What is Database Marketing?
Arthur Middleton Hughes
VP Solutions Architect
KnowledgeBase Marketing
What KnowledgeBase
Marketing Does
Selected List of Clients
How a modern database
system works
Mail, Email, Phone
Customer
Transactions
Marketing
Database
Inputs from Retail,
Phone, Web
Updated
several
times per
day
Data Access
And Analysis
Software
Appended
Data
Marketing
Staff
Access on
the web
Compared with newcomers,
Long term customers:







Buy more per year
Buy higher priced options
Buy more often
Are less price sensitive
Are less costly to serve
Are more loyal
Have a higher lifetime value
Retention is the way to
measure loyalty
90%
80%
70%
Percentage
Retained
from
Previous
Year
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
Years as a customer
5
Retention pays better than
acquisition
Annual Profit
$48
$60
$40
$20
$0
($20)
($40)
($60)
($80)
($62)
New Customer
3rd Year
Customer
Two Kinds of Database People

Constructors
People who build databases
Merge/Purge, Hardware, Software

Creators
People who understand strategy
Build loyalty and repeat sales

You need both kinds!
What doesn’t work:
Treating all customers alike
79.67%
This 28% lost 22% of the
bank’s profits!
80.00%
60.00%
24.82%
40.00%
15.83%
1.52%
20.00%
0.00%
-20.00%
Bank Customers by Profitability
-21.83%
-40.00%
5%
11%
28%
28%
28%
Marketing to Customer
Segments
Spend Service
Your Best Customers 80% of Revenue
Your Best Hope for New
Gold Customers
1% of Total
Revenue
GOLD
Move Up
These may be losers
Dollars Here
Spend Marketing
Dollars Here
Reactivate or
Archive
Examples of Profitable
Strategies






Newsletters
Surveys and Responses
Loyalty Programs
Customer and Technical Services
Friendly, interesting interactive web site
Event Driven Communications
What proves that relationship
building works?





Manufacturer of building products
Catalog sent to 45,000 contractors
Previous policy: wait for the orders
Test: pick 1,200 customers, split into
test of 600 and control of 600
Two person pilot program build
relationship with test customers to see
the results
Credit: Hunter Business Direct
What did they offer?

Follow up on bids and quotes
Schedule product training
Ask about customer needs
New Product information

They did not offer discounts



Change in the number of
orders
112%
120%
82%
100%
Change in
number of
orders
80%
60%
40%
20%
0%
1
2
Control vs Test Groups
Change in the Average Order
Size
114%
120%
86%
100%
80%
Change in
average 60%
order size
40%
20%
0%
1
2
Control vs Test Group
Total revenue gain: $2.6
million dollars
127%
140%
120%
100%
Change in
total
revenue
70%
80%
60%
40%
20%
0%
1
2
Control vs Test Group
This stuff works!



Building a relationship with
customers can be highly profitable
Using a database to recreate the
old family grocer is a winning
strategy
Business to business relationship
marketing is the way to go
What is lifetime value?




Net present value of the profit to be
realized on the average new customer
during a given number of years.
Lifetime value is “Good Will.”
To compute it, you must be able to
track customers from year to year.
Main use: To evaluate strategy.
Lets look at a retail operation


Before and after a loyalty program
Lets begin with a loyalty building
communication
Event driven communication:
Dear Mr. Hughes:
Ridgeway Fashions
Leesburg, VA 22069
I would like to remind you that your wife Helena’s birthday is
coming up in two weeks on November 5th. We have the perfect gift
for her in stock.
As you know, she loves Liz Claiborne clothing. We have an
absolutely beautiful new suit in blue, her favorite color, in a
fourteen, her size, priced at $232.00.
If you like, I can gift wrap the suit at no extra charge and
deliver it to you next week, so that you will have it in plenty of
time for her birthday. Or, I can put it aside so you can come in to
pick it up. Please call me at (703) 754-4470 to let me know which
you’d prefer.
Sincerely yours,
Robin Baumgartner
Robin Baumgartner, Store Manager
LTV Before New Strategies
Year 1
40%
200,000
1.4
$50
$14,000,000
Year 2
45%
80,000
1.6
$60
$7,680,000
Year 3
50%
36,000
1.8
$70
$4,536,000
50%
Cost Percentage
$7,000,000
Costs
$6,400,000
Acquisition Cost $32
$13,400,000
Total Costs
49%
$3,763,200
48%
$2,177,280
$3,763,200
$2,177,280
$600,000
1
$600,000
$600,000
$3.00
$3,916,800
1.12
$3,497,143
$4,097,143
$20.49
$2,358,720
1.32
$1,786,909
$5,884,052
$29.42
Retention Rate
Customers
Visits Per Year
Spending Per Visit
Revenue
Profit
Discount Rate
NPV Profit
Cum NPV Profie
Lifetime Value
New Retention Strategies
Provide all customers with a card or
register their credit cards
Birthday Club
Communicate with them
Give them premiums if they shop a lot
Lets see what could happen
With New Strategies
Retention Rate
Customers
Visits Per Year
Spending Per Visit
Revenue
Year 1
50%
200,000
1.6
$55
$17,600,000
Year 2
60%
100,000
2
$70
$14,000,000
Year 3
65%
60,000
2.4
$80
$11,520,000
Cost Percentage
Costs
Acquisition Cost $32
Database Costs
Loyalty Program
Loyalty Costs
Total Costs
50%
$8,800,000
$6,400,000
$500,000
$5.00
$1,600,000
$17,300,000
49%
$6,860,000
48%
$5,529,600
$250,000
$8.00
$1,600,000
$8,710,000
$150,000
$10.00
$1,440,000
$7,119,600
$300,000
1
$300,000
$300,000
$1.50
$5,290,000
1.12
$4,723,214
$5,023,214
$25.12
$4,400,400
1.32
$3,333,636
$8,356,851
$41.78
Profit
Discount Rate
NPV Profit
Cum NPV Profie
Lifetime Value
Effect of adoption of new
strategies
Old LTV
New LTV
Change
With 200,000 members
Year 1
$3.00
$1.50
-$1.50
-$300,000
Year 2
$20.49
$25.12
$4.63
$926,071
Year 3
$29.42
$41.78
$12.36
$2,472,799
Five Ways to Boost LTV with
DB Strategies





Increase the retention rate
Increase the referral rate
Increase the spending rate
Decrease the direct costs
Decrease the marketing costs
Who is going to defect?



Besides LTV, you can develop a model
that predicts which customers are most
likely to leave.
Putting that model with LTV you can
refocus your entire retention strategy
You create a Risk Revenue Matrix
Focus on A and B: 44% of
your customers.
LTV
High
Medium
Low
Probability of Leaving Soon
High
Medium Low
Priority A Priority B Priority C
Priority B Priority B Priority C
Priority C Priority C Priority C
AmeriLINK
Comprehensive Data on over 230 Million
People in the U.S.
Compiled on an Individual Level
Updated Every 6 Weeks
Sources include Drivers License, Voter
Registration, multiple purchase
transactions, i.e. shopping behavior,
Telephone Sources, Children’s Data, Tax
Assessor/Property Deeds, Occupational
Licenses, Internet Data, Product
Registration, Survey Data, Summarized
Credit Statistics and Multiple Lifestyle
Sources
AmeriLINK
58% Mail Order Buyers
34% Multiple Mail Order Buyers
57% Homeowners
65% with Bankcards, 30% with
Multiple Bankcards
2000 Census Data Currently
Available
Quantifiable Verification – 57%
Last 12 Months
25% Known On-Line
Households
AmeriLINK


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
Web Accessible
100 % Exact Age Available
50% Females by First Name
40% Coded with Lifestyle
Data from Surveys
100% Coded with Terrestrial
Attitudinal Clusters – LWA
and MindBase
54% Coded with
Internet Behavioral
Clusters – Digital
Neighborhoods
Email Address Coverage on
AmeriLink
AmeriLink Production Email Coverage
80,000,000
70,000,000
60,000,000
50,000,000
40,000,000
0212
30,000,000
0312
20,000,000
10,000,000
0
Individual
Household
Online Access
Version Individual
Household Online Access
0212
7,809,156 23,013,716
55,178,025
0312
21,556,071 45,292,792
75,250,009
Digital Neighborhoods
A unique segmentation solution
Digital Neighborhoods(SM), a unique segmentation
scheme developed jointly by KnowledgeBase
Marketing® and its parent company,
Wunderman, segments consumers by einvolvement, which measures consumer presence
online, relationships with sites and transaction
activity. When you understand consumers’ link to
the Internet, you can optimize your email
marketing efforts by customizing and
personalizing your offers and messaging.
Digital Neighborhoods
Direct Sale Cycle
Prudential Agent
Step 1
Step 3
Step 2
Pre Qualification
Of Prospects
Design and Create
campaign
Email to Prospect or
Customer
Real Time Reports on Campaign Progress
Welcome Arthur to
Prudential. To get
information on an LTC
policy click here.
Bob Winslow, Agent
Micro-Site
Prudential Marketing Staff
Step 4
Prizm Cluster Codes
01
Blue Blood Estates
1.18%
Elite
Privileged Super Rich Families
02
Winner's Circle
2.15%
Wealthy
Executive Suburban Families
03
Executive Suites
1.32%
Affluent
Upscale White-Collar Couples
04
Pools & Patios
1.85%
Affluent
Established Empty Nesters
05
Kids & Cul-de-Sacs
2.93%
Affluent
Upscale Suburban Families
06
Urban Gold Coast
0.59%
Affluent
Professional Urban Singles & Couples
07
Money & Brains
1.12%
Affluent
Sophisticated Townhouse Couples
08
Young Literati
0.94%
Upper Middle
Upscale Urban Couples & Singles
09
American Dreams
1.40%
Upper Middle
Established Urban Immigrant Families
10
Bohemian Mix
1.48%
Middle
Bohemian Singles & Couples
11
Second City Elite
1.89%
Affluent
Upscale Executive Families
12
Upward Bound
1.83%
Upper Middle
Young Upscale White-Collar Families
More Cluster Codes
13
Gray Power
2.03%
Middle
Affluent Retirees in Sunset Cities
14
Country Squires
1.33%
Wealthy
Elite Exurban Families
15
God's Country
2.63%
Affluent
Executive Exurban Families
16
Big Fish, Small Pond
1.37%
Upper Middle
Small Town Executive Families
17
Greenbelt Families
1.48%
Upper Middle
Young Middle-Class Town Families
18
Young Influentials
1.35%
Upper Middle
Upwardly Mobile Singles & Couples
19
New Empty Nests
2.06%
Upper Middle
Upscale Suburban Fringe Couples
20
Boomers & Babies
1.11%
Upper Middle
Young White-Collar Suburban Families
21
Suburban Sprawl
1.50%
Middle
Young Suburban Townhouse Couples
22
Blue-Chip Blues
1.93%
Middle
Upscale Blue-Collar Families
23
Upstarts & Seniors
1.28%
Middle
Middle Income Empty Nesters
24
New Beginnings
1.19%
Middle
Young Mobile City Singles
Gains From Cluster Coding
Universe
Mailing
Responses
Response Rate
Revenue
Mail Cost
Cluster Cost
Profit
Gain
1,005,160
16,083
1.60%
$1,222,275
$562,890
$659,385
Good
Bad
Clusters
Clusters
460,445
544,715
14,274
1,809
3.10%
0.33%
$1,084,808 $137,466
$257,849 $305,040
$30,155
$796,804 -$167,574
$137,419
Recency Frequency Monetary
(RFM) Analysis




Used for marketing to customers
Always improves response and profits
Better than any demographic model
The most powerful segmentation
method for predicting response
How to Apply Recency Codes





Put most recent purchase date into
every customer record.
Sort database by that date - newest to
oldest.
Divide into five equal parts - quintiles.
Assign “5” to top group, “4” next, etc.
Put quintile number in customer record.
Response Rate
Response by Recency Quintile
4.00%
3.50%
3.00%
2.50%
2.00%
1.50%
1.00%
0.50%
0.00%
3.49%
1.25% 1.08%
5
4
0.63%
3
Recency Quintile
2
0.26%
1
How to Compute a Frequency
Index





Keep number of transactions in
customer record
Sort recency groups from highest to
lowest
Divide into five equal groups
Number groups from 5 to 1
Put quintile number in customer record
Response by Frequency Quintile
2.50%
1.99%
2.00%
Response Rate
1.56%
1.50%
1.31%
1.00%
0.92%
0.93%
2
1
0.50%
0.00%
5
4
3
Frequency Quintile
How to Compute a Monetary Index





Store total dollars purchased in each
customer record
Sort frequency groups from highest to
lowest
Divide into 5 equal groups (quintiles)
Number quintiles 5, 4, 3, 2, 1
Put quintile number in each record
Response by Monetary
Quintile
1.80%
1.61%
1.60%
1.45%
1.46%
1.40%
1.22%
1.23%
2
1
1.20%
1.00%
0.80%
0.60%
0.40%
0.20%
0.00%
5
4
3
RFM Code Construction
R
5
F
35
4
34
3
33
2
32
31
1
Database
One Sort
Five
Sorts
M
335
334
333
332
331
Twentyfive sorts
Creating an Nth
300,000 Records
Customer Database
For Nth by 10, select every
tenth record.
Nth
30,000 Records
Result will be
statistical replica of
database
Result of Test Mailing to
30,000
#
1
2
3
4
5
RFM
555
554
553
552
551
Mailed
240
240
240
240
240
Response
20
16
13
10
11
Rate
8.15%
6.56%
5.62%
4.33%
4.51%
6
7
8
9
10
545
544
543
542
541
240
240
240
240
240
9
12
6
10
7
3.78%
4.98%
2.88%
4.26%
3.10%
11
12
13
14
535
534
533
532
240
240
240
240
10
9
8
6
4.13%
3.83%
3.35%
2.70%
Test Response Rate by RFM Cell
Index of Response 0 = Break Even
500
400
300
200
100
0
-100
-200
555
455
355
255
111
Test, Full File & RFM
Selects Compared
Response Rate
Responses
Net Revenue
No. Mailed
Mailing Cost
Profits
Test
Full File RFM Select
1.34%
1.17%
2.76%
402
23,412
15,295
$16,080 $936,480 $611,800
30,000 2,001,056
554,182
$16,500 $1,100,581 $304,800
($420) ($164,101)
$307,000
8.00%
Test Vs Rollout Response
Rates
7.00%
7.00%
6.00%
6.00%
5.00%
5.00%
4.00%
4.00%
3.00%
3.00%
2.00%
2.00%
1.00%
1.00%
0.00%
0.00%
554 553 552 551 545 544 543 542 541 535 534 533 532 531 525 524 523 522 521 515 514 513 512 511 455 451 445 444 443 355 354 351 344
When NOT to Use RFM






If you use it all the time, half your
customers will never hear from you
They will be lost
The others will suffer from file fatigue
Use it sparingly; When you need a
boost
Use it to identify your best customers
Don’t go hog wild!
A few recent case studies
Modeling to increase response


Auto insurance mailed 1.2MM per mo.
Model showed how to pick right HH
Total Mailed
Cost of Mailing
Number of Responses
Response Rate
Number of Sales
Sales Rate
Total Revenue
Revenue per Sale
Profit
Return on Promotion
Control
Group
1,264,571
$547,559
13,366
1.06%
1,599
12.0%
$2,605,603
$1,630
$95,896
18%
Optimized
Group
1,264,571
$547,559
16,090
1.27%
2,323
14.4%
$3,158,151
$1,360
$187,851
34%
%
Change # Change
0%
0
0%
0
20%
2,724
20%
0.22%
45%
724
21%
2.47%
21% $553,208
-17%
($270)
96%
$91,955
96%
16.8%
Isuzu Post Card Direct


To sell their trucks, Isuzu developed 24
different postcards, each with a
different case study of truck use such
as Gardening, Plumbing, Moving.
They set up a web site and used direct
mail to get dealers to come to the site
and order post cards for prospects
developed by Isuzu.
Isuzu Results


Dealers modeled to determine their
likelihood to purchase.
The model really worked:
Group Companies
High Score
Medium Score
Low Score
Control Group
Total
Totals
1,899
1,855
1,890
520
6,164
Buying
97
32
15
3
144
Buying
Rate
5.10%
1.70%
0.80%
0.60%
2.30%
Trucks
Sold
107
34
16
3
160
HP On Line Survey
Hewlett Packard tested an on line survey
to promote their network printers.
Direct mail drove responders to a web
site that contained the survey. Responders
received $10 in Pizza Hut coupons.
The survey provided a special HP offer
for network printing solutions, product links,
and e-subscription information.
Competitive Advantage Through Advanced Technology
Universal Music
eMarketing Campaign
UMG tested an e-marketing strategy to increase
record sales of a new album release for Lucinda
Williams
The campaign delivered more than a 960%
increase to the Lucinda Williams fan base
Reached nearly 200,000 fans and prospects with
email communications
Won DMA Gold Echo Award in 2002
What Works:
Email Marketing
Where else can you get these
results in 3 days?
Speech Email Follow Up Results on 3rd Day
Delivered
Viewed
Clicked
Do Not Mail
168
131
75
0
Download % Viewed % Clicked % Download
68
78%
45%
40%
Get customers to join a club


A company sold sporting goods created
a member club.
When DB was built they learned that:


Club members bought 11 times more than
non club members.
In two years, 81% of club members
became multi-buyers.
Club members conversion
80.5%
90.0%
Conversion to MultiBuyers after two years
80.0%
70.0%
60.0%
23.4%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Goal Club
Non Goal Club
Cataloger Example


Miles Kimball sent 20,000 emails with
three different catalogs, and 20,000 with
the three catalogs alone.
Those who got the emails bought 18%
more than those who got the catalogs
alone.
118
120
115
110
105
100
100
95
90
Control
Test
Retailer Example



Video retailer sent email newsletters to
170,000 customers for 6 months.
Control group of 14,000 got no emails
Retail sales to test group was 28% more than
to those without emails.
128
140
100
120
100
80
60
40
20
0
Control
Test
Books by Arthur Hughes
From McGraw Hill. Order at
www.dbmarketing.com
Contact Arthur: arthur.hughes@kbm1.com