2000 Systems Engineering Capstone Conference • University of Virginia
VALUE SCORING OF A CELLULAR PHONE CUSTOMER
Student Team: Tim Campbell, Dean Rafferty, Christi Settlage, and Kim Zalesak
Faculty Advisor: Christina Mastrangelo, Associate Professor
Graduate Student Advisor: Jeff Dietz
Client Advisors: Kevin Bradfield, Suzanne Cardwell, Margaret Ide, Margaret Mayer, Chris McCall, and TJ Shea
America One Communications
1601 Rolling Hills Drive
Richmond, VA 23229
KEYWORDS: cellular phone, Customer Lifetime
Value, Retention Department
ABSTRACT
This project proposes an algorithm for the value
scoring of a cellular phone customer. The Customer
Lifetime Value (CLV) model was chosen as the basis
for the algorithm. Attributes that determine a
customer’s value were incorporated into the Customer
Lifetime Value model using Microsoft Excel. Once the
algorithm was developed, values were developed for a
sample of the customer base, using the appropriate
customer attributes deemed necessary. These values
were then used along with a developed scale to classify
the profitability of the customers into groups; customers
were assigned a value score based on their respective
classification groups. The algorithm has the potential to
aid the America One Retention Department in
identifying valuable customers. Through identification,
these valuable customers can be targeted in retention
strategies. Implementation of this algorithm will help
America One remain competitive in the fast-growing
cellular phone industry.
INTRODUCTION
Within the past 15 years, the cellular industry has
grown very rapidly. Wireless phones are becoming
ubiquitous in today’s society. With 70 million users of
cellular phone service in the U.S., competition among
cellular phone businesses has increased dramatically.
(Gerstenfeld & Oku 1999). Companies must compete
with each other to gain and retain their customers by
offering low rate plans, free phone accessories, and
outstanding customer service. Because of this strong
competition, companies have decreased their prices,
which in turn decreased their profit margins. “Wireless
revenues are increasing by nearly 20% annually. This
figure reflects the increasing public acceptance of
wireless communications but doesn’t take into account
the fact that carrier revenue per subscriber actually has
decreased” (Graham 1998). This competition has made
it hard for companies to increase their profits without
very careful evaluation and optimization of their
processes. “As these new entrants vie for the existing
customer base, they force a lowering in the average
service price within the market. Consequently, the need
to find, acquire, retain, and grow a profitable customer
base becomes more acute” (Boyle 1998).
One such company that is faced with this issue is
America One. America One is a wholly-owned
subsidiary of Capital One, a very profitable credit card
company. America One branched off from Capital One
in 1994 with hopes of using the same techniques in
targeting specific offers to customers that would
maintain the balance between increasing profits and
keeping the customer satisfied with the service.
However, the cellular phone business is much more
complex than the credit card industry. Measuring
profitability becomes a much harder task due to the
many variables associated with a customer and his or
her account, such as the number of free minutes, type of
phone, risk, etc. With competition so great, defining
and measuring customer profitability would positively
impact an organization.
DESCRIPTIVE SCENARIO
The Retention Department of America One is
designed to ensure customer satisfaction as well as to
retain its customers. Customers are routed to this
37
Value Scoring Of A Cellular Phone Customer
department from the Customer Service Department if
they have indicated a desire to close their account or if
they are not happy with the current plan they have
contracted. It is the Retention Representative’s
responsibility to understand the customer’s complaint
or problem and find the most “profitable” way to retain
the customer. The Representative uses a computer
system to pull up the customer’s account information to
look at the rate plan and phone equipment that the
customer owns. The same rate plans are available to all
the customers in a given market, regardless of how
profitable those customers are. The Representative
must then look at various customer attributes located
throughout the computer system as well as other
systems within America One and determine how much
to concede to the customer in order to keep his or her
service. The Representative must use his or her best
judgement in determining how profitable the customer
is to America One (Bradfield 1999). This is a very
difficult task and without an organized method for
determining a customer profitability score, the potential
exists for extreme variability among the Retention
Representatives in what offers they make to the
customers. Due to this variability, each customer’s
profitability is not guaranteed to be maximized, and as a
result neither is the overall profitability of America
One.
consistent choices. They will then be able to
differentiate customers and strive to retain the
profitable ones.
SELECTING A MODEL AND CUSTOMER
ATTRIBUTES
For the purpose of profitability scoring of a
customer, several different models were considered. In
order to determine the optimal model, a set of indicies
of performance (IPs) were developed to evaluate and
analyze the candidate models. These criteria included
robustness, type of output, speed of computation,
amount of training necessary for understandability,
amount of data transformation needed for input into the
model, the ability to handle incomplete or dirty data,
and the amount of historical data required. The models
under consideration included classification trees
(recursive partitioning algorithms), clustering,
discriminant analysis, Markov Chains, survival
analysis, Multivariate Adaptive Regression Splines
(MARS), neural networks, and Customer Lifetime
Value (CLV). Each of these models was evaluated
against the above criteria. The Customer Lifetime
Value model ranked highest and was chosen as the
optimal model for profitability scoring.
Customer Lifetime Value Model
NORMATIVE SCENARIO
In order to diminish the variability among the
Retention Representative offers, a customer
profitability algorithm was created. The profitability
score yielded by the algorithm is based on behavioral
attributes of the customer. As a customer calls into the
Retention Department, the Representative will see the
profitability score of that customer. These scores will
serve to make the profitability measure more valuable
and easy to interpret for the Retention Representatives.
The score can be used by the representative in order to
categorize the customer’s profitability and then used to
decide how much to offer them in order to retain them
as an America One customer. The highest incentive
points will be linked to the plans which make a specific
customer most profitable, not simply the most
expensive plans. The points will be used to encourage
the representatives to offer the most profitable plans to
the customer. With the incentive points linkage system
in place, the plans will be filtered so that they are best
fit to the customer. Thus, the Retention
Representatives will not have to rely on their own
interpretations as to which plan to offer the customer,
and they will always be ensured that they are making
38
The Customer Lifetime Value (CLV) model is a net
present value (NPV) calculation that illustrates the
relationship between a customer’s revenues, expenses,
and expected life. CLV focuses on consumer behavior
and often incorporates the following factors: initial
sales to a customer, future sales, customer service costs,
relationship marketing expenses, cross-sell revenues,
probability of future purchases and customer retention,
and credits, discounts, or other incentives used to keep
an account (Berger and Nasr 1998). The CLV value is
calculated as the difference between revenues and
expenses minus the cost of promotional marketing used
to retain an account; all values are discounted back to
the present.
The CLV model in its most basic form is as follows:
n

ri  
r i-1 
CLV  GC * 

M
*

 (1  d)i-.5 
i
i  0 (1  d) 



Where:
CLV = Customer Lifetime Value ($)
2000 Systems Engineering Capstone Conference • University of Virginia
GC = Yearly Gross Contribution margin, revenues cost of sales ($)
M = Relevant promotional costs of the customer ($)
n = Expected customer lifetime (years, or other time
unit)
r = Yearly retention rate (%)
d = Discount rate (%, firm specific)
Customer Attributes
After determining the appropriate model, a list of
attributes considered important for the profitability
scoring of a cellular customer was compiled. An
investigation ensued to determine what type of data was
tracked in America One’s databases for each of the
customers. Once this had been determined, each
attribute was analyzed and evaluated as to its relevance
in the model. The attributes included in the model are
revenues and expenses for the telecommunication
industry. The most obvious attributes include:




Monthly Access Fee – The amount that a customer
pays a cellular provider for the use of the phone
and a set number of “free” minutes each month.
“Free” Minutes – These are the number of minutes
that come with the minute plan a customer signs up
for.
Peak/Off-Peak Minutes Used – Peak and off-peak
minutes refer to the different prices that a customer
pays per minute depending on the time of day
(peak and off-peak hours) the customer is using the
phone.
Further analysis revealed additional attributes that
are not being disclosed due to confidentiality
issues.
to close the customer’s account per his/her original
request. Thus, the profitability calculation is only
performed on customers who pass through the two
filters. These filters eliminate wasted time and
resources on analyzing customers who are not likely to
be profitable over their expected lifetime. The process
can be seen schematically in Figure 1 below:
Customer call forwarded
to Retention
Does customer have good
payment history?
Yes
No
Has customer called into
Retention often?
Yes
No
Find available rate
plans
Calculate CLV for
each plan
Categorize CLV into
scoring buckets
RECOMMENDED STRATEGY
End
The recommended profitability-scoring algorithm is
a combination of a classification tree and a CLV model.
This algorithm begins by running each customer
through a series of two filters. The first filter checks a
customer’s payment history. Depending on the history,
a customer may be too risky to retain and should
therefore not be offered incentives to stay. The second
filter checks to see the number of times a customer has
called into the Retention Department. As noted above,
calls forwarded to the Retention Department are very
expensive and a customer who calls several times are
either a) bluffing to try to get a better deal or b) can not
be satisfied by what America One has to offer and
should be let go. If a customer does not pass through
both these filters, the Representative will be instructed
Figure 1
The profitability of the remaining customers who
pass through the filters is calculated using the CLV
model. The customer attributes that have been selected
to be included are incorporated as variables in the
model. Their attributes would be used to calculate their
individual CLV on each possible rate plan. After the
algorithm calculates a customer’s CLV, the Retention
Representatives see the customer’s profitability on each
of the potential plans, thereby making the solution more
robust and accountable for future changes since it is not
limited to the analysis of the customer on only their
current plan.
39
Value Scoring Of A Cellular Phone Customer
CLV As Applied To America One
Once the general profitability algorithm had been
chosen, the generic CLV model needed to be adjusted
to fit America One’s customers and the available
attributes. This CLV equation in its most basic form
was expanded for additional complexity and to account
for attributes specific to the cellular industry. The
following equation is a CLV model that accounts for
the above changes:
n
n
n

 

ri  
ri
ri
CLV  GC * 
 E * 
 M * 
i
i -.5 
i -.5 
(1

d)
(1

d)
(1

d)
i 0
i 1
i 1

 
 

displayed more clearly to show the Representative
specifically what rate plans to offer or whether or not to
let the customer close the account.
To rectify this problem, a set scale was developed to
classify the CLV’s of each of the customers into
profitability scores. Each CLV was assigned a value
between 0 and 10. This will enable the Representatives
to more accurately see which customers are the most
profitable. In addition, it was determined that this
profitability score could be linked to the incentive
system so as to encourage the Representative to offer a
plan based on the customer’s intrinsic value.
RESULTS/CONCLUSIONS
Where
CLV = Customer Lifetime Value ($)
GC = Monthly Gross Contribution margin, revenues
minus cost of sales ($)
E = Equipment Contribution, retail – wholesale price
($)
M = Relevant promotional costs of the customer ($)
n = Expected customer lifetime (years)
i = Time counter, counting in 0.0833 years (1 month)
r = Retention rate (%)
d = Discount rate (%, firm specific)
Some of the data attributes necessary for the above
equation were not available from America One and
thus, the model was modified. While, the previous CLV
equation is ideal, the following equation is the one
implemented by the Capstone team:
i


r

i


n
j 1


CLV  GC * 
i


(1

d)
i 1




The attributes deemed important were included as
variables in this equation.
These CLV calculations are only useful if they can
be interpreted correctly. A value showing the
profitability of a customer will not be very useful to a
Representative who is trying to use it to determine how
to handle the customer’s call. A Representative does
not have the time to interpret the profitability score. He
or she needs to know whether or not to offer the
customer a better plan or to close the account within the
first minute of the call. This information must be
40
The profitability score developed provides a clear
method by which the Representatives can determine the
customers who are potentially profitable. Tying the
highest score (the most profitable plan for that
customer) to the highest incentive points will
monetarily reward the representatives for, and thus
encourage them to, make the most profitable
recommendations to customers.
This scoring system is also a valuable tool that can
be used and modified in the future. It provides a very
good framework for categorizing customers and
looking at their worth to the company. It provides for
quicker decisions on how to handle the customer as
well as what to offer them. The accuracy of this score
can be improved with the addition of some of the
attributes that were excluded from the model due to
tracking difficulties within the America One database.
A recommendation would be to start tracking some of
those attributes on a customer-by-customer basis. In
addition, this scoring system could possibly be
modified in the future to identify the key attributes of a
profitable customer.
The CLV model presents a strong approach to
calculating the profitability of a customer and
rewarding Representatives for offering rate plans
specifically designed for that customer. It enables
America One to look at a customer’s profitability
individually and not just as a whole. America One will
have the advantage of being able to tailor the rate plans
to each individual customer. As individual customer
attention becomes more important in today’s economic
environment, America One might find this to be a
profitable investment.
2000 Systems Engineering Capstone Conference • University of Virginia
REFERENCES
Berger, Paul D. and Nada I. Nasr, “Customer Lifetime
Value: Marketing Models and Applications”,
Journal of Interactive Marketing, Winter 1998.
Boyle, P. “Data warehousing: Managing knowledge.”
Telecommunications
32:1. January 1998: 61-68.
Bradfield, K. Personal Interview. 30 August, 1999.
Gerstenfeld, D. “The Vibrancy of Telecoms.” The
Jerusalem Post. 20 May 1999: 11A.
Graham, D.“Shopping for Revenue Management.”
Wireless Review
15.4. 15 February 1998: 122-132.
Oku, K. “VTG Worldwide.” Business Wire. 18 August
1999.
Capital One’s Information Technology department in
Richmond where she will join the rest of the Capstone
team after graduation.
BIOGRAPHIES
Tim Campbell is a fourth-year Systems Engineering
major with a concentration in management systems
from Roanoke, Virginia. His main contributions to the
project were analyzing the potential models and
creating a Visual Basic user interface. Mr. Campbell
has accepted a job with Capital One’s Information
Technology department in Richmond.
Dean Rafferty is a fourth-year Systems Engineering
major concentrating in management from Virginia
Beach, Virginia. His main contribution to the project
was working with the customer data and the CLV
model to build the profitability algorithm in Microsoft
Excel. Mr. Rafferty has accepted a job with Capital
One’s Information Technology department, where he
will begin work after graduation.
Christi Settlage is a fourth-year Systems Engineering
major from Richmond, Virginia, concentrating in
management systems. Her main contributions to the
project were the descriptive and normative scenarios
and the development of the bucketing alternatives. Ms.
Settlage has accepted a job with Capital One’s
Information Technology department where she will
begin work after graduation.
Kim Zalesak is a fourth-year Systems Engineering
major with a concentration in management systems
from Springfield, VA. Her main contributions to the
project were evaluating and selecting the customer
attributes, developing the bucketing alternatives, and
linking the profitability score to the incentive point
system. Ms. Zalesak has also accepted a job with
41
Value Scoring Of A Cellular Phone Customer
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