NEDMA14: Answering Marketing’s Top 3 Questions Using Predictive Analytics - William B. Disch, Ph.D.

How can we boost response, reduce churn and
upsell current customers?
William B. Disch, Ph.D.
Senior Vice President of Analytics, Virtual DBS
May 14th, 2014
Answering Marketing’s Top 3 Questions
Using Predictive Analytics

Today’s Presenter
William Disch, Ph.D.
Senior Vice President of Analytics
• Heads the analytics division at Virtual DBS
• Primary focus is on client collaboration and employing ROI-oriented multivariate
predictive modeling and algorithm creation, product sequencing, segmentation, and
custom analytics specific to industry verticals
• Key is collaboration in operationally defining context of measurable objectives
• Presenter at major database, analytics and academic conferences nationwide including
the DMA, DM Days, the AMA, and others

How can I effectively improve my
campaign response rates?
A Marketer’s Top 3 Questions
My customer churn rate is too high.
How can I reduce it?
My company sells many products.
How can I identify customers who
will buy more than one?

Issue/Question Modeling Solution
Lower than optimal conversion rate for new
leads.
How do I increase new acquisitions while at
the same time keeping costs down?
Customer Acquisition Model
Churn rate higher than acceptable.
Can I identify current customers at the
highest risk to churn before they leave?
Churn (Attrition) Model
Missing upsell opportunities.
How can I identify customers most likely to
buy their next product?
Upsell Model
Three Cases

Case 1
Customer Acquisition Model:
Specialty Foods Retailer

Customer Acquisition Model
Specialty Foods Retailer Business Case
Specialty Foods Retailer Current State
600,000 prospect mail pieces sent annually
Current Results
2% gross response rate (6,000 responders)
25% conversion rate (1,500 customers purchased)
Specialty Foods Retailer Desired State
No change in mail volume
Desired Results
2.6% gross response rate (9,000 responders, 30% improvement)
25% conversion rate (2,250 customers purchased)

Customer Acquisition Model –
The Process Simplified
Campaign
Responders
Campaign
Non-Responders
Virtual DBS
Appended
Demographics
Purchasers
(subset of
Campaign
Responders)
Predictive
Analytics
Processing
Predictive Algorithm Reveals Top
Response/Acquisition Drivers and
their Predictive Weight

Virtual DBS Compiled B2B and B2C Data
Includes Hundreds of Demographics and Related Elements
Demographic
 Income
 Wealth
 Age
 Ethnicity
 Occupation
 Household Type
 Marital Status
 Length of Residence
 Home Ownership
 Home Value
 Mortgage Info
 Home Size (Sq Ft)
 Lender Codes
 Age of Home
 Dwelling Type
 Small Office/Home
Office
 Presence of Children
 Ages of Children
Interests
 Fitness
 Outdoors
 Athletic
 Cultural
 Charitable Events
 Community
Involvement
 Gardening
 Financial
 Travel
 Donor
 Do It Yourselves
 Etc.
Buying Behavior
 Product Types
 Travel
 Upscale Retail
 Finance
 Etc.
Life Stage Clusters
 Mutually Exclusive
Clusters
 Life Stages:
- Springs: 18 - 24
- Summers: 25 - 44
- Autumns: 45 - 64
- Winters: 65+
 Income Range:
- Low: 40k
- Mid: 40k – 75k
- High: 75k+
 Family Type:
- Single
- Couples
- Families
 Community Type:
- Rural
- Suburban
- Urban
B2B Firmagraphics
 SIC Division
 SIC/NAICS Codes
 No. Employees
 Annual Sales
 Ownership Type
 Location Type
 Years In Business
Business Verticals
 Technology Use
 SOHO
 Etc.

0 0.05 0.1 0.15 0.2 0.25 0.3
Gender
Household Type
Dwelling Type
Marital Status
Household Income
Gifts
Sports/Leisures
Mail Order: Food Products
Health
Buyer Orders: Home Care
Garden
Number of Children
Assessed Median Home Value
Political Donor
Hobby: Knitting/Needlework
Likes to Read
Hobby: Cooking
Reading: Cooking/Culinary
Health/Institutional Donor
Predictive Attributes Driving Response/Acquisition
A specialty foods retailer wants to
increase response rates of new
customers buying holiday food
products.
We operationally defined the
event group of those who had
purchased during the past season
at a dollar value of X or higher.
The drivers in the algorithm show
that the best prospects tend to be
females in single family
households with children, with
moderate to high income, and
who have a propensity to use
discretionary income for a variety
of personal and social needs and
behaviors.
Gender
Female - 63%
Household Type
Adult Male & Female Present w Kids - 49%
Dwelling Type
Single Family – 74%
Marital Status
Married - 58%
Household Income
$150k + - 35%
$125-$150k - 10%
$100-$125k - 15%
Assessed Median Home Value
$750k + - 5%
$700-$750k - 1%
$500-$550k - 2%

Customer Acquisition Algorithm Example
An algorithm is a mathematical equation that incorporates predictive drivers and their
weights.
Constant (unique to each algorithm)
+ Gender (x .43)
+ Household Type (x .38)
+ Dwelling Type (x .32)
+ Marital Status (x .31)
+ Household Income (x .27)
+ Gift Behavior (x .25)
+ Sport/Leisure Interest (x .23)
+ Mail Order Food Product (x .23)
+ Health Interest (x .22)
+ Home Care Buyer (x .21)
+ Garden Interest (x .20)
+ Number of Children (x .18)
+ … remaining predictors… (x .XY)
= Propensity to Purchase

How Do We Know the Model Works?
VALIDATION PROCESS:
How we assess the power and
efficacy of a model:
Acquisition model strength is
tested on a validation dataset:
1. Randomly see event
group/target records into the
prospect universe set of
records
2. Run the algorithm
3. Event group/targets should
score near the top of the
scored file
4. Conduct multiple iterations
Model Scoring Validation Gains Table
Probability Random Validation Acquisition Score Rank
5% 0.50 7.34
Ranks
1, 2 and 3, 4
10% 0.50 1.50
15% 0.50 1.37
20% 0.50 1.14
25% 0.50 1.15
Ranks 5,6
30% 0.50 0.93
The validation shows that the top
5% of scored prospects are 7.3
times more likely to become a
customer than a random prospect

Before Acquisition Model Scoring We Are Here
FirstName LastName Address1 City State Zip Phone Email
Acquisition
Score
Aimee Falcigno 9 Rocamora Road Rocky Hill CT 06067 8605718495 ajfal@cox.net ?
Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 amtr007@hotmail.com ?
Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 Amy.lubas@gmail.com ?
Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 andyskar@comcast.net ?
Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 ann.schmidt@mx.ch ?
Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 apaquet@harthosp.org ?
William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 bderech@yahoo.com ?
Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 Beamer4332@Sbcglobal.net ?
Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 beanie1970@sbcglobal.net ?
Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 c.beirne@hotmail.com ?
Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 carolhoyt3@hotmail.com ?
Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 chris.mpeterson@sbcglobal.net ?
Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 cmeeker@smithbrothersusa.com ?
Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 crpoglitsch@comcast.net ?
Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 cstephens8@yahoo.com ?
David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 david.iaia@globalinsight.com ?
David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 david.w.wurts@lmco.com ?
David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 davidavining@gmail.com ?
We have no way of predicting a prospect’s response/purchase behavior.

After Acquisition Model Scoring We Are Here
FirstName LastName Address1 City State Zip Phone Email
Acquisition
Score
Aimee Falcigno 9 Rocamora Road Rocky Hill CT 06067 8605718495 ajfal@cox.net 1
Ann Reynolds 94 Brookmoor Rd West Hartford CT 06109 8603130922 amtr007@hotmail.com 6
Amy Lubas 823 Edgemere Lane Sarasota FL 34242 9419939899 Amy.lubas@gmail.com 9
Andy Skarzynski 34 Ballard Drive West Hartford CT 06119 8602333240 andyskar@comcast.net 1
Ann Schmidt 21 Onlook Rd Wethersfield CT 06109 8605294388 ann.schmidt@mx.ch 1
Anthony Paquette 88 Oak Leaf Drive Colchester CT 06415 8605378667 apaquet@harthosp.org 2
William Derech 132 Randy Lane Wethersfield CT 06109 8605298060 bderech@yahoo.com 10
Suzanne Costanzo 9 Deerfield Trace Burlington CT 06013 8603067433 Beamer4332@Sbcglobal.net 1
Gina Cunningham 28 Cockle Hill Road Salem CT 06420 8608893943 beanie1970@sbcglobal.net 4
Caperton Beirne 7100 Quail Hill Road Charlotte NC 28210 7047808753 c.beirne@hotmail.com 7
Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 carolhoyt3@hotmail.com 8
Christine Peterson 60 Iroquois Road West Hartford CT 06117 8602332239 chris.mpeterson@sbcglobal.net 2
Craig Meeker 44 Garden Gate Farmington CT 06032 8602840123 cmeeker@smithbrothersusa.com 9
Cynthia Poglitsch 98 Cobblestone Way Windsor CT 06095 8606885207 crpoglitsch@comcast.net 10
Carrie Stephens 3135 Crittenton Place Baltimore MD 21211 5618012538 cstephens8@yahoo.com 2
David Iaia 37 Wilson Avenue Belmont MA 02478 6174894839 david.iaia@globalinsight.com 1
David Wurts 6404 S Ross Road Morrison CO 80465 3036974486 david.w.wurts@lmco.com 10
David Vining 65 Falcon Lane Glastonbury CT 06033 8604636450 davidavining@gmail.com 4
We know exactly which prospects are likely to respond and buy.

Case 2
Churn Model:
Telecommunications Business Case

Churn Model
Telecommunications Business Case
Telecom Company Current State (2013 results)
Subscriber count on January 1 : 1,000,000
Net Subscriber count on December 31 : 1,010,000
Current Results
Customers churned : 200,000
Net subscriber gain : 10,000
Churn rate : 20%
Telecom Company Desired State (2014 plan)
Subscriber count on January 1 : 1,010,000
Net Subscriber count on December 31 : 1,119,500
Desired Results
Customers churned : 100,500
Net subscriber gain : 109,500
Churn rate : 10%

How Difficult Is It to Sell Something?
The Economics of Marketing
It is 3x to 7x HARDER to sell to new customer than an
existing one.
Existing Customer New Customer
Existing Product 1X 3X
New Product 2X 7X
Retention makes
a hard job many
times easier
Churn makes an
already difficult job
many times harder
If we can
acquire new
customers at
the lowest
possible cost,
extra resources
can be applied
to retention
efforts

Churn Model
Current
Customers
Lapsed Customers
Virtual DBS
Appended
Demographics
Payment history,
price/promo,
products
purchased, CS
calls, etc.
Predictive
Analytics
Processing
Churn Drivers and their
Predictive Weights

Predictive Attributes Driving Churn
The top predictive churn drivers
show us why customers left:
7. Aggressive Competitive Offer
6. Promotion Period Expiring
5. SOHO*
4. Technical Issues
3. GeoVector*
2. Price
1. Service
0% 5% 10% 15% 20% 25% 30%
Care Call: Service - Last 30 Days
Care Call: Price - Last 30 Days
Care Call: Tech Probs - Last 30 Days
Duration to Promo Roll-Off
Care Call: Service - Last 90 Days
Competitor Aggressive Promotion
Product Grade (single to bundles)
Care Call: Service - Last 60 days
Active or Inactive Promo Flag
Last Package (single, double, triple)
Promo Duration
Significant Churn Predictors (Customer Variables)
* Virtual DBS appends

Telecom Churn Algorithm Example
A churn algorithm is a mathematical equation that
incorporates predictive drivers and their weights.
Constant
+ Aggressive Competitive Promotion (x .16)
+ Time to Promo Expiration (x .21)
+ SOHO (small office, home office), (x .23)
+ Number of Tech Support Calls (x .27)
+ GeoVector (Age, Income, Geo, Family Type), (x .32)
+ Price (x .36)
+ Number of Service Issues (x .43)
= Propensity to Churn

Before Churn Model Scoring We Are Here
We have no way of predicting future churn behavior.
FirstName LastName Address1 City State Zip Phone Email Churn Score
Carol Heminger 515 Edgebrook Lane West Palm BeachFL 33411 5617750098 carolhoyt3@hotmail.com ?

After Churn Model Scoring We Are Here
We know exactly which customers are most likely to churn.
FirstName LastName Address1 City State Zip Phone Email Churn Score
Carol Heminger 515 Edgebrook Lane West Palm BeachFL 33411 5617750098 carolhoyt3@hotmail.com 8

Case 3
Upsell Model:
Utility Company

Upsell Model
Utility Company Business Case
Utility Company Current State
Customers can buy an add-on insurance product to protect their furnace
Quarterly direct mail campaign with insurance offer sent to all 100,000 customers
Current Results
Gross response rate 3% (3,000 responses)
Conversion rate 10% (300 sales)
Utility Company Desired State
Quarterly direct mail campaign sent to 20,000 current customers most likely to
buy insurance product
Desired Results
Mail 80,000 fewer records
Achieve 18% gross response rate (3,600 responses)
Conversion rate 10% (360 sales)

Upsell Model –
Customers
without Insurance
Product
Customers with
Insurance Product
Virtual DBS
Appended
Demographics
Predictive
Analytics
Processing
Upsell Purchase Drivers and their
Predictive Weights

Predictive Attributes Driving Upsell Purchase
GeoVector
3323: 45-64, $75k+, Suburban, Families-~18%
3313: 45-64, $75k+, Urban, Families-~8%
2323: 25-44, $75k+, Suburban, Families-~8%
Household Income
$50,000-$74,999-~19%
$150,000+-~19%
$75,000-$99,999-~18%
Dwelling Type
Single Family-~100%
Homeowner Status
Owner- ~97%
Renter- ~1%
Pro-Environmental Status
Yes- ~3%
Mail Responder
Multiple- ~78%
Single- ~1%
Length of Residence
15+ Years- ~39%
11-14 Years- ~17%
8-10 Years- ~14%
Socio-Demographic Clusters
3-Corporate Clout- ~5.68%
9-Platinum Oldies- ~5.58%
5 Sitting Pretty- ~5.45%
Interests
Garden- ~14%
Investments- ~38%
Travel- ~56
Donor
Religious Donor- ~20%
Health Institutional Donor- ~22%

Before Upsell Model Scoring We Are Here
FirstName LastName Address1 City State Zip Phone Email Upsell Score
Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 carolhoyt3@hotmail.com ?
We have no way of knowing which customers will make a next purchase.

After Upsell Model Scoring We Are Here
FirstName LastName Address1 City State Zip Phone Email Upsell Score
Carol Heminger 515 Edgebrook Lane West Palm Beach FL 33411 5617750098 carolhoyt3@hotmail.com 1
We know exactly which customers are most likely to make a next purchase.

Summary
• Predictive modeling effectively answers difficult marketing questions
• Predictive modeling allows you to maximize your ROI by concentrating
your resources on those customers or prospects most likely to buy or
churn
• Scored data resulting from a predictive model is immediately actionable
• Predictive algorithms are portable and can be used to score a variety of
internal and external lists
• Client collaboration and the operationally defined metrics specific to the
current state business state are key – model performance is highly
correlated with the quality of the metrics used to build the model

About Virtual DBS
What we do
 Virtual DBS offers technology, data, and analytics allowing corporate decision makers to gain
strategic business insights they can use to make profitable business decisions.
 Best in class tools for CDI, predictive analytics, and campaign management to organize, extract,
and monetize customer and prospect databases and generate positive ROI.
 Highly effective and affordable products and services for B2C and B2B marketers.
 Founded and managed by industry veterans with a focus on mathematical precision, customer
service, and client collaboration.

For questions or further information, please contact
John Dodd, EVP
jdodd@virtualdbs.com
Direct 401.667.7595
www.virtualdbs.com
Q and A

After successful deployment of hundreds of modeling initiatives for a
multitude of clients in widely varying marketing scenarios, we have often
seen response performance improvements of 20% to 40% over
established baseline.
• For example, where a particular package-list-offer combination has historically
generated a 2% response rate, we often see our clients enjoying response
rates ranging from 2.4% to 2.8% (i.e. 20% to 40% above established baseline)
by utilizing Virtual DBS predictive modeling in their customer development
targeted marketing campaigns.
• We have seen highly profitable modeling initiatives in which lesser gains were
achieved (often as low as a couple of percentage points over control) – but
have also seen campaigns come in with much higher response lift (e.g. 2x over
baseline).
Appendix
A Note on Response Performance

Appendix
Predictive Modeling Overview
• Modeling uses past behaviors (respond, buy, churn)
to optimize those behaviors going forward
• We combine appended demographics with
customer-specific fields (transaction values, dates,
product details, etc.)
• Two Primary Outcomes:
1. Behavioral Profile
2. Scoring Algorithm

Appendix
Modeling Answers Key Strategic Questions
What do my best customers look like?
How do I find more prospects who look like them?
What is my market penetration?
Where are my new clients going to come from?
How do I stay relevant to my various customer groups?
Which of my customers are most likely to leave?
Which customers are going to spend the most?
What is the lifetime value of a customer?
What is the cost to acquire a new customer?
How do I help low-performing customers to become high-
performing customers?

Appendix
Types of Models
Churn
Acquisition
Customer Optimization (cross and upsell)
Cluster/Segmentation
Best Payer
Best Customer
Price Elasticity
Product Sequencing
Others

0.00
2.00
4.00
6.00
8.00
10.00
Algorithm Performance
Random Validation
There are two primary steps for validating a predictive algorithm once the algorithm has been
created.
First, the event group sample is randomly seeded into the universe sample, using multiple
iterations of random samples, then the file is scored using the algorithm.
If the algorithm is successful, the event group sample should score in the “Best” deciles, and up
and to the left in the above bar chart. The results mean that the randomly seeded event group
sample is being successfully predicted by the algorithm.
In this case, model performance indicates that deciles 1-2 have the greatest lift. Detection of
seeds suggests suppressing the top ~20% of the top scoring records yields a probability of
capturing ~68% of current customers.
Probability
Tier
Random Validation Prospect
Selection
5% 0.50 8.04
Deciles 1
thru 2
10% 0.50 2.52
15% 0.50 1.69
20% 0.50 1.29
25% 0.50 1.08
Decile 3
30% 0.50 0.89
35% 0.50 0.76
Deciles 4
thru 10
40% 0.50 0.59
45% 0.50 0.53
50% 0.50 0.50
55% 0.50 0.50
60% 0.50 0.50
65% 0.50 0.50
70% 0.50 0.50
75% 0.50 0.50
80% 0.50 0.50
85% 0.50 0.50
90% 0.50 0.50
95% 0.50 0.50
100% 0.50 0.50
Appendix
How Do We Know the Model Works?

0.00
0.20
0.40
0.60
0.80
1.00
Lift and Gains Performance
Random Validation
Probability
Tier
Random Validation Prospect
Selection
5% 0.50 8.04
Deciles 1
thru 2
10% 0.50 2.52
15% 0.50 1.69
20% 0.50 1.29
25% 0.50 1.08
Decile 3
30% 0.50 0.89
35% 0.50 0.76
Deciles 4
thru 10
40% 0.50 0.59
45% 0.50 0.53
50% 0.50 0.50
55% 0.50 0.50
60% 0.50 0.50
65% 0.50 0.50
70% 0.50 0.50
75% 0.50 0.50
80% 0.50 0.50
85% 0.50 0.50
90% 0.50 0.50
95% 0.50 0.50
100% 0.50 0.50
Appendix
How Do We Know the Model Works? (cont.)
Second, the probability of increased responding for the modeled event group is plotted again a
random sample.
Using the gains table to the right, the results also show that scored prospects in the top 5% of
the prospect file are 8x more likely to look like a current Best Responder, and those from the
second tier are 2.5x more likely to look like a current Best Responder.
Overall, prospects in the top 10% are approximately 5.5x more likely to look likely to look like
current Best Prospects, compared to only a 50/50 probability by using change alone.
Again, in this case, model performance indicates that deciles 1-2 have the greatest lift.
Detection of seeds suggests suppressing the top ~20% of the top scoring records yields a
probability of capturing ~68% of current customers.

For questions or further information, please contact
John Dodd, EVP
jdodd@virtualdbs.com
Direct 401.667.7595
www.virtualdbs.com

NEDMA14: Answering Marketing’s Top 3 Questions Using Predictive Analytics - William B. Disch, Ph.D.

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NEDMA14: Answering Marketing’s Top 3 Questions Using Predictive Analytics - William B. Disch, Ph.D.