Journal of Service Science and Management, 2011, 4, 284-290
doi:10.4236/jssm.2011.43034 Published Online September 2011 (
Copyright © 2011 SciRes. JSSM
Customer Segmentation Using CLV Elements
Mitra Bokaei Hosseni, Mohammad Jafar Tarokh
Information Technology Group, Industrial Engineering Department, K. N. Toosi University of Technology, Tehran, Iran.
Received June 22nd, 2011; revised July 28th, 2011; accepted August 4th, 2011.
To have an effective customer relationship management, it is essential to have information about the different segments
of the customers and predict the future profit of them. For this reason companies can use customer lifetime value that
consists of three facto rs-current value of customers, potential value, and customer churn. Potential value of customers
focuses on the cross-selling opportunities for current customers. Therefore, cross selling models are built on the total
customers of the database that is not interesting. To overcome this, we presented a framework that estimates the current
value and churn probability for the cu stomers and then segm ented them base on these two elements and select the most
profitable segment for th e cross-selling models. In this study we predict the customer churn ba se on logistic regression
as a case study on the insurance database.
Keywords: CRM, Customer Lifetime Value, Churn Predict i on, Cross-selling, Logistic Regression
1. Introduction
Life insurance is become one of the popular insurances in
recent years. It is divided into many categories which
each of them delivers different services to the customer.
Customers agree to pay for the insurance in different
ways. General way to pay for the agreed amount is
monthly payment. Therefore, it is important for the
insurance company to know about its customers and their
payment styles to manage its relationships with them.
The insurance company must have the information about
which customers are likely to leave the company or are
likely to pay their loans not in the defined time. It also
must understand its loyal customers and use different
marketing strategies to retain them.
Some customers repeatedly switch providers, or
“churn”, it is also obvious in insurance industry. Different
methods for churn rate measurement were mentioned in
the literature. Companies can use data mining techniques
to identify the characteristics of the customers who will
remain loyal or the churners. In insurance industry rapid
customer churn is a significant problem due to the
competitive environment of this industry. Wu et al. used
decision rules and data mining to investigate the potential
customers for an existing or new insurance product [1].
These methods enable companies to invest in customers
who will produce the most profit for the company.
Many life time value models were presented in order
to evaluate the customer value in its lifecycle. Each of
them had specific characteristics that were suitable for
special industry. The usage of these models also depends
on the available data about that industry. Customer value
can be identified by three factors, current value, potential
value and churn rate and also by socio-demographic data
from the customers. Up-selling, cross-selling, and custo-
mer retention is defined as the three core activities for
increasing the customer value [2].
Current cross-selling models were being built on the
total customer database of the organization. This leads to a
high overhead for building cross-selling models because
the whole database contains also the data from the
unprofitable customers and churners. Also the current
LTV models needed data from different products that a
customer had for a period of time to calculate the potential
value of the customers. Some organizations lack from
these kinds of data. To overcome these problems men-
tioned, it could be interesting to build a cross-selling mo-
del on the loyal customers with appropriate current value.
LTV is based on the understanding of the behavior of
the profitable customers of the organization. For this
reason, this study proposed a model to understand the
profitable segments of the customers based on the LTV
components and socio-demographic data of customers.
First we evaluated the current value of each customer
based on the transactional data. Then the churn rates were
evaluated and loyal customers were predicted with the use
of logistic regression. The customers were segmented
Customer Segmentation Using CLV Elements285
based on the current value and customer loyalty that were
calculated. After analyzing and selecting the profitable
segments organizations can use the cross-selling strategies
to develop the relationship between customers and the
organization. For this reason, customers’ data should be
collected for evaluating the life time value for each cus-
In this paper we presented a new model for understan-
ding the customer behavior based on the customer
lifetime value. We built a cross-selling model on the
loyal customers with appropriate current value. We also
examined this model to data that was collected from an
insurance company. Logistic regression was selected to
predict the customer behavior based on these data. This
paper is organized as follows. Section 2 presents an
overview about life time value literature and related
works and also review of mathematical model we used
for prediction. Section 3 specifies the model and section
4 evaluates the model based on the real data collected
from the insurance company. Section 5 includes the
conclusion of the research.
2. Literature Review
This section is divided into three parts, first we review
the CRM dimensions and mention which dimension we
are going to focus. Second, we review the LTV defini-
tions and models. Third, we describe the mathematical
models for prediction we use for this paper.
2.1. CRM Dimensions for Customer Behavior
To manage the different segments of customers managers
can use customer relationship management (CRM) as a
leading business strategy in a competitive environment,
while retention of the current customers in a competitive
environment is vital for survival of the companies. CRM
pursues long term relationship with profitable customers.
There are different definitions in the literature for the
customer relationship management, that we mention
some of them here. Ling & Yen believe that CRM
comprises a set of processes and enabling systems sup-
porting a business strategy to build long term, profitable
relationships with specific customers [3]. Swift defined
CRM as an enterprise approach to understand and
influence customer behavior through meaningful com-
munications in order to improve customer acquisition,
customer retention, customer loyalty, and customer
profitability [3]. Parvatiyar and Sheth defined CRM as a
comprehensive strategy and process of acquiring,
retaining, and partnering with selective customers to
create superior value for the company and the customer.
It involves the integration of marketing, sales, customer
service, and the supply chain functions of the organiza-
tion to achieve greater efficiencies and effectiveness in
delivering customer value [4]. Kincaid viewed CRM as
the strategic use of information, processes, technology,
and people to manage the customer's relationship with
your company (Marketing, Sales, Services, and Support)
across the whole customer life cycle [5]. Lawson-Body
& Limayen believe that CRM refers to all business
activities directed toward initiating, establishing, maintai-
ning, and developing successful long-term relational
exchanges and it is the set of methodologies and tools
that help an enterprise manage customer relationships in
an organized way [6]. All these definitions emphasized
on the importance of customer acquisition and retention
through business intelligence to provide value to the
organization and customers. It is implied in the literature
that customer acquisition is more expensive than
customer retention because the lack of information on
new customers makes it difficult to target the appropriate
customers. Therefore, precise evaluations of customer
value and customer segmentation are critical for success-
ful CRM. Customer relationship management also identi-
fies the suitable products for a special segment.
Increased customer retention and loyalty, higher custo-
mer profitability, creation value for the customer, custo-
mization of the products and services, and lower process,
higher quality products and services are mentioned as the
potential benefits of CRM [7,8]. Marketers believe that
80% of the profits are produced by to 20% of profitable
customers and 80% of the costs are produced by top 20%
of unprofitable customers. This rule is called the 80/20
rule that the marketers use it for customer profitability
evaluation [9,10].
By these definitions it may seem that CRM is only
useful for managing the relationships between businesses
and customers. A closer examination revealed that CRM
is also applicable to business-to-business environments.
CRM helps smooth the process when various representa-
tives of seller and buyer companies communicate and
collaborate [11].
CRM is used to identify the most profitable customers
and allocate the resources to this segment of customers to
achieve more profit. The four dimensions of the CRM
are essential efforts to gain customer insight [12]. CRM
dimensions are listed below:
Customer identification: CRM begins its work with
this cycle. It is also known as customer acquisition.
In this cycle company seeks its target customers
based on the organization and marketing strategy.
This target is assumed profitable for the organization.
In this cycle organization analyses the target
customer and segments the target customer. Analy-
zing the target customer involves seeking the pro-
fitable segments of customers through analysis of
Copyright © 2011 SciRes. JSSM
Customer Segmentation Using CLV Elements
customers’ underlying characteristics, whereas cus-
tomer segmentation involves the subdivision of an
entire customer base into smaller customer groups or
segments, consisting of customers who are relatively
similar within each specific segment [13].
Customer attraction: after identifying the appropriate
customer segment, organization must attract this
segment by offering services and products and
allocating resources to this segment. Direct marke-
ting is one of the techniques for customer attraction.
Customer retention: this part of CRM refers to
customer satisfaction that is the significant factor
for customer loyalty. In this cycle companies can
use one-to-one marketing strategies that refer to
personalization of services or products for each
customer which need understanding the customer
behavior. Loyalty programs and churn analysis are
the other elements of customer retention strategies.
Customer retention is a vital part of CRM because
of the high costs for identifying and attracting the
new customers.
Customer development: this cycle consist of custo-
mer lifetime value, up selling and cross selling and
market basket analysis. Customer lifetime value is
discussed in the next part of this section. Cross
selling analysis refers to finding the optimal product
to offer to a given customer [14] and up selling
analysis is focused o n selling moreor a more
expensive versionof the products that are current-
ly purchased by the customer [15].
This paper focuses on the life time value models to
develop the customer relationship by calculating the
customer current value and predict the characteristics of
the churners by prediction models such as logistic re-
gression. This paper also suggests the profitable segment
based on the churn probability and current value for the
cross selling strategies. The current value of the customers
and prediction of churners were based on the data collected
from the existing customers of the insurance company.
2.2. Customer Life Time Value (CLTV)
Customer life time value is known as customer value,
customer equity, and customer profitability. Berger and
Nasr defined LTV as the net profit or loss to the firm
from a customer over the entire life of transactions of
that customer with the firm [16]. Gupta and Lehmann
defined LTV as the present value of all future profits
generated from a customer [17]. Hwang et al. defined the
LTV as the sum of the revenues gained from the
company’s customers over the lifetime of transactions
after the deduction of the total cost of attracting, selling,
and servicing customers, taking into account the time
value of money [18]. Glady et al. defined the CLV as the
present value of future cash flows yielded by the cus-
tomer’s product usage, without taking into account pre-
viously spent costs [19]. Basic model for LTV is based on
the definition of Hwang et al. and is represented in
Equation (1).
where i is the period of cash flow from customer
transaction, i the revenue from the customer in period
i, i the total cost of generating the revenue i in
period i, and n is total number of periods of projected life
of the customer under consideration. The calculation
above is the most basic model that ignores the fluctua-
tions of sales and costs. Berger and Nasr have proposed
LTV calculation model that reflects the fluctuations of
sales and costs that is represented in Equation (2) [17].
 
πt is the function of customer profits according
to time t.
Hawng et al. suggested a new LTV model of individual
customer considering churn rate of a customer. This
model is represented in Equation (3).
LTV π1
pi tN
td d
where i is service period index of customer i, i is
the total service period of customer i, d is the interest rate,
t N
Ei is expected service period of customer i,
is the past profit contribution of customer i at period i,
t is the future profit contribution of customer i at
period i, and t
tis the potential benefit from
customer i at period .
Customer segmentation methods using LTV can be
classified into three categories: 1) segmentation by using
only LTV values, 2) segmentation by using LTV compo-
nents and 3) segmentation by considering both LTV
values and other information [20]. The first category uses
the equations above and the data collected from the
organization to calculate the customer lifetime value. The
second category uses LTV components—current value of
customers, potential value and customer loyalty. And the
third one uses both three LTV components and also the
socio-demographic data from customers and product or
transaction information. In this paper we use the third
category to understand the most profitable segment of
our customers.
2.3. Logistic Regression
In our suggested model we used binary logistic regression
Copyright © 2011 SciRes. JSSM
Customer Segmentation Using CLV Elements287
model for predicting customer churn. Binary logistic
regression is most useful when you want to model the
event probability for a categorical response variable with
two outcomes. The model is represented in Equation (4).
The variable z represents the exposure to some set of
independent variables, while f(z) represents the probabili-
ty of a particular outcome, given that set of explanatory
variables. The variable z is a measure of the total
contribution of all the independent variables used in the
model and is known as the logit. The variable z is usually
defined as the Equation (5).
 
  (5)
where x(i) is the socio-demographic data about the
customer i. Our goal is to achieve the intercept and other
coefficients to predict whether a new customer with spe-
cific socio-demographic information will churn or not.
3. Research Model
Currently cross-selling models are being built on the total
customer database that is not profitable for the company
and doesn’t lead to the appropriate result. To overcome
this, it could be interesting to build a cross selling model
on the loyal customers with appropriate current value.
LTV is based on the understanding of the behavior of the
profitable customers of the organization. For this reason
we propose a model to understand the profitable segment
of the customers based on the LTV components that is
mentioned in the previous section. First phase of this
model evaluates the current value of each customer based
on the transactional data of each customer. The second
phase evaluates the churn rate and predicts loyal custo-
mers using logistic regression model. The third phase
segments the customers based on the current value and
customer loyalty that calculated in previous phases. After
analyzing and selecting the profitable segment company
can use the cross selling strategies to develop the
relationship between customers and the organization. The
research model is illustrated in the Figure 1.
4. Model Evaluation
4.1. Data Collection and Description
The raw data of this study consists of life insurance data
of a private insurance company in Iran and collected in
2009. The dataset is composed of 11695 records and 12
data fields. This data set consists of 8 types of life insur-
ance and the customers have different payment styles.
Data fields and their value variances are described in the
Table 1. We used data from one insurance type to evalu-
ate the proposed model. The data from this insurance
Figure 1. Research model.
type consists of 1651 records. In this dataset some cus-
tomers refuse to pay the specified amount in the contract
in the specified time periods. Therefore it is essential for
the company to know about its loyal customers and al-
locate its resources to these customers.
4.2. Phase 1: Calculating the Current Value
Current value is a profit gained from a customer during a
period of time. In this paper we assume the time period
from the contract date of each customer to 2009. There-
fore we calculate the cumulative value of the customer
from the past to present. The current value is calculated
from a simple calculation as follows [19]:
Customer Value = (average amount asked to pay –
Cumulative amount in arrears)/total service period.
Table 2 presents the minimum, maximum and mean of
the current values for 1651 customers.
4.2. Phase 2: Calculating Customer Loyalty and
Churn Rate
Customer loyalty is derived from the customer satisfac-
Table 1. Sample of data fields and their values in the data-
Field Minimum value Maximum va l u e
Life asset 0 600,000,000
Die asset 0 1,000,000,000
Asset incremental percentage 0 0.1
Insurance incremental percentage0 0.1
Gender 0 1
Age 14 88
Insurance time 1 20
Payment type 1 5
Number of delays in payment 0 24
Amount of money that is not paid0 200,052,000
Copyright © 2011 SciRes. JSSM
Customer Segmentation Using CLV Elements
Table 2. Calculated current value for one insurance type.
Current value
Minimum Mean Maximum
0 2,227,541 53,751,000
tion. Loyal customers are defined as the customers that
are more likely to continue their relationship with the
organization. Customer loyalty can be achieved from the
following equation:
Customer Loyalty = 1 – Churn rate
Churners are customers that have a relationship with a
company but will go to the competitor in the near future.
In the insurance company, experts defined the churners
as customers who have delays in payments. Also it is
very popular that most people have two or more different
insurances from one insurance company. Therefore, it is
important for an organization in a competitive environ-
ment to understand the characteristics of its loyal cus-
tomers due to the high costs of identifying and acquiring
new customers and introduce its new services to the loyal
customers. With the use of prediction models and the
current customers’ databoth socio demographic and
transactional datacompanies are able to predict the be-
havior of the new customers and target new customer
segments. In this paper we evaluate logistic regression as
a prediction model. The interest is to segment the cus-
tomers into churners and non churners. Therefore the
result is a binary probability of churn.
Logistic regression can be used to predict a dependent
variable on the basis of continuous or categorical inde-
pendents and to determine the effect size of the inde-
pendent variables on the dependent; to rank the relative
importance of independents; to assess interaction effects;
and to understand the impact of covariate control vari-
ables. Logistic regression applies maximum likelihood
estimation after transforming the dependent into a logit
variable (the natural log of the odds of the dependent
occurring or not). Goodness of fit tests such as the like-
lihood ratio test is available as indicators of model ap-
propriateness, as is the Wald statistic to test the signifi-
cance of individual independent variables. The insurance
data set has some variables that can be considered as
categorical variables, such as payment type or insurance
time. These variables are represented in Table 3.
The independent variables are the dataset fields and
the calculated regression coefficients and the intercept is
represented in Figure 2. Figure 2 shows the coefficients
(B), the standard errors associated with the coefficients,
the Wald chi-square statistics, associated p-values (Sig),
and odds ratio (Exp(B)).Variables with Sig > 0.05 are not
statistically significant. The probability of churn can be
Table 3. categorical variables coding.
Variable Frequency
1 2 3
11498 1 0 0
Customer must
pay every month
2124 0 1 0
Customer must
pay every 3 month
325 0 0 1
Customer must
pay every 6 month
43 0 0 0
Customer must
pay every year
560 1 0 0
Customer must
pay the fee during
5 years
10173 0 1 0
Customer must
pay the fee during
10 years
151408 0 0 1
Customer must
pay the fee during
15 years
209 0 0 0
Customer must
pay the fee during
20 years
0575 1 Female
Gender 11075 0 Male
Figure 2. Variable in equation of enter logistic regression.
calculated by substituting the coefficients for each vari-
able in the Equation (6).
log 1kk
 
 
where p is the probability of churn.
Figure 3 compares the odd ratio for all the independ-
ent variables.
Table 4 represents four segments of customers based
on their current value and the churn probability. We
briefly discuss the strategies for each segment.
Segment I: this segment consists of loyal customers,
but company has not succeeded in gaining profit from
Copyright © 2011 SciRes. JSSM
Customer Segmentation Using CLV Elements289
Figure 3. Exp(B) for the regres sion variables.
Table 4. Segmentation with current value and loyalty.
Current value
Churn probability Low High
High II IV
them. Customers in this segment may have larger oppor-
tunities for the up-selling strategies.
Segment II: this segment can be regarded as unattrac-
tive. These customers have high churn probability, and
their current value is low.
Segment III: this segment consists of loyal customers
with high current value. These customers are the best
targets for cross-selling strategies.
Segment IV: this segment is the least important one for
the up/cross selling activities. Customers have high cur-
rent value and the churn probability is high. They are
churners that have low possibility for cross-selling ac-
Data collected from the insurance company is the wit-
ness for this claim. Table 5 shows the result of the col-
lected data for each segment. In segment four, only one
customer is reference for cross-selling example. Al-
though this segment has high current value but it seems
that it’s better for companies not to investigate on these
customers and allocate the resources to other segments
that has higher potential for up/cross-selling activities.
In the two first phases we calculated and discussed the
Table 5. Customer dispersal through four segments.
Current value
Churn probability Low High
Segment II: Segment IV:
926 customers 299 customers
Up-selling: 5 Up-selling: 5
Cross-selling: 3 Cross-selling: 1
Segment I: Segment III:
311 customers 115 customers
Up-selling: 5 Up-selling: 5
Cross-selling: 9 Cross-selling: 3
current value and customer churn probability for a type
of life insurance. In this phase we present a segmentation
model based on the current value and the customer churn
and we evaluated this model based on the real data col-
lected from the insurance company. The dispersal of the
customer data in the four segments are represented in the
Table 5. As illustrated in Table 5, most of customers are
in the second segment that is not interesting at all for the
company. Although this segment includes most of the
customers but it has the least number of cross-selling and
up-selling activities. This shows benefits of segmenting
the customers before offering cross-selling and up-selling
to all customers of the company.
5. Conclusions
The main objective of CRM is to have an effective
relationship with different segments of the customers.
For this reason it is essential to have enough information
about the current customers and their behavior. Based on
the information about these current customers, companies
can identify the most profitable segment of these
customers. Identifying the most profitable segments can
help the company to manage a different relationship with
this segment, and also can help the company to use the
socio-demographic features of the profitable segment as
a selective condition for the new customer targeting.
In this paper we focused on the relationship manage-
ment of the current customers and suggested a segmenta-
tion model to optimize the selection of the most desirable
segment by the company and implement the cross-selling
models based on this desirable segment. This approach is
more effective than implementing the cross-selling
models on all the current customers of the company. For
this reason we segmented the customers based on the
current value of the customers and the churn rate of the
customers. We used data from the insurance company.
The current value for each customer was calculated
based on the transaction data. To predict the customer
loyalty, we used prediction models such as logistic
regression. This model used both customer’s socio-
demographic data and transaction data about the number
of delays for paying the life insurance fees. The results of
these models showed coefficients for the prediction
variables. With the use of these models and current
values of customers, companies can segment their custo-
mers and select the desirable segment to announce them
about the other services of the company.
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Customer Segmentation Using CLV Elements
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