Caserta Concepts (http://casertaconcepts.com/) and PNT Marketing Services (http://www.pntmarketingservices.com/), a leading provider of Customer Intelligence-based database marketing and analytic services, discussed how to turn your customer data into increased sales with predictive analytics and response modeling in a recent webinar.

1. WELCOME!
Joe Caserta
Founder & President, Caserta Concepts
This document is solely for the presentation of confidential PNT
information. No part of it may be circulated, quoted, or reproduced
for distribution outside the organization to which it was presented
without prior written approval from PNT Marketing Services, Inc. This
material was used by PNT Marketing Services during an oral
presentation; it is not a complete record of the discussion.
October 11, 2013

2. Caserta Concepts
• Technology services company with expertise in data
analysis:
› Big Data Analytics
› Data Warehousing
› Business Intelligence
› Strategic Data Ecosystems
• Core focus in the following industries:
› eCommerce / Retail / Marketing
› Financial Services / Insurance
› Healthcare / Higher Education
• Established in 2001:
› Industry recognized work force
› Consulting, Writing, Education
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3. Expertise & Offerings
Strategic Roadmap/
Assessment/Consulting/
Implementation
Big Data
Analytics
Data Warehousing/
ETL/Data Integration
BI/Visualization/
Analytics
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4. Our Strategic Partners
Hadoop Distributions
Platforms
Analytics & BI
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5. Our Clients
Finance
& Insurance
Retail/eCommerce
& Manufacturing
Education
& Services
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6. October 11, 2013
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7. What is Predictive Analytics
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8. Use Cases for Predictive Analytics
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9. The Basic Steps
Prepare and Sample Data
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10. Common Predictive Methods
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11. Additional Resources
Special Thank You:
Jen Underwood
phone: 813.435.5344 email: jen
www.impactanalytix.com
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12. Do you have access to all your customer data?
Are you able to quickly and easily derive customer-intelligent insights?
Are you using those customer insights to drive more precise lead targeting?
Do you use that targeting to direct your marketing dollars to the highest-ROI acquisition campaigns?
Are you able to leverage campaign response data to generate better leads faster?
Turbo-Charging Lead-Gen and
Conversion
How to Use Data Analytics and Response Models
to Reach, Realize, and Retain Customers.
This document is solely for the presentation of confidential PNT
information. No part of it may be circulated, quoted, or reproduced
for distribution outside the organization to which it was presented
without prior written approval from PNT Marketing Services, Inc. This
material was used by PNT Marketing Services during an oral
presentation; it is not a complete record of the discussion.
October 11, 2013

13. Outline
• Introduction and background
• Methodology
• Case study
• Q&A/wrap-up
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14. PNT Marketing Services Overview
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15. About PNT Marketing Services
PNT Marketing Services
(PNT) is a leading
provider of Customer
Intelligence-based
marketing services
(customer databases,
insights, and high-ROI
marketing actions).
PNT helps clients
acquire, grow and keep
profitable customer
relationships.
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16. PNT Clients
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17. PNT Methodology
Step 1:
PNT uses a
powerful 5step
methodology
to turbocharge
marketing
results
Step 2:
Step 3:
Step 4:
Step 5:
• Diagnostic
• Creating the customer-centric marketing
and analytic datamart
• Generating customer insights
• Implementing high-ROI marketing actions
driven by customer insights
• Measure/track/improve (“virtuous spiral”)
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18. Case Study
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19. Case Study
• Business problem
• Insights
• Approach
• Application of the methodology
• Results
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20. Case Study: Business Problem
PNT Client:
› PNT client working with a large for-profit education firm
Existing Program Design:
› Undifferentiated offers blasted to an undifferentiated but very broad list
› Had proven success increasing:



Response
Click-through-rate (CTR)
Click-to-lead rates
Need:
› Breakthrough results for even greater lead generation and more
sophisticated predictive analytics
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21. Cast Study: Insight
Diagnosis:
› Most programs are 80/20
Prospect
Base
20%
Responses
80%
› If you can find part of that subset you can pick your choice of:


Save money by reducing program size to likely responders – not the
issue for this client
Increase response by shifting program budget and concentrating funds
on differentiated offers and extra engagement targeting likely
responders. This strategy is often called strategic engagement which is
just jargon for segmentation and offer matching.
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22. Case Study: Approach
The trick is to find the likely
responders and engage them.
Here’s how we did it!
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23. Case Study: Application of the Methodology
All E-mail
messages
Construct an
Analytic Data
Warehouse
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All
Contacts
12 months
of history
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24. Case Study: Application of the Methodology
Data Capture Included
Contact Level Data
Send Data & Response Data
Client provided data – which
contacts became leads who enrolled
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Analysis of behavior patterns
that influence form-fill
Call-center Data
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25. Case Study: Application of the Methodology
Database Analysis
Goal: Generate “Quick Hits” to impact lead generation fast
Method:
1.
A statistical platform, which packages components from R with an
easy-to-use interface and ODBC connectivity to the MS SQL Server
datamart
2.
RFM Model
Identify “near-miss” contacts – those with high RFM scores but
who had not yet filled out a lead form online – segmented into four
distinct sub-groups for maximum targeting effectiveness
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26. Case Study: Application of the Methodology
Pilot Campaign Created
Included special messaging and creative to target each of
the four “near-miss” sub-groups
“Near-miss” Sub Groups Identified
› Near-miss” sub-group 1: Recent and Active
› “Near-miss” sub-group 2: Recent and Inactive
› “Near-miss” sub-group 3: Not Recent and Active
› “Near-miss” sub-group 4: Not Recent and Inactive
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27. Case Study: Results
Almost 300 incremental leads generated for an
ROI of 2.8X (180%)
Click-Through
Rate
Click-to-lead
116%
55%
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28. Case Study: Results
Refined our master data model (MDM)
Used
to develop increasingly sophisticated models
› Predicted likelihood to open (linear regression)
› Predicted likelihood to fill out lead form (linear regression)
› Predicted likelihood to enroll within next 60 days (logistic
regression)
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29. Case Study: Results
The Analytic Platform
Leveraged to support on-going improvement in response and lead-gen
Demographic data reveals key predictors
in age and income
Reveals key “digital-body language”
components in sites visited
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30. Case Study: Results
• “Likely to open” model
based on historic open
behavior for pool of
contacts; linear regression
that predicts open rates
• Open model produces a
predicted lift of +238.76%
for decile 1 vs. 10, and
99.12% for decile 1 vs.
random.
• Messages to Decile 1
contacts produce almost
twice as many opens as a
random selection.
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31. Case Study: Results
• “Likely to fill out lead form” model based on
demographics, engagement, digital body language, and
prior form-fills; linear regression that predicts lead form
fill.
• Lead model produces a predicted lift of +315% for decile
1 vs. 10, and 287% for decile 1 vs. random.
• Messages to Decile 1 contacts produce almost three
times as many leads as a random selection.
• An improvement of almost 50% vs. the original “RFM”
lead-gen model
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32. Case Study: Results
•
“Likely to enroll in 60 days”
model based on
demographics, engagement,
and digital body language;
logistic regression that
predicts enrollment
•
Enrollment model produces a
predicted lift of +328.25% for
decile 1 vs. 10, and +215.15%
for decile 1 vs. random.
•
Messages to Decile 1 contacts
produce more than three times
as many enrollments as a
random selection.
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33. Q&A/Wrap-up
• Summary
• Q&A
• Contact
Tony Coretto, Co-CEO
Joe Caserta, President
PNT Marketing Services, Inc.
Caserta Concepts
http://www.pntmarketingservices.com
http://www.casertaconcepts.com
tcoretto@pntmarketingservices.com
joe@casertaconcepts.com
914-588-7278 (m)
914-261-3648 (m)
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