The document discusses a presentation given by Trish Mathe of Life Line Screening and Ozgur Dogan of Merkle on driving results through strategic data sourcing and optimization. It provides background on the presenters and an overview of their session topics, including the evolution of the marketing landscape, developing a framework to assess data value, and a case study on Life Line's data sourcing and optimization. The key trends discussed are the data explosion, challenges of increased costs and complexity, and the need for integrated and analytical approaches to data to improve ROI and customer focus.

1. 9/30/2011
October 5th, 2011
Driving Results through Strategic
Data Sourcing and Optimization:
Life Line Global Case Study
Trish Mathe – Vice President of Database
Marketing, Life Line Screening
Ozgur Dogan – General Manager, Data
Solutions Group, Merkle
Presenter Backgrounds
• Trish Mathe
• Vice President of Database Marketing at Life Line Screening
• Over 10 years of database marketing experience both in financial services
and healthcare industries
• Areas of expertise include: building and maintaining marketing
infrastructure and automation, prospect and customer database
management, campaign management and measurement
• Experienced in marketing to the fifty plus crowd, healthcare professionals,
and several other specialty market segments
• Ozgur Dogan
• General Manager of Data Solutions Group at Merkle
• Oversees the delivery of analytical data sourcing and optimization solutions
for Merkle’s clients across all industry verticals
• Spent 7 years at Merkle and has 15 years of industry experience in building,
implementing and integrating database marketing solutions
• Technical MBA Degree from the University of Georgia
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Session Overview
1. Evolution in the CRM Data Landscape
2. Developing a quantitative framework to assess value of data
3. Future Trends and Innovation Opportunities
4. Life Line Data Sourcing & Optimization Case Study
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2. 9/30/2011
Evolution of the Marketing Landscape
Global Market Trends
• Fundamental changes in the consumer decision making and
buying process
• Advancing and evolving technology use
• Expanding fragmentation – media and channels
• Data explosion driven by emergence of digital media
• Clutter and confusion in the data landscape
• Increased Accountability and Measurement
Ultimately, these influencers are changing the way marketers will create
competitive advantage in the future.
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Consumers are More Connected Today than Ever
Blog
Email Search
27%
actively
read blogs
87% use email
87% 27% 86%
86% use search
1+ times per day frequently
Social Display
63% use
20% click on
Facebook 63% banner ads
weekly IM Mobile 20%
33% use IM 51% are active
regularly 51% texters
33%
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3. 9/30/2011
Database Marketing Landscape is Evolving
DbM 1.0 DbM 2.0
Single Campaign/ Media Targeting Integrated Media Optimization
Direct/Identified Model New Entrants
Key Trends
Domestic US and International Solutions
Offline focus Digitalization
Cost Pressure Increased Cost Pressure
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Data Explosion!
Today, the codified information base of the world
is believed to double every 11 hours
15 out of 17 sectors in the United States have more data stored
per company than the US library of Congress
“We create as much information in two days now as we did from
the dawn of man through 2003.”
Eric Schmidt, Google CEO
“Organizations are overwhelmed with the amount of data they
have and struggle to understand how to use it to drive business
results.” (2010 MIT Sloan/IBM Study)
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Major Factors Driving Opportunity
Emergence Challenges Objectives Solution
New Channels
& Media
Cost
Pressures
Improve
Customer ROI
Centricity
Increased Analytic
Focus on
Complexity Data Sourcing
The Customer
Accountability & Optimization
&
Measurement
Integrated
Approach
Increased
Message
Technology Volume
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4. 9/30/2011
Business Impact of Analytical Data Sourcing
Leading direct marketer saved $2 MM in list sourcing cost in it first four 4 months
through analytical data sourcing optimization without negatively impacting
response
Total List Spends and Savings
$4,000,000
$3,500,000
$3,000,000
$2,500,000
$2,035,459
$2,000,000
$1,500,000
$820,040
$1,000,000
$490,515 $456,425
$500,000 $268,479
$0
Jun Jul Aug Sep Total
2010 Costs 2011 Costs Savings
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CRM Data Landscape
CRM Data Provider Landscape
COMPANY TYPES
SEGMENTATION TOOL SYNDICATED
COMPILERS LIST MANAGEMENT SPECIALTY COMPILERS CREDIT DATA DIGITAL DATA
PROVIDERS RESEARCH
Aggregators,
Lifestyle/Behavioral, Generic Clusters - utilizing Panel data representing
Demographics & Credit Scores, Owners,
Response Data Realty, Transactional, attitudinal, demographics, or consumer attitudes &
Firmographics Credit Attributes Audience,
Life Events credit information behaviors
Analytics
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5. 9/30/2011
Common Data Types and Constraints
Type of Data Examples Common Constraints
Compiled & Experian INSOURCE, ‐ Can only afford one source
Aggregated Data Epsilon TotalSource, ‐ It is difficult to determine unique value
Data Source so only purchase single source
Syndicated Research MRI, Scarborough ‐ Unable to implement beyond basic
messaging and product design
Vertical Lists New parents, ‐ Too many choices on the market, hard
magazine subscribers to evaluate
‐ Selection limited to a small number of
data card attributes
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Analytical Data Sourcing and Optimization
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How to Assess the Value of Data
Framework
Predictive Power Descriptive Power
Composite Score
Source Quality Universe Coverage
Key Dimensions for Evaluation:
– Predictive Power: Does the source add incremental lift to my predictions?
– Descriptive Power: Does the new source provide the ability to better
segment my target audience or lend new insights?
– Universe Coverage: Does the source provide access to new and unique
prospects (or overlay to existing customers)?
– Source Quality: Does the source provide accurate and high quality data?
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6. 9/30/2011
Data Optimization Lab
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Evaluating Value of Data Sources ‐ Example
Key Dimensions for Evaluation
Predictive Power Descriptive Power Example
Composite Ranking
Composite Score Vendor 1 Vendor 2 Vendor 3 Vendor 4 Vendor 5 Vendor 6 Vendor 7 Merkle
Composite Score 2.50 6.90 4.60 5.85 4.85 3.90 6.40 1.00
Score Rank 2 8 4 6 5 3 7 1
Source Quality Universe Coverage
Module Ranking
Vendor 1 Vendor 2 Vendor 3 Vendor 4 Vendor 5 Vendor 6 Vendor 7 Merkle
Score 0.2% 0.2% 2.1% 2.5% 4.4% 2.5% 0.2% 0.1%
Source
Rank 2 4 5 7 8 6 3 1
Quality
Rating High High Medium Medium Low Medium High High
Score 76.7% 62.6% 68.2% 66.1% 81.6% 83.2% 69.3% 94.0%
Universe
Predictive Power By Expert Model Rank 4 8 6 7 3 2 5 1
Coverage
Rating Medium Low Medium Medium High High Medium High
Score 150 138 144 150 145 149 134 151
Overall Model X Model Y Model Z Predictive
Rank 2 7 6 3 5 4 8 1
Power
Vendor A     Rating High Low Medium High High High Low High
Vendor B     Score 95% 31% 53% 81% 80% 63% 45% 100%
Descriptive
Vendor C     Power
Rank 2 8 6 3 4 5 7 1
Rating High Low Medium High High Medium Low High
Vendor D    
 Low  Medium  High
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Analytical Data Sourcing & Optimization
Traditional Data Analytical Data
Sourcing Sourcing
Incented to increase list
Incented to increase list
Incentive volume
performance and
reduce list costs
Not fully aligned with Client’s Fully aligned with Client’s
Alignment business goals cost efficiency and growth goals
Recommendations driven by Analytically Driven Optimization
Recommendations Experience and Relationship Approach
Dedicated Team focused on
Team Driven to increase commissions
Driving performance
World Class Analytics Team with
Analytics No real analytics or science
data optimization experience
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7. 9/30/2011
List Optimization Dynamics
The purpose of the list optimization process is to balance cost and value
Maximize List
Value
Increase Performance
Expand Universe
Minimize List Cost
Reduce List Costs
Reduce Run Charges
Reduce Duplication
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Analytic Approach to List Universe Optimization
Existing Universe Lists Future Universe Lists
List
List List
List List
List List
List List
List List
List List
List List List
List
List List
List
List
List List List
List
List
List
List List List
List List
“N” lists
Merkle’s approach is to inform the
source /list pool and universe
optimization process with analytics to ROI
define the right mix and number of lists
that maximize ROI
N lists
# of Lists
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Optimized Source Mix Illustration
The ratio of the Base File names increases in the optimized source mix scenario
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8. 9/30/2011
Optimization Performed At Multiple Levels
LEVEL 1 Source Optimization
Identify lists with high performance and lower
Expand Universe Through New Lists
costs
LEVEL 2 Universe Optimization
Replace lists with low performance and/or high overlap
LEVEL 3 Campaign Optimization
Model Scoring Segmentation
Today’s
Focus
HIGHER PERFORMANCE
LOWER COSTS
HIGHER VISIBILITY
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Optimization Lab – Data Sourcing and Integration Process
Data Source Source Source
Sourcing Optimization Integration Effectiveness
Source
Optimization Derived Data
Life Event Development
Triggers
Vertical Data Campaign 1
Performance
Compiled Data Audience Defined Campaign Optimization
Optimization Campaign 2
Optimization Universe
Campaign ROI
Enhanced
messaging &
Credit Data segmentation
Source
Effectiveness
Campaign 3
Partner Data
Customer Data Deploy Campaign Level
Create the best Analytics
Marketable Universe
Source Evaluation
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Trends and Innovation Opportunities
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9. 9/30/2011
Data Sourcing and Optimization As Enabler of
Customer Centricity
• Effective ICM™ demands a broad
set of core competencies in order
to be effective. Data plays a
central role in delivering on the
vision of ICM.
• Understanding the optimal mix
of data, both third party and
customer enables optimal
analytics.
• Analytics informed effectively
through data enables
segmentation, customer
optimization, marketing mix,
media targeting, and predictive
modeling in support of the four
functional areas within ICM.
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Data Sourcing As Strategic Engagement
Phase I ‐ Evaluation Phase 2 ‐ Implementation
(Months 0 – 3) (Months 3+)
Establish KPI’s
Illustrative
List Simulation/Optimization on
Optimization Historical Campaigns Refine Optimization Models
Evaluation of New Compiled
& Vertical Sources
Execute Test Campaign
Early Harvest
Eliminate list sources with
high duplication rates
Develop list optimization tool
Optimized list sourcing for
Rollout Highlights (incl. brokerage services)
26 Strategic data research and analysis
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List Optimization Engine Automates the Process
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10. 9/30/2011
Economic and Environmental Data Integration
Economic and Environmental Data
Examples
 New house starts and vacancy rates
 Unemployment rate and per capita personal
income
 Consumer pricing and sentiment index
 Precipitation and temperature data
 Disaster areas
Business Impact
 Better targeting of products and services
that are sensitive to environmental factors
 More predictive media mix optimization
and allocation models
 Ability to explain performance changes
due to environmental factors
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Digital Data Innovation and Integration
Online Data
• Place scripts on publisher sites to collect data about interests and in
Aggregators market activity (travel, auto, etc) at a cookie level
Anonymous (cookie) • Use the data to optimize online communications like Display Ads
audience targeting
Online Data
• Collect data across publisher, portal sites on in market activity, user profiles
Aggregators
• Includes “in market” data and IP‐email connected to postal address
PII Targeting
Offline to Online • Providers that own offline data assets match specific offline customer or
prospect audiences to online anonymous IDs
Audience Targeting • Several partner with Yahoo!, MSN, AOL for match
• Collect online data focused on specific niche areas – B2B, video, semantic
Niche Providers context, network provider, etc.
• Online panels evaluate user activity across sites, profiling companies tag
Online Panels sites to profile visitors
• The Rapleaf model of providing customer emails to determine social
Social behavior and identify influencers was shut down.
• No clear path to licensing data – most usage is in display
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Key Take Aways
• CRM data landscape is changing rapidly due to digital
media emergency and data explosion
• Innovative optimization approach delivers ROI by
reducing data costs and increasing marketing
performance
• It’s important to cut through the clutter and identify the
most valuable data assets in the market place including
newly emerging sources like digital
• Integrating analytics expertise with data market
knowledge is necessary to gain access to best and most
comprehensive marketable universe
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11. 9/30/2011
Data Sourcing & Optimization Case Study
Life Line Screening Overview
• Leading provider of community‐based preventive
health screenings and employs approximately 1000
employees in the U.S. and abroad
• Mission is to make people aware of the existence of
undetected health problems and guide them to seek
follow‐up care with their personal physician
• Since their inception in 1993, Life Line has screened
over 6 million people, and currently screens 1
million people each year at 20,000 screening events
globally
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Screening Process: Participant’s Experience
• “Results Letter”
mailed within 3
Participant Screened At weeks.
Screening Local Venue: Church,
• Advised to share
Scheduled Club, Community Center
with physician for
appropriate
follow‐up.
• If anything critical
participant is
provided a
Results are reviewed “Doctor’s Review
by a board certified Kit” immediately
physician and advised to go
to a physician or
emergency room
within 24 hours.
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12. 9/30/2011
Life Line’s Global Expansion Strategy
What? Where? Why?
Copy & paste model British Commonwealth • English speaking
• Cultural similarities
• Low regulatory barriers
Proof of concept #1: India • English speaking
Grass root marketing • Market potential
partnership • DM challenging
Proof of concept #2: Continental Europe • Non-English speaking
Franchise operations • Fragmented regulatory
landscape
• Good customer
response
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Life Line Projected Global Presence
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Life Line Business Challenge
• Interested in rapidly growing the customer base in US
and across the globe
• Using multiple compiled lists provides support to the
large‐scale Direct Mail acquisition program
• Limited universe and heavy mailing volume causing
contact fatigue
• Applying the learnings generated in US to support the
global expansion strategy with UK as the first pilot
market
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13. 9/30/2011
CRM Solution Roadmap
High
Targeting
Insight
Program Development
Measurement
Source Incremental P&L and
Hierarchy
Integration of Promotion
History Prospect Segmentation
“Silo” Sources Marcom Contact Strategy per
Prospect and Customer level
Insights Segment
Impact
Brief knowledge on the 50‐75 LTV & Profitability Tracking
years old target population Integration of Sources
@ The Customer Level
Multi‐Source Interaction
Creative & Source Testing
Campaign Approach
Single level source campaign
level measurement
Phase I Phase II Phase III
Low High
Program Sophistication
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Analytics and Targeting Solution for US
• Started with an in‐depth analysis of Life Line’s historical campaign
data and quantified the impact of contact history on campaign
performance
• Learnings from the analysis were used to develop a segmented
modeling strategy based on prior contact history that drove the
selection of best prospect names
• A new targeting methodology was developed and tested against
the current compiled data vendors in a head to head test
• Segmented modeling solution increased response rate by 38%
and generated 62K incremental customers given the same mailing
quantity
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Analytics Solution Framework
STEP 2 – DEVELOP A
STEP 1 – PERFORM CONTACT
PREDICTIVE MODELING
HISTORY ANALYSIS
SYSTEM
Base Universe Selection Model
Universal M odel #3
Segmented Segmented
Model #1 Model #2
Global
STEP 3 – DEVELOP
Optimal
Solution OPTIMIZATION
Local
Maximum
Local ALGORITHM TO
Maximum
MAXIMIZE DIRECT
MAIL CAMPAIGN
PERFORMANCE
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14. 9/30/2011
Targeting Evolution – Gen3.0
• LLS models continue to be redeveloped to keep current and the
approach refined to gain incremental lift.
• Gen3.0 segments out prior contacts from non‐prior and also
urbanicity. Promotion history as a predictor is removed and
used outside of the model to remove bias that comes from
having it in the model.
• In head to head testing Gen3.0 is winning over Gen2.0 in 5 out
of 7 campaigns and driving an incremental 6% improvement on
average over an already strong Gen2.0 model.
Modeling Approach
Gen1.0 – Gen3.0
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UK Predictive Modeling Solution
• We developed a Modeling System consisting of multiple
Customer Clone and Response Models to support Life
Line’s UK business
• Detailed analysis of the promotion history revealed that
two separate response models were needed (Prior and No
Prior) given the large performance differences between
the two contact strategy segments
• All of the models performed well and will provide a steady
stream of high performing target prospects going forward
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UK Modeling and Selection
Leveraging the learning's from the US:
UK Models
1. A customer clone model is used to
eliminate 50‐75 year olds who do National Canvas
50‐75 yr olds
not look like current Life Line
customer customers
Customer Clone Model
2. Prospects are then separated
between those who received an
offer from Life Line in the past 12
Priors No‐Priors
months vs. those who did not Response Response
Model Model
3. Segment‐specific response models
are used to improve identification
of prospects with prior and no Optimization Algorithm To
prior contacts Combine The Predictive Models
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15. 9/30/2011
UK Segmented Model – Summary
• Modeling process identified the characteristics among each
segment that best defined the responders
• Predictors of response for households without prior contact:
• Have a shorter length of residence
• Pay higher property tax
• Shorter distance to the screening location
• Reside in areas of higher concentration of existing Life Line UK customers
• Predictors of response for households with prior contact:
• Number of individual promotions received over previous 12 months
(the fewer the better)
• Reside in an area where others have responded to a past campaign
• Households that place orders by mail and the amount of the order
• Donate to charity
• Have a shorter length of residence
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UK Results
UK Results
• Prospects identified through the Segmented Models yielded up to 62%
improvement in performance relative to campaign average
• Merkle and Life Line Teams are working on the next generation segmented
models to further increase the response performance
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Trish Mathe
tmathe@llsa.com
Ozgur Dogan
odogan@merkleinc.com
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