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The Art and Science of
Database Marketing
Robert J Fulmer – Database(d) Marketing.
Olney, MD 20832
Cell 240-328-5846 fulmerrobert@verizon.net
Virtually Every “Active” Consumer and
Business is in Multiple Databases
• Consumer Compiled Databases-Polk,
Metromail, Donnelly, Infobase, DBA
• Consumer Credit-Equifax, TRW, Transunion
• Consumer Psychographic-Lifestyle Selector
• Geodemographic-Prizm, Microvision,
ACORN, CRMI, Updated Census Data
• Business Compiled-ABI and D&B
• Consumer and Business Lists
• Internal Customer Databases-Name &
Address, Transaction, Billing, Customer
History
US Sprint, AT&T, MBNA, ING, Travelers Group,
Solomon Brothers, T. Rowe Price, Sony, Blue Cross-Blue
Shield are making Millions/Billions of dollars with
Databased Marketing.
We offer your firm the exact same...We offer your firm the exact same...
• Computer Hardware
• Computer Software
• External Data Sources
• Proven Marketing Applications
• Statisticians-Model Developers
• Competitive Pricing
We Have Fortune 500 Experience &
We Outsource With the Best Vendors
• Marketing Research ExperienceMarketing Research Experience-Projects for USA
Today, The Wall Street Journal, NBC Television, Fox
Broadcasting, Hearst, VNU
• Media Planning ExperienceMedia Planning Experience-Toyota, Procter and
Gamble-Saatchi and Saatchi
• Strategic Business RelationshipStrategic Business Relationship with Experian,
Interlogic, ASC Database Marketing, Merkle Direct,
Info USA and Dun & Bradstreet
Since every active prospect exists in multiple
databases, the key to database marketing is
picking the prospects that will..
• Respond
• Respond and make a
purchase
• Respond, make a
purchase and then make
more purchases
Direct Marketing Evolved into Database Marketing
• Direct MarketingDirect Marketing
• Focus on Lists of
Consumers or
Businesses
• Uses Descriptive
Research to describe
Past List Performance
• Limited Targeting
Capability
• Database MarketingDatabase Marketing
• Focus on Individual
Consumers or
Businesses
• Uses Predictive Research
to Score Individual
Prospects
• Unlimited Targeting
Capability
Traditional Direct Marketing
Mailing List #1
Mailing List #2
Mailing List #3
{
{
{
Make a decision based on response by list
Highly Responsive
Continue to Mail
Marginal
Test Again
Poor Response
Drop List
Database Direct Marketing
Marketing to the Individual
Create a
Customized
Marketing
Database by
Combining all the
lists into one file.
Database Direct Marketing
Dem
ographics Buying Behavior
Psycographic
Geographic
Enhance the File with Internal and External Data
Add back all
records and
append additional
data overlays
Database Direct Marketing
Model, Group and Score
Find the prospects
most likely to
respond
Traditional Direct Marketing
Income
ResponseRate
0 +
+
Response Rate Correlation:
Response Rate and File Information
Traditional Direct Marketing
Income
ResponseRate
0 +
+
x
2
y
2
x
1
y
1
Response Rate Correlation:
Linear Analysis - Two Dimensional
Database Marketing
Income
ResponseRate
0
+
x
1
y
1
Response Rate Correlation:
Linear Analysis - Three Dimensional
Database Marketing
Income
ResponseRate
0
+
Age
Response Rate Correlation:
Multiple Linear Analysis - Three Dimensional
Database Marketing
Income
ResponseRate
0
+
Age
Response Rate Correlation:
Multiple Linear Analysis - Three Dimensional
Database Marketing
• RFM (recency, frequency, monetary)
• CHAID (decision tree)
• Linear and Logistical Regression
• Neural Networks, Systems Engineering
Modeling Methods
Database Marketing
• Select historical response data
– (past direct mail campaigns)
• Analyze the respondents and the non-
respondents
– 2,000 - 5,000 each
• Score (i.e. 0-999)
• Segment (quintiles, deciles, etc.)
Process
The Intelligent Closed Loop is a Source for
Positive and Negative Response Data, Demographics and
Payment History that is used to Build Predictive Scoring Models
Customer-Database-
Purchasing Information
Target
Selection
•Acquisition
•Up-Sell
•Cross-Sell
•Retention
Message
Selection
Implementation
Printing -- Data Processing
Personalization -- Letter-Shop
Response
Analysis
Data Cleaning
Data Upgrades
•Purchasing Data
•Demographics
•Psychographics
Customer Profile
Predictive Model
Yes
No
Modeling Methodology Used by
Telematch, US Sprint and Experian
Large Heterogeneous Mixture of All Customers on a File
Segment A
Homogeneous
Segment B
Homogeneous
Segment C
Homogeneous
Segment A
Response Model
Segment B
Response Model
Segment C
Response Model
List Selection
Segment C
List Selection
Segment B
List Selection
Segment A
Responders &
Non - Responders
Demographic
Attributes
Each List Selection Goes to Telematch
Database Marketing Focuses on Individual
Positive and Negative Consumers to Build
Predictive Scoring Models
• Negative Consumers
• Non-Responders
• Non-Convert to Customer
• Amount of Order is
Unprofitable-Unpaid Bill
• Customer Lifetime Value
is Low
• Financial Performance-
Low
• Positive Consumers
• Responders
• Converted to Customer
• Amount of Order is
Profitable-Paid Bill
• Customer Lifetime Value
is High
• Financial Performance-
High
The Enhanced Modeling Database is used to build
Predictive Models with a Positive and Negative
Customer Profile. These Positive and Negative
Profiles can then be used to Score the Entire
Range of a Prospect File or List
Positive
Customer
Profile
Negative
Customer
Profile
Male
Age 45
Sales
HH Income $55K
Female
Age 36
Professional
HH Income $65K
Prospects that closely match or are exact
opposites of your positive customer profile are
the easiest to identify.
Predictive Power of Models
Strong
Negative
Average Strong
Positive
Correlation +1.0-1.0 Correlation
Positive
Customer
Profile
Negative
Customer
Profile
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
. .
.
.
.
Multivariable Regression-Simplified
y
x
Positive Value
Dependent Variable 1.0
Purchasers/Donors
Negative Value
Dependent Variable -1.0
Non-Purchasers
Independent/Predictor Variables
V
Regression is a technique for finding and describing a functional (mathematical)
relationship between a dependent variable (Purchaser) and one or more independent or
predictor variables.
v
1
2
Database Marketing
Database Append
Sample Mailing
Model
Score
Segment
Validate
Non Response
Mail
LISTLISTLIST
Responses
=Past Mailing
Positive Response
Responses
Responses
Process
Segment Score Range Database Quantity Anticipated % Rate
Segment 3 999 to 574 255,752 0.81%
Segment 2 573 to 465 606,371 0.72%
Segment 1 464 to 0 975,460 0.47%
1,837,583 0.61%
Segment Analysis
Segment 3
14%
Segment 1
53%
Segment 2
33%
Response Rate Analysis
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0.80%
0.90%
Segment 3 Segment 2 Segment 1
Avg. % Rate
Database Marketing
33% lift
Final Analysis: Example of a homogeneous audience
Database Marketing
In a customer file with purchasing history and external demographic data
it is possible to build models that have a lift of 150% over the average response
rate and have a lift of 10 to 1 from the best to the worst decile in the file.
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8 9 10
Percent
Response
Rate
Decile
s
150% Lift above Average
1,000% Lift Best to Worst Decile
1%Average Response
Two Dimensional Matrix
Probability to Respond & Estimated Spending
Respons
e
High Low
Spending
High
Low
DECILES 1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
The Customer Life Cycle
and Marketing Activities
LoseRetainDevelopAcquire
Network
Reactivating
Retaining
Cross
Selling
Upselling
NetworkingConverting
Relationship
Marketing
Prospecting
Objectives and Benefits of Modeling
• Acquisition Models: Raise response rates, number of orders and
amount of orders; acquire customers with higher lifetime value, lower
return rates and higher payment rates.
• Conversion Models: convert customers with high lifetime value and
medium and long term profitability.
• Relationship Marketing Models: Increase survival time and lifetime
value of customers.
• Cross selling and Up-selling Models: Increase business base with
customers selling greater depth and width of products and services.
• Networking Models: Acquire new leads to be filtered through
acquisition models.
• Retention Models: Increase all positive indicators of customer
performance while preventing customer defection.
• Reactivation Models: Restoring performance of inactive customers
clients or donors.
Case Study: New Customer Acquisition
Blue Cross/Blue Shield Federal
• Blue Cross/Blue Shield budget only permits mailing 1.25
million names on their prospect list of 2.2 million names.
• Previous years-random deselection was used to reduce
mailing quantity.
• Created a new customer acquisition model, validate,
overlay prospect file with demographic data from R.L.
Polk, score the file and select best 1.25 million names.
• Saved over $9 Million in foregone revenue-for a cost of
$50,000- 180 to 1 Return on Investment.
• Blue Cross/Blue Shield Federal now owns a predictive
model that is being updated and used for future mailings.
Database Marketing
Strategic Applications
• Strategic Business Development: Use a new customer acquisition model to
build the customer base with profitable new customers- with good lifetime
value.
• Improve financial performance through customer development. Target
customers for cross-selling and up- selling. Increase the amount and
frequency of purchases from the customer base.
• Improve customer retention by identifying “at risk” profitable customers
before they fire your firm. Offer special products and services to save these
“at risk” customers.
• Practice Relationship Marketing by offering special products, services and
communication targeted to a segmented customer database.
• Apply Financial Models to all stages of the Customer Life Cycle. Improve
new customer acquisition, customer development and retention. This will
work like compound interest to dramatically improve growth and earnings.

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The Art and Science of Database(d) Marketing

  • 1. The Art and Science of Database Marketing Robert J Fulmer – Database(d) Marketing. Olney, MD 20832 Cell 240-328-5846 fulmerrobert@verizon.net
  • 2. Virtually Every “Active” Consumer and Business is in Multiple Databases • Consumer Compiled Databases-Polk, Metromail, Donnelly, Infobase, DBA • Consumer Credit-Equifax, TRW, Transunion • Consumer Psychographic-Lifestyle Selector • Geodemographic-Prizm, Microvision, ACORN, CRMI, Updated Census Data • Business Compiled-ABI and D&B • Consumer and Business Lists • Internal Customer Databases-Name & Address, Transaction, Billing, Customer History
  • 3. US Sprint, AT&T, MBNA, ING, Travelers Group, Solomon Brothers, T. Rowe Price, Sony, Blue Cross-Blue Shield are making Millions/Billions of dollars with Databased Marketing. We offer your firm the exact same...We offer your firm the exact same... • Computer Hardware • Computer Software • External Data Sources • Proven Marketing Applications • Statisticians-Model Developers • Competitive Pricing
  • 4. We Have Fortune 500 Experience & We Outsource With the Best Vendors • Marketing Research ExperienceMarketing Research Experience-Projects for USA Today, The Wall Street Journal, NBC Television, Fox Broadcasting, Hearst, VNU • Media Planning ExperienceMedia Planning Experience-Toyota, Procter and Gamble-Saatchi and Saatchi • Strategic Business RelationshipStrategic Business Relationship with Experian, Interlogic, ASC Database Marketing, Merkle Direct, Info USA and Dun & Bradstreet
  • 5. Since every active prospect exists in multiple databases, the key to database marketing is picking the prospects that will.. • Respond • Respond and make a purchase • Respond, make a purchase and then make more purchases
  • 6. Direct Marketing Evolved into Database Marketing • Direct MarketingDirect Marketing • Focus on Lists of Consumers or Businesses • Uses Descriptive Research to describe Past List Performance • Limited Targeting Capability • Database MarketingDatabase Marketing • Focus on Individual Consumers or Businesses • Uses Predictive Research to Score Individual Prospects • Unlimited Targeting Capability
  • 7. Traditional Direct Marketing Mailing List #1 Mailing List #2 Mailing List #3 { { { Make a decision based on response by list Highly Responsive Continue to Mail Marginal Test Again Poor Response Drop List
  • 8. Database Direct Marketing Marketing to the Individual Create a Customized Marketing Database by Combining all the lists into one file.
  • 9. Database Direct Marketing Dem ographics Buying Behavior Psycographic Geographic Enhance the File with Internal and External Data Add back all records and append additional data overlays
  • 10. Database Direct Marketing Model, Group and Score Find the prospects most likely to respond
  • 11. Traditional Direct Marketing Income ResponseRate 0 + + Response Rate Correlation: Response Rate and File Information
  • 12. Traditional Direct Marketing Income ResponseRate 0 + + x 2 y 2 x 1 y 1 Response Rate Correlation: Linear Analysis - Two Dimensional
  • 13. Database Marketing Income ResponseRate 0 + x 1 y 1 Response Rate Correlation: Linear Analysis - Three Dimensional
  • 14. Database Marketing Income ResponseRate 0 + Age Response Rate Correlation: Multiple Linear Analysis - Three Dimensional
  • 15. Database Marketing Income ResponseRate 0 + Age Response Rate Correlation: Multiple Linear Analysis - Three Dimensional
  • 16. Database Marketing • RFM (recency, frequency, monetary) • CHAID (decision tree) • Linear and Logistical Regression • Neural Networks, Systems Engineering Modeling Methods
  • 17. Database Marketing • Select historical response data – (past direct mail campaigns) • Analyze the respondents and the non- respondents – 2,000 - 5,000 each • Score (i.e. 0-999) • Segment (quintiles, deciles, etc.) Process
  • 18. The Intelligent Closed Loop is a Source for Positive and Negative Response Data, Demographics and Payment History that is used to Build Predictive Scoring Models Customer-Database- Purchasing Information Target Selection •Acquisition •Up-Sell •Cross-Sell •Retention Message Selection Implementation Printing -- Data Processing Personalization -- Letter-Shop Response Analysis Data Cleaning Data Upgrades •Purchasing Data •Demographics •Psychographics Customer Profile Predictive Model Yes No
  • 19. Modeling Methodology Used by Telematch, US Sprint and Experian Large Heterogeneous Mixture of All Customers on a File Segment A Homogeneous Segment B Homogeneous Segment C Homogeneous Segment A Response Model Segment B Response Model Segment C Response Model List Selection Segment C List Selection Segment B List Selection Segment A Responders & Non - Responders Demographic Attributes Each List Selection Goes to Telematch
  • 20. Database Marketing Focuses on Individual Positive and Negative Consumers to Build Predictive Scoring Models • Negative Consumers • Non-Responders • Non-Convert to Customer • Amount of Order is Unprofitable-Unpaid Bill • Customer Lifetime Value is Low • Financial Performance- Low • Positive Consumers • Responders • Converted to Customer • Amount of Order is Profitable-Paid Bill • Customer Lifetime Value is High • Financial Performance- High
  • 21. The Enhanced Modeling Database is used to build Predictive Models with a Positive and Negative Customer Profile. These Positive and Negative Profiles can then be used to Score the Entire Range of a Prospect File or List Positive Customer Profile Negative Customer Profile Male Age 45 Sales HH Income $55K Female Age 36 Professional HH Income $65K
  • 22. Prospects that closely match or are exact opposites of your positive customer profile are the easiest to identify. Predictive Power of Models Strong Negative Average Strong Positive Correlation +1.0-1.0 Correlation Positive Customer Profile Negative Customer Profile . . . . . . . . . . . . . . . . . . . .
  • 23. Multivariable Regression-Simplified y x Positive Value Dependent Variable 1.0 Purchasers/Donors Negative Value Dependent Variable -1.0 Non-Purchasers Independent/Predictor Variables V Regression is a technique for finding and describing a functional (mathematical) relationship between a dependent variable (Purchaser) and one or more independent or predictor variables. v 1 2
  • 24. Database Marketing Database Append Sample Mailing Model Score Segment Validate Non Response Mail LISTLISTLIST Responses =Past Mailing Positive Response Responses Responses Process
  • 25. Segment Score Range Database Quantity Anticipated % Rate Segment 3 999 to 574 255,752 0.81% Segment 2 573 to 465 606,371 0.72% Segment 1 464 to 0 975,460 0.47% 1,837,583 0.61% Segment Analysis Segment 3 14% Segment 1 53% Segment 2 33% Response Rate Analysis 0.00% 0.10% 0.20% 0.30% 0.40% 0.50% 0.60% 0.70% 0.80% 0.90% Segment 3 Segment 2 Segment 1 Avg. % Rate Database Marketing 33% lift Final Analysis: Example of a homogeneous audience
  • 26. Database Marketing In a customer file with purchasing history and external demographic data it is possible to build models that have a lift of 150% over the average response rate and have a lift of 10 to 1 from the best to the worst decile in the file. 0 0.5 1 1.5 2 2.5 1 2 3 4 5 6 7 8 9 10 Percent Response Rate Decile s 150% Lift above Average 1,000% Lift Best to Worst Decile 1%Average Response
  • 27. Two Dimensional Matrix Probability to Respond & Estimated Spending Respons e High Low Spending High Low DECILES 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
  • 28. The Customer Life Cycle and Marketing Activities LoseRetainDevelopAcquire Network Reactivating Retaining Cross Selling Upselling NetworkingConverting Relationship Marketing Prospecting
  • 29. Objectives and Benefits of Modeling • Acquisition Models: Raise response rates, number of orders and amount of orders; acquire customers with higher lifetime value, lower return rates and higher payment rates. • Conversion Models: convert customers with high lifetime value and medium and long term profitability. • Relationship Marketing Models: Increase survival time and lifetime value of customers. • Cross selling and Up-selling Models: Increase business base with customers selling greater depth and width of products and services. • Networking Models: Acquire new leads to be filtered through acquisition models. • Retention Models: Increase all positive indicators of customer performance while preventing customer defection. • Reactivation Models: Restoring performance of inactive customers clients or donors.
  • 30. Case Study: New Customer Acquisition Blue Cross/Blue Shield Federal • Blue Cross/Blue Shield budget only permits mailing 1.25 million names on their prospect list of 2.2 million names. • Previous years-random deselection was used to reduce mailing quantity. • Created a new customer acquisition model, validate, overlay prospect file with demographic data from R.L. Polk, score the file and select best 1.25 million names. • Saved over $9 Million in foregone revenue-for a cost of $50,000- 180 to 1 Return on Investment. • Blue Cross/Blue Shield Federal now owns a predictive model that is being updated and used for future mailings.
  • 31. Database Marketing Strategic Applications • Strategic Business Development: Use a new customer acquisition model to build the customer base with profitable new customers- with good lifetime value. • Improve financial performance through customer development. Target customers for cross-selling and up- selling. Increase the amount and frequency of purchases from the customer base. • Improve customer retention by identifying “at risk” profitable customers before they fire your firm. Offer special products and services to save these “at risk” customers. • Practice Relationship Marketing by offering special products, services and communication targeted to a segmented customer database. • Apply Financial Models to all stages of the Customer Life Cycle. Improve new customer acquisition, customer development and retention. This will work like compound interest to dramatically improve growth and earnings.