The Art and Science of

Database Marketing

Robert J Fulmer – Database(d) Marketing. 19701 Grayheaven Manor Rd. Olney, MD 20832

Cell 240-328-5846 robert@robertjfulmer.com

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...

• 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 Experience-Projects for USA

Today, The Wall Street Journal, NBC Television, Fox

Broadcasting, Hearst, VNU

• Media Planning Experience-Toyota, Procter and

Gamble-Saatchi and Saatchi

• Strategic 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 Marketing

• Focus on Lists of

Consumers or

Businesses

• Uses Descriptive

Research to describe

Past List Performance

• Limited Targeting

Capability

• Database 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 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

Res

ponse

Rat

e

0 +

+

Response Rate Correlation:

Response Rate and File Information

Traditional Direct Marketing

Income

Res

ponse

Rat

e

0 +

+

x

2

y

2

x

1

y

1

Response Rate Correlation:

Linear Analysis - Two Dimensional

Database Marketing R

esponse

Rat

e

0

+

x

1

y

1

Response Rate Correlation:

Linear Analysis - Three Dimensional

Database Marketing R

esponse

Rat

e

0

+

Response Rate Correlation:

Multiple Linear Analysis - Three Dimensional

Database Marketing R

esponse

Rat

e

0

+

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

LIST LIST LIST

Responses = Past Mailing

Positive Response

Responses

Responses

Process

S egm ent S core Range Database Quantity A nticipated % Rate

S e g m e n t 3 9 9 9 to 5 7 4 2 5 5 ,7 5 2 0 .8 1 %

S e g m e n t 2 5 7 3 to 4 6 5 6 0 6 ,3 7 1 0 .7 2 %

S e g m e n t 1 4 6 4 to 0 9 7 5 ,4 6 0 0 .4 7 %

1 ,8 3 7 ,5 8 3 0 .6 1 %

Segment Analysis

Segment 3

14%

Segment 1

53%

Segment 2

33%

R e sp o n se R a te A n a ly sis

0 .0 0 %

0 .1 0 %

0 .2 0 %

0 .3 0 %

0 .4 0 %

0 .5 0 %

0 .6 0 %

0 .7 0 %

0 .8 0 %

0 .9 0 %

Se g m e n t 3 Se g m e n t 2 Se g m e n t 1

A vg . % R a te

Database Marketing 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

Deciles

150% Lift above Average

1,000% Lift Best to Worst Decile

1%Average Response

Two Dimensional Matrix Probability to Respond & Estimated Spending

Response 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

Lose Retain Develop Acquire

Network

Reactivating

Retaining Cross

Selling

Upselling

Networking Converting

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.