Analysis Optimization Analysis Analysis Optimization
Netezza Powered Analytic Solutions
Media Plans Execs, Analysts,
Internal Data
Customer Master Data
Campaigns
Quants, Staticians,
Responses
Data Miners
List Pulls, Campaign
Execution
External Data Sources
Competitive Site Performance Media Perf. 3rd Party Data Social Monitoring Video
13 © 2012 IBM Corporation

14. Merkle helps clients process
hours worth of data in
minutes with Netezza
Need
• Effectively transform petabytes of raw data into
useful information that can influence marketing
processes and predict customer preferences with
accuracy
Benefits
• 25-90% revenue lift for one client through use of
new analytic models
• Regularly received a 70% reduction in processing
time for complex marketing campaigns -
decreasing time from hours to minutes
• Up to 25% decrease in the cost of managing
clients’ environments
© 2012 IBM Corporation

15. Question and Answer Round Table
Nathaniel Rowe Krishnan Parasuraman
Research Analyst CTO, Digital Media
Enterprise Data Mgmt. Netezza and Big Data Solutions
Nathaniel.Rowe@Aberdeen.com @kparasuraman
Visit http://analyzingmedia.com to learn more about big data in digital media.
15 © 2012 IBM Corporation