Download Simple Database Marketing Tools

Simple Database Marketing Tools for Great Marketing Results
Yu-Hui Tao
Department of Information Management
I-Shou University
[email protected]
C. Rosa Yeh
Business Management Consultant
[email protected]
ABSTRACT
Database marketing is an important approach for many businesses in their marketing
activities. It uses current advanced information technology to help businesses be more
effective and profitable. Many tools used in a marketing database derive information from the
existing customer information in their data warehouse. This paper introduces two simple but
essential tools--usage segment code and net revenue equation--and their application in the
credit-card business. The powerful result and the rich application of these two simple tools
will prove that simple yet creative ideas can be converted into database marketing tools to
increase the return of investment in such a marketing database.
Keywords: credit card; database marketing; data mining; marketing tools; OLAP
簡易資料庫行銷工具建行銷大功
陶幼慧
義守大學資訊管理學系
[email protected]
葉俶禎
企業管理顧問
[email protected]
摘要
資料庫行銷是現今許多企業行銷活動的重要方法之一。它運用先進的資訊科技來提昇企
業的效率與獲利率。許多行銷資料庫的工具都是從現有資料倉儲中的顧客資料推導出
來。本文介紹兩個簡單但基本的工具—使用區隔代碼及淨盈利公式--及它們在信用卡業
的應用。它們表現的效能與豐富的應用範圍印証了即使是簡單但具有創意的觀念也可以
被應用到資料庫行銷來增進其投資報酬。
關鍵字：信用卡，資料庫行銷，資料探掘，行銷工具，線上分析處理。
1. Introduction
Marketing is the “process of
executing
the
conception,
pricing,
promotion, and distribution of ideas, goods,
and services to create exchanges that satisfy
individual and organizational goals“ [1].
Database marketing is an approach to
generate integrated and accessible customer
information to help the marketers better
target their marketing efforts to existing
customers or prospects. In other words, the
ultimate goal of database marketing is to
create a win-win situation for both the
marketers and the customers by reducing
marketing costs, increasing sales and profits,
and building customer loyalty.
The marketing database, if done
correctly, can assist marketing managers
from daily operation, resource allocation,
budget planning, to strategic decision
processes. Some of the well-known database
marketing
tools
are:
the
Recency-Frequency-Monetary
(R-F-M)
Formula, the lifestyle segment of the
existing customers and the lifetime value of
a customer [5]. RFM formula is often used
to identify the best customers who had
bought most recently, who had bought most
frequently within a specified period, and
who had spent specified amounts. Lifestyle
segment is a customer profile composed of
the geo-demographics, psychographics,
lifestyles, attitudes and purchase behaviors.
Lifetime value is an estimate of the profit a
customer can generate over his/her
relationship with the company.
There are many commercial database
marketing tools available, such as
Metromail’s DNA [3,4] and EQUIFAX’s
MicroVision [2]. However, any company
with a modest customer database can start
their own database marketing activities with
simple tools in-house. Usage Segment Code
(USC) and Net Revenue Equation (NRE)
are
simple
existing
customer-based
marketing tools, which had been proven
useful in a top-ten credit-card issuer in
understanding customer behaviors and in
various other marketing activities. USC is
the spending pattern of a customer while
NRE is the retrospective estimated profit
from a customer during a certain period.
This article introduces and illustrates these
two marketing tools and their applications in
a US credit-card issuer. However, these two
tools can be applied to any membership-type
business, such as retail banking, mortgage,
mail order, department stores, insurance
services, auto clubs, airlines, and hotels.
2. Database Marketing Tools
We will define and illustrate USC and
NRE in each of the following sections. The
illustration is based on, but not limited to,
the application in the credit-card business.
2.1 Usage Segment Code (USC)
USC is a way to cluster existing
customers (cardholders) into distinct groups
based on customer history, such as the last
12-month of total spending, type of
spending (e.g., purchase or cash advance),
percentage of type of spending (e.g., cash
advance over the sum of purchase and cash
advance), number of months paying
interests, delinquent status and history, other
status code, and so on.
The criteria of each segment should
be fine enough so that they can be either
used individually for a certain marketing
activity which requires very fine clusters, or
further
combined
into
larger
groups/segments for appropriate marketing
activities. Table 1 illustrates a partial
40-segment
scheme.
The
complete
40-segment scheme can be further grouped
into larger groupings such as new customers
vs. old customers, or transactors vs.
revolvers. Table 1 demonstrates the old
customer segments (USC from 25-40)
which include customers who have been on
the book for over 12 months (Months On
Book (MOB) >= 12). The young customer
segments (USC 9-24) which are not shown
in Table 1 have similar definitions as the old
customer segments except their MOBs are
smaller than 12. In this case, we define
transactors as those customers who paid
their balances off every month; and
revolvers as those who paid at least
one-month interest over the last 12 months.
Revolvers are preferable customers because
they positively contribute to the credit card
company’s bottom line.
Table 1. Usage Segment Code Table
USC
Description
Criteria
25 Old Severe Delinquency
MOB >=12 and (>0 60 DPD, >4 30 DPD, or >10 5-DPD)
26 Old Problem Payer
MOB >=12 and ((>0 30 DPD or >5 5-DPD)
27 Old Mild Delinquency
MOB>= 12 and >2 5 DPD
28 Old Never Active
MOB>= and No Interest paid and No Balance
29 Old High Bal. Transactor
MOD>=12 and No Interest paid and Avg. Bal. >= $250
30 Old Transactor
MOB>=12 and No Interest paid and Avg. Bal. < $250
31 Old Low Revolver-Cash User
MOB>=12, Paid 1-7 Mon. of Int. and Cash Bal. > 20%
32 Old Low Revolver-High Bal.
MOB>=12, Paid 1-7 Mon. of Int. and Avg. Bal. >= $250
33 Old Low Revolver
MOB>=12, Paid 1-7 Mon. of Int. and Avg. Bal. < $250
34 Old Med. Revolver-Cash User
MOB>=12, Paid 8-11 Mon. of Int. and Cash Bal. > 20%
35 Old Med. Revolver-High Bal.
MOB>=12, Paid 8-11 Mon. of Int. and Avg. Bal. >= $1,000
36 Old Med. Revolver
MOB>=12, Paid 8-11 Mon. of Int. and Avg. Bal. <$1,000
37 Old High Revolver-Paying Down
MOB>=12, Paid 12 Mon. of Int. and Avg. Bal. = $0
38 Old High Revolver-Cash User
MOB>=12, Paid 12 Mon. of Int. and Cash Bal. >90%
39 Old High Revolver-High Bal.
MOB>=12, Paid 12 Mon. of Int. and Avg. Bal. >= $2,000
40 Old High Revolver.
MOB>=12, Paid 12 Mon. of Int. and Avg. Bal. <$2,000
2.2 Net Revenue Equation
may include the following components:
Net revenue equation is a formula
used to calculate the net revenue of an
existing customer in a periodical interval,
such as a month or a cycle. The accuracy of
NRE depends on the ongoing revenue
dynamics of the customers and various costs
of the company processes. Due to the
complexity of the calculation and the
complicated nature of a business, it is
difficult to accurately determine NRE.
However, once the guidelines are made and
agreed upon, NRE can be a very useful tool
to show how profitable a customer is.
NRE =
NRE bases simply on a customer’s
periodical contribution to the company’s
profit. That means, we quantify a customer
by his/her contribution in revenue without
other risk factors. A sample cycle-day NRE
Fees_Income + Interchange_Income
+ Functional_Cost + Loss +
Reward_Cost
Note that:
1. Fee income includes all possible income
due to fees, such as purchase fees, cash
advance fees, insurance fees, late fees
and other fees. Normally this is a
positive item.
2. Interchange income includes both the
purchase and the cash advance each with
a different interchange rate. Normally
this is a positive item.
3. Functional costs include costs in all
functional areas for supporting the
product on a cost per person basis. The
functional cost rates are usually different
for a delinquent account and a
non-delinquent account. Normally this is
a negative item.
4. Loss is mainly the total amount of
charge-off’s. Normally this is a negative
item if there is any charge-off. Most
accounts have this item equal to zero.
5. Reward cost is related to the cost in
supporting reward prizes during product
promotion. For example, the reward can
be a cash rebate, bonus points, or
coupons. Each reward has a cost
structure associated with it. Normally
this is a negative item if the product is a
reward based product. For non-reward
based products, this item is set to zero.
3. Applications
The power of USC and NRE is best
shown in back-end analyses when these two
tools collaborate. Results of these back-end
analyses help marketers better understand
customers’ consumption behaviors and how
these behaviors impact the company’s profit.
They are also used for non-marketing
activities, such as those in the retention area
whose primary goal is to retain those
valuable customers who are leaving the
bank. We will illustrate this joint application,
and show you how a credit-card product
manager used it in a migration analysis to
learn both the customer profit dynamics and
ways to better target different customer
groups.
3.1 Usage Segment Migration Analysis
Combining USC and NRE allows us
to look at the USC migration patterns over a
certain period and learn how the
corresponding NREs change. Once the
USC/NRE migration patterns are clear, the
marketing/product managers can better
target their marketing resources to retain
those segments who migrate into preferable
segments and to shift adverse migration
behaviors toward preferable patterns.
Before looking into the migration
patterns, we profiled each usage segment in
the old customers group in Table 1 with
their average NRE, % of accounts, Return
On Assets (ROA), Average Purchase
Balance, Average Cash Balance, and % of
Net Revenue. (See Table 2.) This profile
gave the managers and the analysts a better
idea of the characteristics of different
customer segments before any further
analyses.
Table 2. Customer Profile by USC as of January 1996
USC
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Total*
Avg. NRE
% of Accts
ROA
Avg. Pur. Bal.
$20.86
0.1% 12.8%
$1,425
$26.46
2.5% 15.9%
$1,513
$28.49
3.1% 15.9%
$1,649
($0.14)
10.5%
N/A
$0
$1.36
2.4%
2.1%
$792
($0.05)
12.7% -1.1%
$55
$9.07
1.3% 11.2%
$349
$8.07
3.8%
9.3%
$1,033
$0.89
5.5%
8.7%
$99
$14.88
1.5%
9.3%
$607
$25.18
1.2% 12.5%
$2,371
$5.57
2.0% 12.1%
$471
8.3% 14.2%
$1,802
$26.85
$32.66
1.1% 12.5%
$767
1.0% 14.2%
$3,448
$48.18
$17.33
1.0% 14.0%
$1,174
$6.46
100% 10.9%
$494
Avg. Cash Bal.
% Net Revenue
$534
0.2%
$483
11.7%
$503
15.8%
$0
-0.3%
$0
0.6%
$0
-0.1%
$626
2.1%
$8
5.5%
$24
0.9%
$1,322
4.0%
$37
5.3%
$83
1.9%
$475
39.3%
$2,364
6.5%
$617
8.5%
$316
3.0%
$217
100.0%
*: The total includes numbers from USC 1 to 40, which is not completely shown here.
When customers migrate from one
usage segment to another, the value of their
NRE changes. The first step in the migration
analysis is a 40 by 40 USC matrix of the
customer counts and the corresponding NRE
changes, which served as the master
migration data map. The data sets can be a
quarter, 6 month or a year apart in order to
see the percentage changes over time. Then,
we partitioned the master matrix into easily
analyzed groupings. For example, Table 3
illustrates the partial USC-count% matrix
with the USC data in January 1995 and
January 1996 for the old accounts group.
For easy reference, this table was arranged
so that the highlighted figures lined up in a
diagonal fashion. We quantified the gain or
loss in each cell by the average NREs from
the original 40 by 40 USC matrix. For
example, nearly 30% of the high-balanced
high revolvers (USC 39) turned into
paying-down high revolvers (USC 37) over
the year of 1995 with the loss of nearly $22
per cardholder (See Table 2, $48.18-$26.85),
a vivid evidence of the lack of effective
retention effort to keep the high-balanced
high revolvers in their most profitable status
a year ago.
The cells in bold prints were the target
of investigation during our analysis. For
example, 62.5% of the inactive group (USC
28) in 1995 remained inactive in 1996,
which indicated the need to promote the
usage of their credit cards. Also, over 50%
of those transactors (USC 30) in 1995
remained transactors a year later, which
indicated the need to motivate these
customers to revolve; otherwise, to prevent
further losses to the company, strategies
were needed to let these customers leave.
For those high revolvers (USC 37-40), some
stayed where they were or revolved more,
but more were moving into less revolving
status, which indicated we might be losing
our best customers.
Table 3. Customer Migration Analysis - Old Accounts from 1/95 to 1/96
1996
1995
USC 28
USC 29
USC 30
USC 31
USC 32
USC 33
USC 34
USC 35
USC 35
USC 37
USC 38
USC 39
USC 40
USC
26
0.3%
0.5%
0.7%
3.2%
2.4%
1.7%
3.5%
3.1%
2.2%
3.1%
3.8%
2.8%
3.6%
USC
27
0.1%
0.5%
0.5%
4.7%
4.3%
2.0%
4.8%
5.5%
3.7%
6.5%
0.5%
0.9%
1.5%
USC
28
62.5%
2.3%
14.3%
7.3%
3.3%
12.1%
4.4%
2.8%
4.3%
0.2%
0.1%
0.1%
0.1%
USC
30
8.9%
52.3%
57.7%
12.3%
17.6%
27.8%
5.4%
5.6%
6.1%
0.6%
0.2%
0.1%
0.3%
USC
33
USC
36
3.3%
19.4%
13.9%
22.8%
28.4%
30.8%
18.1%
16.1%
24.8%
8.9%
5.7%
4.8%
6.8%
For a marketing manager, or a product
manager who has the responsibility of
promoting his/her product, usage-segment
migration analysis such as the one shown in
0.6%
0.7%
1.6%
13.3%
9.8%
7.1%
16.7%
15.3%
18.1%
9.3%
8.2%
6.5%
10.5%
USC
37
0.0%
0.1%
0.2%
9.5%
10.3%
3.0%
14.4%
20.4%
19.9%
47.1%
27.9%
29.9%
25.6%
USC
38
0.0%
0.0%
0.0%
6.4%
0.4%
0.2%
9.2%
0.8%
1.1%
2.4%
28.0
1.2%
4.0%
USC
39
0.0%
0.0%
0.0%
1.9%
1.2%
0.1%
2.8%
4.3%
0.5%
4.1%
2.0%
29.5%
8.0%
USC
40
0.0%
0.0%
0.0%
2.5%
1.1%
0.4%
4.4%
2.0%
3.3%
2.7%
3.3%
0.9%
17.9%
Table 3 provides a clear and complete
picture of how customers migrate over time.
With this information, marketing/product
managers can then develop marketing
strategies to correctly target each individual
migration pattern. Combining USC and
NRE, the migration analysis puts customers’
consumption behaviors in concrete and
quantifiable terms, making it a much easier
task to earn upper management support for
the marketing/product managers’ campaign.
To understand more about why
preferable and less preferable USC
migration patterns occur, more detailed
analyses can be conducted with other
customer data in the customer database. For
example, to analyze customers who migrate
from heavy revolvers (USC 37-40) to mild
revolvers (USC 34-36), we profiled
customer lifestyle and demographic data,
comparing them with the marketing
activities applied to these customers to
understand what we did (or did not do) to
change their NRE from high to low. We
could then adjust our marketing strategies to
customers with similar lifestyle or
demographics. Using the same method, we
would also learn what prompted the
transactors (USC 30) to migrate to revolvers
(USC 31-40), so that we could continue to
focus on those marketing activities which
propelled customers toward this more
desirable migration pattern.
3.2 Other Applications
As
demonstrated
above,
the
collaboration of USC and NRE alone can be
a very powerful weapon for the marketing
function. But the application of USC and
NRE does not stop there. When used with
other data, each tool can solve a number of
marketing or product positioning problems.
In the category of marketing analyses, these
two tools can be classified as the non-risk
factors and thus need to be combined with
other risk-based information, such as the
behavior score or credit bureau’s fico score
for any campaign. We have used USC
and/or NRE with other factors in the
following applications with satisfied results:
1. Determined how to adjust customers’
annual membership fees.
2. Determined how to promote revolvers
by lowering their interest rate to proper
levels.
3. Selected customers for a customer
survey and a focus group.
4. Determined how to target customers
with fee-based insurance marketing
campaign, such as the credit insurance
program
and
some
membership
programs.
5. Profiled different levels of transactors
and revolvers to understand how to
solicit new customers with preferred
profiles.
6. Used in retention group to identify
customer values and to apply
appropriate
strategies
to
retain
customers.
4. Conclusion
USC and NRE were developed and
tested in many projects in a top-ten US
credit-card issuer with satisfied results. They
are simple to understand and to implement,
and as illustrated, they derived rich
customer information which provided vital
input to important marketing strategies.
More importantly, this case proved that
simple yet powerful database marketing
tools can be developed in-house even with
less than perfect data warehouse or marts.
On the road to on-going database marketing
processes, many companies invest millions
of dollars in a customer data warehouse.
Simple ideas such as USC and NRE can
increase the return of this investment.
Acknowledgment
I would like to thank Debbie
Sandgren and Diane McCowin, my
colleagues at the credit card company where
USC and NRE were developed and used, for
their support in making these two database
marketing tools possible. They are both
working for a different US credit-card issuer
now with higher responsibilities.
References
[1] Bennett, P. B., (Ed.) Dictionary of Marketing Terms, American Marketing Association,
Chicago, USA, 1988.
[2] Equifax, Microvision Workshop, USA, 1996.
[3] Metromail, DNA Demographic Super Cell User’s Guide, Illinois, USA, 1996.
[4] Metromail, DNA Demographic The 104 Cells, Illinois, USA, 1995.
[5] Stone, B., Successful Direct Marketing Methods, 4th Ed., NTC Business Books,
Lincolnwood, Illinois, USA, 1988.