Customer Segmentation Using CLV Elements

doi:10.4236/jssm.2011.43034

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Journal of Service Science and Management, 2011, 4, 284-290

doi:10.4236/jssm.2011.43034 Published Online September 2011 (http://www.SciRP.org/journal/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.

Email: [email protected], [email protected]

Received June 22nd, 2011; revised July 28th, 2011; accepted August 4th, 2011.

ABSTRACT

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-

tomer.

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

Classification

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

Customer Segmentation Using CLV Elements

286

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 more―or a more

expensive version―of 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).



0.5

LT V

nii

d







 (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].

C R

 

LTV π







 (2)

where





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

iii

NEi

NNt

pi tN

ttN

tBt

td d















(3)

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

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).



e11e

Fz 





(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).

0112233kk

zxxxx



 

  (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-

set.

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

Customer Segmentation Using CLV Elements

288

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’ data―both socio demographic and

transactional data―companies 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.

Parameter

coding

Variable Frequency

1 2 3

Description

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

Payment_type

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

Insurance_time

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).

0112233

log 1kk

 



 







 (6)

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

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

Low I III

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-

tivities.

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

High

Cross-selling: 3 Cross-selling: 1

Segment I: Segment III:

311 customers 115 customers

Up-selling: 5 Up-selling: 5

Low

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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