analytical data sourcing and optimization summi… · • other data providers delivered lower...
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Analytical Data Sourcing and Optimization
Willy Sennott Sr. Director, Business Analytics & Research, People to People Ambassador Programs Ozgur Dogan SVP, Data Solutions Leader, Merkle
Presenter Backgrounds
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Willy Sennott
Ozgur Dogan
• Sr. Director, Business Analytic and Research at People to People.
• 14 years in the Finance, Marketing and Analytics function at PTP
• Oversees all Marketing analytics, business ROI, pricing, list acquisition, customer research and modeling processes.
• CPA and graduate of University of Washington Business School
• Data Solutions Leader at Merkle • Oversees the delivery of digital and offline data sourcing
and optimization solutions for Merkle’s clients across all industry verticals
• Spent 9 years at Merkle and has 15 years of industry experience in building, implementing and integrating CRM solutions
• Technical MBA Degree from the University of Georgia
Agenda
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Understanding the CRM data
landscape
Quantitative framework to
assess value of data
Industry Perspective & Case Study:
People to People
What’s Next: Digital Data Innovation
Agenda
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Understanding the CRM data
landscape
Quantitative framework to
assess value of data
Industry Perspective & Case Study:
People to People
What’s Next: Digital Data Innovation
Trends in the Data Industry: 2013
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Data Explosion “Big Data” is
complex
Need for multiple sources
Change in what’s
valuable
Increase of data sources, digital,
fragmentation, and overall volume of data in unlike any other time in the
industry
“Big Data” is driving big
confusion; not always translating
into insights
No single provider of data can do it all - there are plenty
of data companies out there, but no
company can have all the answers
“Smart Data” is not a commodity
Increased focus on consumer
preference and data privacy
Privacy
In the age of Big Data, the amount of available CRM data is becoming more overwhelming
SEGMENTATION TOOL PROVIDERS
DIGITAL DATA
SYNDICATED RESEARCH
CREDIT DATA
INTERNATIONAL
COMPILERS
TRANSACTIONAL DATA
LIST MANAGEMENT
SPECIALTY COMPILERS
BUSINESS TO BUSINESS
CLIENT DATA
1st Party
3rd Party
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Incredible amount of digital investment is accelerating innovation in the enabling data, technology, and analytics
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2012 Display Media “LUMAscape”
Innovation in digital data is creating
opportunities for both online and offline
audience targeting
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Data providers first recommend the data they
“own” because of their business model
…and everyone says their data is “the best”
There is limited or no
accountability for business
performance
Incentive system is broken. The more money marketers spend on data the
more money the data brokers make
Data source recommendations are made based on aggregate list level performance
data
Traditional Data Sourcing Model is Broken
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$
How is the Analytic Approach Different Than Traditional Model?
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Traditional Approach to Data Sourcing
“Sell what we own; it’s highest margin for us.”
“Sell what we own; assume it’s good.”
Product centric
Incentives
Analytics
Approach
“Sell what we own; it’s easiest to build.” Recommendations Driven by unbiased
optimization approach
Fully aligned with cost efficiency and
performance goals
Use of Advanced Analytics and Granular Data
Consultative and solution oriented
Analytic Approach to Data Sourcing
Agenda
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Understanding the CRM data
landscape
Quantitative framework to
assess value of data
Industry Perspective & Case Study:
People to People
What’s Next: Digital Data Innovation
Quantitative Framework for Assessing the Value of Data
Vendor 4 Composite
Optimization Score
Key Dimensions for Evaluation:
• Universe Expansion: does source increase breadth and coverage
• Descriptive Power: does source bring “texture” to an audience
• Source Quality: is the data reliable and accurate
• Predictive Power: does this data increase the predictive value of existing models
Data vendors provide
sample files for evaluation
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• Source 1 – very high reach, moderate information richness, and high predictive strength.
• Source 2 – medium reach, highest descriptive power, and the good predictive power.
• Other data providers delivered lower value.
Digital Data Evaluation Example
Universe Expansion Index represented by Bubble size. For illustration purposes
0
20
40
60
80
100
120
-20 0 20 40 60 80
Des
crip
tive
Pow
er In
dex
Predictive Power Index
Data Optimization Lab Results: All Data Providers
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Top Scoring Providers
Source 1
Source 2
Agenda
Understanding the CRM data
landscape
Quantitative framework to
assess value of data
Industry Perspective & Case Study:
People to People
What’s Next: Digital Data Innovation
People to People – why we are passionate about what we do
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http://vimeo.com/16153155
Remember not to blink!!
Premier Ambassadors Offerings Under the People to People Name
Student Student Ambassadors Leadership Citizens
Program Type Overseas Domestic Overseas Program Focus Student education and Student education and Professional exchange cultural exchange; leadership development; and cultural exchange, Primary profit driver Lower margin but feeder natural extension of to overseas program existing business model Target Market Age 10 – 18 Age 10 – 18 Age 45 – 80 Primary Pricing Range $5,000 - $7,000 $1,700 - $3,000 $5,000 - $6,500 Length 14 – 21 Days 5 – 10 Days 8 – 12 Days Annual Historical % of Individuals Traveled 60% - 75% 15% - 30% 5% - 10% Primary Destinations South Pacific Washington D.C. China Europe New York South Africa Asia California Russia Boston India
Three programs generated 96% of gross revenue and receipts in 2011 and will again in 2012
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Our Current Business Challenges and How We are Using New Data to Overcome Them
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• Traditional data elements do not allow us to “triangulate” to our unique audience
• Of the audience population of ~ 29M there are currently less than 500K customers across the industry with no particular competitor owning more than 100K. PTP owns 20K of this audience.
Challenge is to get to
here Attitudinal • Will let child travel • Does child have
interest • Will attend meeting
Demographics (age and income)
Psychographics Buying behavior, etc.
PTP: Refining our customer profile through Primary research
• Utilizing a MaxDiff Survey technique with both our current audience and the general population we are able to identify 8 segments within our general target market
•3 Segment most likely to travel with PTP •2 Segments somewhat likely to travel with PTP •3 Segments not likely to travel with PTP
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ROI IMPACT: Identifying top 3 and bottom 3 segments PRIOR to investing hard DM $ in the prospects eliminates 54% of our initial marketing spend while retaining 88% of our customers
How our Traditional Data sourcing and Modeling process works
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Prospect / Lead Sourcing
Modeling
A performance hierarchy developed
from historical performance
Traditional focus on compiled and “DMA” vendors
Sources based on ability to provide new prospects
versus new data elements
Client specific indexes used to overlay against the model
Regression using traditional data (largely demo and purchase behavior)
Risks with expanding “Big Data”
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• “exciting” • “sexy” • “Intuitive value”
Initial Lure
• Outside of key data group • Decision makers usually
Wrong Audience
• How does it change message? • How does it change offer? Missing ROI
How We See Online Data Aligning Us With Our True Customer
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Prospect / Lead Sourcing
Modeling
A performance hierarchy developed
from historical performance
Traditional focus on compiled and “DMA” vendors
Sources based on ability to provide new prospects
versus new data elements
Client specific indexes used to overlay against the model
Regression using traditional data (largely demo and purchase behavior)
Online targeting and behaviors
Resourcing data elements versus prospects or leads directly – ROI
challenge
Using online behavior in first pass model
Leveraging continued online and CRM data elements to remodel
throughout the process
Agenda
Understanding the CRM data
landscape
Quantitative framework to
assess value of data
Industry Perspective & Case Study:
People to People
What’s Next: Digital Data Innovation
Digital Big Data Platform
Connecting the Precision of Offline with the Reach of Digital
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Offline Digital Bridging the Gap Between Offline & Digital Marketing
Connecting Anonymous and PII Data
Integrated Offline with Digital Targeting
$50 Billion in DM spend
5 Year CAGR (-2%)
Focus on PII Information
High Cost Media
$15 Billion in Spend
5 Year CAGR (15%)
Focus on Anonymous Data
Limitless, Low-Cost Media
Next Generation Digital Data Platform
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Match to IP Address
Pixel Attributes
Reach, Accuracy, Privacy
Cookie Match
Agnostic Data Collection & Onboarding
Offl
ine
& O
nlin
e
3rd Party
2nd Party
1st Party
Target Audience
Digital Media Partners
Exchanges and DSPs
DMPs
Agencies
Marketers
Digital Data Platform
Data Market Place
Data Evaluation
Module
Targeting Optimization
Module
Predictive analytics
Experimental design
Segmentation
Public Domain
• CRM will be powered by a combination of offline, online, anonymous and personal data.
• Access to “Smart” data is not a commodity, and it won't be in 10 or 20 years.
Evolving Data Landscape
Future = (Anonymous + Personal) + (Offline + Digital)
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Summary
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CRM data landscape is changing rapidly due to innovation and Big data explosion
Analytically-led, unbiased approach is needed to determine the best mix of valuable digital and offline data
sources that will drive high performance
We are in the early but accelerating stages of an exciting journey and new approaches will be necessary ….first
movers will have a significant advantage
