Today’s marketers are working feverishly to capitalize on the potential of highly insightful, yet unstructured, information being generated online. This coupled with the demands of real-time, rules-driven, audience-centered marketing represents a fundamental paradigm shift in how marketing is done. While the term “big data” may be fairly new, the concept is familiar to data-driven marketers who for years have been trying to run complex analytics across a deluge of structured and unstructured data flowing in from point-of-sale systems, web sites, social media, email campaigns, newsletters and many other online and offline sources. A new study produced by strategic consulting firm Winterberry Group in conjunction with the Interactive Advertising Bureau (IAB) and sponsored by IBM, reveals top investment priorities, high impact data use cases and barriers to adoption pertaining to big data in marketing and digital media. During this one hour webinar, we will present some of the key findings from our study which had contributions from over 175 advertising and marketing thought leaders. You will learn about the high priority use cases for today’s digital marketers, the underlying big data challenges and how some of the leaders are gearing up to address them with specific solutions. Audience Optimization Channel Optimization Advertising Yield Optimization Content Optimization & Ad Targeting

1. The New Age of Digital Marketing:
How Big Data and Advanced Analytics
Are Reshaping the Marketing World
Jonathan Margulies Krishnan Parasuraman
Managing Director CTO, Digital Media
Netezza and Big Data

2. Winterberry Group: Helping Advertising, Marketing, Media and
Information Companies Grow Value
Strategic Consulting
• Corporate Strategy Development
• Market Intelligence
• Marketing Process Optimization
• M&A Transaction Diligence Support
• Investment Banking Services, through

3. Our Agenda
What inspired the
research?
What role is data playing
in digital advertising
today?
How should we be
thinking about our “data
platform” for the future?

4. In the Beginning, There Were Subscriber Files…
H. Catalogus
(0-~1980 A.D.)

5. … Which Begat Demographic “Selects,” Data Cards and the First True
Commercial Data Models…
NAME ADDRESS
PHONE
GENDER
AGE
INCOME
H. Catalogus H. Mailinglistus
(-~1980 A.D.) (~1980s)

6. … Which Begat Modeling, Cluster Segmentation, Cooperative
Databases and—With the Arrival of the Internet—E-mail Data…
H. Catalogus H. Mailinglistus
(-~1980 A.D.) (~1980s)

7. … Which, In Concert with the Growth of “CRM,” Gave Rise to
Sophisticated Database Management, CDI and MDM Infrastructures…
“Customer File”: Contact
Persistent Identifiers
Info, CRM, Demographics
“Prospect File”:
Demographics, Credit Scores
Interactions Logs
Transactional / Loyalty “Single Source of the
Records Truth”
Public Records
Self-Reported “Intent” Data Mr. John Q. Customer
One Response Rate Way H. Analyticus
Boston, MA 01234 (~1990-2000s)

8. But “Evolution” Isn’t Always Painless; The Emergence of Digital
Channels Has Brought With It a Deluge of New Data Sources
Direct Mail Display Advertising
Call Centers
Search
Catalogs
Retail Transactions Social Media
Print Publications
Mobile
Broadcast Outlets
Email Tablets

9. And Those Various Channels Generate—and Rely Upon—a Range of
Information Types
Transactional added from
Psychographic and behavioral purchase records, cooperative
compiled from databases
surveys, analytical models
Offline Social compiled from
Providers
social
Geo- Social Sites / sites, blogs, sharing
Demographic Offline Online sites,
compiled from Compilers Providers
?
publishers, data Online Data Types:
bases and other • Registrations
third parties • Cookies (Flash) / browsing
activities
• Social networks
Portals /
Publish • Online purchase data
Online
ers • In-market purchase intent
Compilers
Artwork Source: David Harbaugh, Harvard Business Review

10. … And So “Traditional” Database Infrastructures Are Being Asked to
Support Vast New Streams of Unstructured Information
?
Behavioral (Clickstream)
Intent (Opt-In/Registered
and Inferred)
Web Analytics (Geo-/
Technographic)
“Customer File”: Contact Info
and Demographics
“Prospect File”: CRM
Demographics, Credit Scores
Transactional / Loyalty
Records
Public Records
H. Digitalus
Self-Reported “Intent” Data (~2009-Today)

11. But The Integration of “Traditional” and “Digital” Data Poses a Set of
Unique Challenges, Owing To Discrepancies Between…
Known Names/Addresses… “Batch” Processing…
… and Anonymous IP Addresses … and Real-Time Deployment
Campaign-Driven Execution… Single-Channel Focus…
… and Continuous Targeting … and Integrated Marketing

12. Today, The “Use Cases” for Marketing Data Differ Substantially Across
Addressable Media
Online Display Advertising
• Advertiser: Creative/offer optimization, real-time media buying,
click fraud analysis/ad verification, search portfolio optimization,
site optimization
• Publisher: Yield optimization, inventory forecasting,
ad sales analysis, content/offer optimization, search optimization,
site optimization
Email
• Triggered messaging (via CRM/loyalty platforms)
• Target segmentation and message management
Direct Mail
• Cluster segmentation and offer management
• Targeted messaging and variable-content print

13. Our Agenda
What inspired the
research?
What role is data playing
in digital advertising
today?
How should we be
thinking about our “data
platform” for the future?

14. Our Panel: Senior Thought Leaders Across the Data Ecosystem
“Which Best Describes Your Job Role / Function?”
N=176
Source: Winterberry Group survey

15. “To What Extent Are the Following Use Cases Focal Points of Your
CURRENT Data-Driven Marketing Activity?”
Source: Winterberry Group survey Not a focus of our A significant focus of our
current data utilization current data utilization

16. “To What Extent Do You Believe The Following Use Cases Will Be Focal
Points of Your FUTURE Data-Driven Marketing Activity?”
Not likely to be a focus of our Likely to be a significant focus of
Source: Winterberry Group survey future data utilization our future data utilization

17. Use Case: Audience Optimization
Identifying customers and likely Fundamental Effectiveness: Identifying customers
prospects through the integration of Advertising and likely prospects through the
rich (though disparate) data sources; Benefit integration of first- and third-party data
managing cross-channel marketing sources
execution with the goal of engaging
those audiences strategically—and in Maturity Level Low: Despite technology advances,
accordance with consumers’ preferred uncertainty around the optimal
advertising media. approach to structured integration of
data
Core E-commerce Marketers, Digital
Beneficiaries Advertisers, Lead Generation Portals,
Publishers (for traffic acquisition)
Long-Term High: The ability to define high-
Potential potential audiences and facilitate
multichannel communication
represents a fundamentally new way of
marketing

18. Use Case: Channel Optimization
Fundamental Effectiveness/ Efficiency: Enabling
Advertising “right message, at the right time, via
Enabling “right message, at the right
Benefit the right media” targeting; expanding
time, via the right media” targeting;
expanding the role of consumers in the role of consumers in choosing
choosing optimal/preferred optimal/preferred communications
communications media. media
Maturity Level Low: Traditional marketing efforts are
channel-specific; “channel agnostic”
internal alignment that most marketers
have not yet undertaken
Core E-commerce Marketers, Digital
Beneficiaries Advertisers, Lead Generation Portals,
Publishers (for traffic acquisition)
Long-Term High: Media-agnostic communication
Potential strategies will enhance consumer
engagement (through dialogue and
purchase behavior)

19. Use Case: Advertising Yield Optimization
Fundamental Efficiency: Maximizing the value of
Advertising available advertising inventory by
Maximizing the value of available
Benefit identifying and “selling” high-value
advertising inventory by identifying
and “selling” high-value audiences audiences across individual publisher
across individual publisher properties properties and delivery media
and delivery media.
Maturity Level Low: Though technological advances
are rapidly allowing audiences to be
“sold” across distinct online media
platforms, the use case demands true
cross-channel yield optimization
Core Publishers
Beneficiaries
Long-Term High: For a publisher community
Potential struggling to effectively monetize
content, the identification and
optimization of audience-centric
inventory has the potential to deliver
substantial revenue opportunities

20. Use Case: Targeted Media Buying
Fundamental Efficiency/Effectiveness: Enabling the
Advertising economical, value-oriented purchase of
Enabling the economical, value-
Benefit advertising media; delivering targeted
oriented purchase of advertising
media; delivering targeted messages messages to audiences across a diverse,
to audiences across a actionable range of channels
diverse, actionable range of channels.
Maturity Level Intermediate: “Real-time bidding” (RTB)
tools have matured substantially over
the past few years, and are in common
use by enterprise marketers across
verticals
Core Marketers (via Demand-Side Platforms),
Beneficiaries Digital Agencies/Trading Desks
Long-Term High: Meaningful media-buying
Potential efficiencies are already accruing to
sophisticated users; coordinated use of
these applications and the targeted
messaging/offer tools will deepen value

21. “To What Extent is Your Company (Or Your Clients) Realizing Value
From the Following Data Sources?“
Source: Winterberry Group survey We (or our clients) are realizing no We (or our clients) are realizing
value from these data sources significant from these data sources

22. “To What Extent Do You See the Following Attributes Driving the
Underlying Usefulness of a Marketing Dataset?”
Source: Winterberry Group survey Not at all important in driving Critically important in driving
the value of a data set the value of a data set

23. “To What Extent Do You Believe Each of the Following Are Inhibiting
Interest/Investment in Marketing Data?”
Source: Winterberry Group survey Not inhibiting interest / Substantially inhibiting interest /
investment in marketing data investment in marketing data

24. The Complexity of Today’s Advertising and Marketing Programs Has
Driven Many to Re-Examine their Internal Operating Silos
What’s at stake? Holistic Marketing
• Data Process Management
• Strategic Resources/Authority
• Creative Assets
Effective People
• Investment Capital
• Knowledge/Expertise Management Brand Mktg.
Digital Direct Mktg.
LoBs Sales
Fin. Int’l Mktg.
IT

25. Our Agenda
What inspired the
research?
What role is data playing
in digital advertising
today?
How should we be
thinking about our “data
platform” for the future?

26. Foundational Capability: The Big Data Platform
Impressions Audience Optimization
Cookies
Online
Registrations
Purchase Transactions
In-Market Intent
Influence Channel Optimization
Sentiments
BIG DATA
Social
Followers
Recommendations
Likes PLATFORM
Ad Yield
Psychographic surveys Optimization
Geo-Demographic
3rd Party
Segments
Offline Transactions
Responses Targeted Media Buying

27. What should be the requirements for your Big Data Platform ?
Impressions Audience Optimization
Cookies
Online
Registrations
Purchase Transactions
In-Market Intent
Influence Channel Optimization
Sentiments
?
Social
Followers
Recommendations
Likes
Ad Yield
Psychographic surveys Optimization
Geo-Demographic
3rd Party
Segments
Offline Transactions
Responses Targeted Media Buying

28. The 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

29. IBM’s Big Data Platform
Impressions
Netezza
Cookies • Extreme Performance
Online
Registrations
• In-Database Analytics
Purchase Transactions
In-Market Intent
• Scalable Appliance
Influence
Sentiments
Streams
BIG DATA
Social
Followers • Act on Data “In-Motion”
Recommendations
Likes
PLATFORM • Real time analytics
• Alerts/Actions
Psychographic surveys
Geo-Demographic
3rd Party
Segments
Offline Transactions
Big Insights
Responses • Unstructured Data
• Complex Analytics

30. IBM’s Big Data Platform Delivers Results
Impressions
1 Single view of customer across channels
Cookies
Online
Registrations
Purchase Transactions
In-Market Intent
Influence 2 Increased Targeting Precision
Sentiments
Social
Followers
Recommendations
Likes
3 Improved Relevance
Psychographic surveys
Geo-Demographic
3rd Party
Segments BIG DATA
Offline Transactions
Responses
PLATFORM 4 Higher campaign profitability

31. IBM’s Big Data Platform Delivers Results
1
Impressions
Increased customer retention equating to
Cookies
20% higher revenues; 5-7x more campaigns
Online
Registrations
executed per week
Purchase Transactions
In-Market Intent
2
Influence 25-90% revenue lift for one client through use of
Sentiments new analytic models; 70 % reduction in
processing time for complex marketing
Social
Followers
campaigns—decreasing time from hours tomins
Recommendations
Likes
3
Psychographic surveys Scaled to support 50% data growth per year;
Geo-Demographic Increased campaign performance by 50%
3rd Party
Segments BIG DATA
Offline Transactions
Responses
PLATFORM 4
Transparent view of billions of ad impressions
per day; achieved campaign goals while reducing
CPA from $170 to $80

32. The New Age of Digital Marketing:
How Big Data and Advanced Analytics
Are Reshaping the Marketing World
Krishnan Parasuraman
Jonathan Margulies
CTO, Digital Media
Managing Director
Netezza and Big Data
@kparasuraman