A CRM-BASED PRICING MODEL

FOR M-COMMERCE SERVICES

Gülfem Işıklar, Mehmet Üner, Ayşe Başar Bener

Department of Computer Engineering, Boğaziçi University, Bebek, Istanbul, Turkey

Keywords: Mobile commerce applications, pricing, CRM, utility function.

Abstract: Mobile network operators (MNOs) and service providers need flexible pricing mechanisms in order to

fulfill next generation business models. Heavy competition in the wireless market place increases the

importance of designing the most effective business models and developing long-term relationships with

customers. Customer Relationship Management (CRM) is an effective tool in order to pursue long-term

relationship with profitable customers. In this article, we focus on proposing a simple and robust pricing

model for mobile commerce services based on CRM approach. This mechanism enables operators to satisfy

the needs of their existing customers and to achieve the maximum revenue and utilization rate on their

current customer portfolio as well. Our aim in this research is to come up with a unit price for each mobile

service/product class by taking the issues of social welfare maximization into consideration. The CRM

implementation in the proposed model further allows MNO to better understand its customers and hence to

direct its marketing plans to a specific target market. Analytical and simulation results demonstrate the

implementation and the effectiveness of the suggested approach.

1 INTRODUCTION

The rapid growth of wireless networks has led to

vast changes in mobile devices, middleware

development, standards, network implementation,

and user acceptance (Deitel, 2001). Today’s most

popular wireless devices include cell phones,

personal digital assistansts (PDAs), and laptops.

Such devices allow individuals and organizations to

connect to the Internet and World Wide Web at any

time, from almost any place. Electronic commerce

(e-commerce) was just one of the possible services

performed via Internet. E-commerce still continues

to grow, but so far most e-commerce applications

require wired infrastructures. Mobile commerce (m-

commerce) is one of the opportunities created by

next generation wireless networks.

In the earlier years, e-commerce meant the

facilitation of commercial transactions electronically

such as sending purchase orders or invoices

electronically. Later, it is defined as the purchase of

goods and services over the World Wide Web via

secure servers using electronic shopping carts and

electronic payment services (Deitel, 2001).

Although m-commerce has a lot of common

grounds with e-commerce, it is a larger set in terms

of the functionality provided to the users. For

example, m-commerce provides location-based

services that are context-sensitive with respect to the

geographic location of the user. Another

functionality of m-commerce is the ability of bill

integration, which refers to the several purchases

combined into one bill. Customers only need to pay

one bill for all of their applications (Kuo, 2006). In

such an environment, the mobile network operator

(MNO) plays the role of intermediator between

customers and other services providers. Hence, an

MNO has to apply a robust pricing mechanism so as

to satisfy its customers and maximize its revenue.

In this research, we first differentiated customers

of an MNO using their Customer Relationship

Management (CRM) values. The purpose of this

segmentation is to be able to offer different levels of

quality of service (QoS) to different groups of

customers. CRM is a tool which enables

organizations to better understand the needs of their

customers, and as a result to assure maximum

utilization and revenue from their services by

customer-specific strategies. In the second step, we

differentiated mobile services/products according to

their bandwidth requirements and their tolerances to

delays. We aim to come up with a unit price for each

mobile service/product class.

118

I¸sıklar G., Üner M. and Ba¸sar Bener A. (2006).

A CRM-BASED PRICING MODEL FOR M-COMMERCE SERVICES.

In Proceedings of the International Conference on e-Business, pages 118-125

DOI: 10.5220/0001428101180125

 SciTePress

The rest of this paper is organized as follows:

The definition of mobile commerce and the mobile

value-chain are given in Section 2. Section 3

includes the basis of a CRM tool, as well as its

implementation in our approach. The unit prices of

mobile services/products and a numeric application

are represented in Section 4, following the proposed

framework. Finally, in Section 5 we provide

conclusions and we discuss the future work.

2 MOBILE COMMERCE

E-commerce continues its growth in wireless area by

m-commerce (Ngai, 2006). In (Clark, 2001), it is

defined as any transaction with monetary value that

is conducted via a mobile network. In another study,

any e-commerce transaction, processed by anyone,

anywhere, through wireless devices, is considered

mobile commerce (Keen, 2001).

One of the advantages of m-commerce is that it

gives companies the opportunity for reaching a

broad range of consumers. M-commerce has some

specific dimensions which are not available for

traditional e-commerce. For example it gives

nomadic access to its users and it is location-centric.

The services in m-commerce can be personalized

and m-commerce allows service providers to treat

each customer separately (Mahatanankoon, 2005).

We utilized a well-known customer management

tool to make MNOs recognize their groups of

customers.

In the related literature, m-commerce is analyzed

under five main topics: M-commerce theory and

research, wireless network infrastructure, mobile

middleware, wireless user infrastructure and mobile

commerce applications (Ngai, 2006). The lowest

level of m-commerce is the theory and research. It

consists of consumer behavior, the acceptance of

technology, the diffusion of m-commerce

applications, m-commerce business models and

strategies. Pricing models for m-commerce services,

which is the main topic of our research, is a part of

this level.

The new technologies, that solved the bandwidth

limitation problem, allow higher transmission rates

and more sophisticated mobile services. The m-

commerce applications are classified into six main

categories: Mobile financial applications, mobile

advertising, mobile inventory management, mobile

auctions, mobile entertainment services and

proactive service management (Varshney, 2000).

However, the number of such applications

constantly increases, since existing e-commerce

applications can be offered on mobile networks.

Such a categorization may direct the MNO to define

its product/service classes which constitutes one of

the phases of our suggested mechanism. It is not

always possible to use flat pricing schemes in terms

of kilobytes or megabytes, as the MNOs have to

manage their network traffic by analyzing the

priority and the resource requirements of given

services. For instance, a mobile financial application

should be more privileged than a mobile advertising.

The business models and value chains

established for the Internet do not exactly match to

the ones for mobile networks. Therefore an m-

commerce value chain needs to be formed. A value

chain could be defined as the linkage and integration

of a series of activities in which enterprises deliver

the created and valued products or services to

customers (Porter, 1985). Like any other

service/product, m-commerce involves a large

number of value providers in a chain that terminates

with the end-user. Among the value chain actors,

despite that each has its significance; mobile

network operator plays the most critical role on the

entire chain (Kuo, 2006; Rülke, 2003). In this

article, we investigate the role of mobile network

operator in the value chain, especially when

charging various mobile services that are offered to

different types of customers.

In Figure 1, a basic mobile value chain is shown. It

mainly consists of three categories: Hardware,

services and infrastructure. The hardware part of the

mobile value chain includes the mobile equipment

supplier, wholesaler retailer. They are responsible

for producing and selling the mobile terminal to the

end-user. The other part is the services part, which

contains the content provider and the mobile value-

added service provider. They are responsible for

offering the services/products to the customers.

Finally, the infrastructure part consists of software

and infrastructure supplier. They provide the

required software and infrastructure to the mobile

network operator and to the service providers. In this

mobile value chain, only the MNO has direct contact

with the customer (Kuo, 2006; Questus, 2000;

Barnes, 2001). Although it is possible to introduce

each one of these relationships seperately into the

pricing mechanism, we built a pricing model

between MNO and their end-users.

3 CUSTOMER RELATIONSHIP

MANAGEMENT

As competition increases, many firms use the

Customer Relationship Management (CRM) systems

to improve business intelligence, to make better

decisions, to enhance customer relations, and to

A CRM-BASED PRICING MODEL

119

increase quality of services and product offerings.

CRM could be defined as the strategies, processes,

people and technologies used by companies to

successfully attract and retain customers for

maximum corporate growth and profit (Roh, 2005).

It is a strategic process used to learn more about

customer’s needs and behaviors, in order to provide

them with highest quality products and services.

This results in customer retention and discovering

and attracting new customers.

CRM is also an effective tool for customer

segmentation to develop one-to-one relationships

with customers. In order to truly understand

customers, it is essential to have a segmentation

management that divides customers into smaller

Figure 1: The mobile commerce value chain.

subgroups. Customer segmentation can be defined as

the classification of customers according to their

likely behavior and potential profitability. This intra-

group similarity allows customers in the same

subgroup to respond somewhat similarly to a given

marketing strategy. Especially in telecommunication

industry, the MNOs have to understand the needs of

customers in specific geographical regions and

demographic segments in order to be able to more

successfully offer mobile services to the right people

in an appropriate way. In our proposed model, we

assumed that each subscriber has a CRM value

defined by the MNO. In general, MNOs prefer

outsourcing the CRM service to other service

companies. According to SAS, one of the well-

known solution companies, telecommunication

providers’ best hope for improving profitability is to

focus on improving effectiveness in attracting and

retaining customers, maximizing the value from

each customer relationship, developing more

efficient and dynamic business processes, evaluating

success based on the right performance measures

and aligning the entire organization around unified

strategic directions (www.sas.com).

One of the ways of segmenting customers is

using the Life Time Value (LTV) model (Kim,

2005). In previous studies, LTV is defined as the

sum of the revenues gained from 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 (Dwyer, 1999; Hoekstra, 1999; Jain,

2002). These CRM values are calculated for each

customer and they direct MNOs to classify their

customers. In (Kim, 2005), customer segmentation

methods using CRM values are categorized into

three groups: 1) Segmentation by using only CRM

values, 2) Segmentation by using CRM components,

and 3) Segmentation by considering both CRM

values and other information. In the first method,

customers are sorted in a descending order according

to their CRM values. The list is divided by its

percentile to enable companies to offer different

services and products for each percentile. The

second method segments customers by using CRM

components; such as the current value and the

potential value of the customer, and his loyalty

(Hwang, 2004). The current value is calculated by

the average revenue earned from a customer’s recent

transactions after deducting its debts to the

company. In other words, it is a measure of

customer’s past profitability. The potential value of

costumer is the expected profits from that customer

in the future.

Sex

Educational Back

round

Age

Subscri

tion Duration

Attribute to Pa

Bills

Application’s -: Type

- Time-of-day

- Duration

CRM

value

Figure 2: The components of the CRM value.

The customer loyalty is the rate that specifies the

value that customer will still be the customer of that

company. In the third method, customers are

categorized according to both their CRM values and

other managerial information; such as demographic

information and transaction history.

In our research, we proposed segmenting the

customers of the MNO in terms of the following

categories: customer’s sex, educational background,

Mobile

ment

Wholesaler

Mobile

ment

ardware

Mobile Value-

Added Service

Content Provider

Software Supplier

Infrastructure

lie

nfrastructure

Mobile Network

Operator

Services

ICE-B 2006 - INTERNATIONAL CONFERENCE ON E-BUSINESS

120

age, subscription duration, frequently used

applications, average duration of his applications,

time-of-day of his applications and his attribute of

paying the bills (Figure 2). As each company has its

own marketing strategy, the number and the variety

of selected variables could be different. We

proposed that each customer should have a CRM

value in the range of [0-100] which is being

calculated dynamically in respect to the strength of

these variables.

4 PROPOSED FRAMEWORK

4.1 The Representation of the

Framework Variables

Our proposed model is based on the idea of offering

various quality of service levels to different

customers for different types of mobile services. In

this research, we performed a customer

segmentation both to maximize their satisfaction and

to maximize the revenue of the MNO from each

customer. Although there is not a limit on the

number, in this case study we divided customers into

4 different segments depending on their CRM values

(Figure 3) (IBM, 2003). This segmentation was the

one that IBM has used for its telecommunication

customers. We defined the ranges of segments just

to give an example.

Figure 3: Customer segmentation according to their CRM

values (IBM, 2003)

This classification allows the ability to develop

value driven strategies. The customers with the

highest CRM values, called “Champions”, are

classified as Segment 1. For the company, these

customers provide with high revenue and low cost.

Segment 2 is constituted of customers with CRM

value which is relatively lower than the

“Champions”, called “Demanders”. They provide

with high revenue and high cost. The customers with

the medium CRM value are called “Acquaintances”

and they provide with low revenue and low cost.

Finally, the “Parasites”, the customers with lowest

CRM value, cause low revenue and high cost for the

company (IBM, 2003). Each company may have

different CRM strategies; nevertheless for this case

study, we supposed that our company aims primarily

to satisfy its “Champions” and then, try to promote

its “Demanders” and “Acquaintances” to higher-

valued segment. Our company does not concentrate

on “Parasites”, it just offers them the best possible

quality of service level.

The service concept includes the characteristics

such as throughput, jitter, delay or loss when

transmitting a packet in one direction across a set of

one or more paths within a network. Differentiation

of services accommodates heterogeneous application

requirements and customer expectations, and it

permits differentiated pricing (Blake, 1998). In next

generation wireless networks, different mobile

services will require different amounts of bandwidth

as well as different QoS requirements. In networks

that handle diverse types of data stream, ranging

from file transfer, to real-time traffic such as

streaming audio and video, heterogeneous delay

sensitivities can occur. Therefore, in our proposed

model we mainly categorized the mobile services

based on their tolerance levels to latency or packet

losses due to the next generation of IP, IPv6

(Deering, 1998). Although there is no limit for the

number of classes, as seen in Figure 4, we defined

three different classes of mobile services/products to

represent the various needs of customers. The

services/products class 1 (Gold) with the highest

priority includes the premium products, which

require mostly a large amount of bandwidth and

these services are very sensitive to delays. At the

packet level, applications of this class are forwarded

with the highest priority that the system can offer to

the customers. An instance of application in this

category may be the financial services. Even though

they do not require large amount of bandwidth, they

are very sensitive to delay and loss. The product

class 2 (Silver) and the product class 3 (Bronze)

have respectively lower priority than the product

class 1 (Gold) and they are suitable for applications

requiring less bandwidth. As seen in the Figure 4,

the “Parasites”, the customers in Segment 4, are not

included since they will receive the remaining best

possible service level (Best Effort) after offering to

the customers of the 3 upper segments.

In such an environment, both the customers of

each segment and the MNO should be in social

welfare. Social welfare can be taken to mean the

welfare or well-being of a society. The satisfaction

of a customer depends on his network access, the

services being received and its quality of service

level, while the satisfaction of the MNO depends on

Segment 2: Customers with CRM value 70-85

“Demanders”

Segment 3: Customers with CRM value 50-70

“Acquintances”

ment 4: Customers with CRM value 0-50

“Parasites”

Segment 1: Customers with CRM value 85-100

“Cham

ions”

A CRM-BASED PRICING MODEL

121

the utilization rate of its services. Therefore, we

have to set the most appropriate pricing mechanism

so as to encourage maximum number of customers

to use mobile applications and obtain maximum

revenue from these services. The network’s

resources are used most efficiently if they maximize

the total customer satisfaction. In economic theory,

marginal cost pricing is the concept that a customer

of a system of scarce resources should be charged a

price equal to the marginal cost imposed by the

customer on the system, both on itself (the internal

effect) and on others (the external effect) (Stidham,

2002). In other words, the marginal cost is the

change in total cost associated with producing one

more unit of output. The point where the optimal

efficiency is achieved, usage based charges must be

equal to the marginal cost of usage. Here, the

marginal cost is almost solely a congestion cost;

congestion costs are the performance penalties that

one customer’s traffic imposes on the other

customers.

The social welfare is only maximized when

prices are set equal to marginal cost considering all

externalities (Shenker, 1996). The parameters and

the variables of the model are defined as:

u (x

) : Utility function value that the i

customer

has from j

product/service class.

: Total number of packets of j

product/service

class belonging to the i

customer.

:Delay and retransmission cost experienced by i

customer for one unit of data in the j

product/service class.

d (∑x

) : Average of delays that one unit of i

customer’s data experiences in j

product/service

class.

The utility function value in the model represents

the net benefits of individual customers from mobile

services/ products. According to standard economic

theory, the price should be set equal to the external

effect of a marginal increase in flow at the resource.

When this is done, the algorithm always converges

to a local maximum and never to a saddle point

(Stidham, 2002). In this study in order to determine

the prices, external effects are taken into account.

Therefore, the results that are obtained will be local

maximum points but not saddle points.

d (∑x

) expresses the relationship between the

total traffic flow (the sum of i

customer’s packets)

in j

product/service class with the experienced

delay in this class. We assumed that the numbers of

customers in four different segments are n

, n

and n

, respectively.

4.2 The Utility-based Objective

Function

A socially optimal allocation of products/services

) to the customers is defined as one that

maximizes the aggregate net utility of all customers

of all segments. It may be found by solving the

optimization problem represented as

follows;

Figure 4: Pricing and billing scheme belonging to a customer.

Customer

value

Service/ product

type

Champions

85-100

Service/ product

type

70-85

Demanders

Service/ product

type

Acquaint ances

50-70

Service/ product

type

Product

Class

∑

Product

Class

∑

Product

Class

∑

P roduct

Class

∑

Parasites

0-50

Accounting

Billing

Pricing

Tariff

ICE-B 2006 - INTERNATIONAL CONFERENCE ON E-BUSINESS

122

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three terms represent the sum of each customer’s

utility value that they obtain by using the mobile

services/products. As we focus on the first three

segments not on n

(“Parasites”) in the above

function, we have three expressions of the received

utility by n

customers of the Segment 1, by n

customers of the Segment 2 and by n

customers of

the Segment 3, respectively. The remaining part of

the objective function consists of the experienced

delay costs and retransmission costs. For instance,

the fourth term expresses the total cost experienced

by the “Champions” from the gold class of

product/service; while the fifth term expresses the

total cost experienced by the “Champions” from the

silver class of product/service.

Our main assumption is that the lower

product/service classes suffer from delays

originating not only from their own classes but also

from delays of higher classes. Therefore, these

influences of the higher classes are indicated in the d

(∑x

) expressions. For instance in the sixth term of

the objective function, we can see that the bronze

products/services of the “Champions” suffer from

the silver and gold products/services and also from

its own class of products/services. Naturally when

calculated mobile services prices, these extra costs

should be added to the prices of the higher classes.

Hence, the price should be computed to measure the

congestion cost that the i

customer’s packets

impose on the other customers.

In the objective function, it is assumed that the

utility functions u (x

) are all increasing, concave

and differentiable; likewise, the delay functions d

(∑x

) are all increasing, convex and differentiable,

which are common assumptions in the literature

(Kelly, 2000). The optimal points (x

) where all the

customers are satisfied with the QoS of the

services/products offered, can be calculated by

equalizing the partial derivative of the objective

function for each of the x

(j = 1, 2, 3, i = n

, n

)

to 0.

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customer’s packets impose on the other customers

for each product/services. Here, P

represents the

price of the j

service/product class of the i

customer segment. Therefore, for the first three

customer segments (for j = 1,2,3), the prices of each

service/product class can be calculated by the

following calculations:

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The tricky aspect of our proposed model is that; it

does not consider the customer segment 4, the

“Parasites”, in the objective function. The reason is

the aim of exclusion of these customers from the

socially optimal allocation of products/services (x

In other words, x

receives the remaining value,

namely best effort, after the x

allocations for the

first three:

****

4123

jj j

xxx

−−−

Hence, we used the x

value when calculating P

values.

4.3 Numerical Example

In order to make the proposed model clearer, in this

section we give an example about the

implementation of the model. We chose a network

A CRM-BASED PRICING MODEL

123

with one user who utilizes four types of services in

order to illustrate the model effect. The objective

function for this case takes the form:

with

()

−

The delay expression in the objective function is

consistent with the delay expression of a resource

operating as a processor-sharing single-server queue

with service capacity v. Without loss of generality,

we can assume that v=1 for the first stage of

allocation problem (Stidham, 2002). It seems that it

gives an infinite negative penalty to a 100%

bandwidth allocation; however it is not exactly true.

The delay function is set so as to allocate some

amount of resource to each class of mobile

service/product. This function enables leaving some

idle portion of resource. In this manner, the total

available resource is considered as 1 and the

calculated x

values will signify the percentages of

the total resource allocated to each class of mobile

service/product. Then, the problem of finding the

socially optimal allocation of flows then takes the

form:

11 2 2

12 3

112123

max

11 1

() () ()

ux ux ux

xx xxx

γγ

−−−−−−

−− −

s.t.

123

0, 0, 0

xxx

++<

≥≥≥

Although there are different approaches for the

determination of utility functions, we considered

generating our simulations by use of square root

utility function in the following form: u(x

√x

where a

≥0, b

≥0, and iЄ{1,2,3}. The a

and the b

values symbolize the utility coefficients of

the mobile service/product classes. Therefore, the

utility coefficient value of the gold services/products

is set greater than the one of silver services/products;

because the services/products having more privilege

are assumed to have more utility than the

services/products having less privilege. Besides, the

values in the equation describe the delay and

retransmission cost coefficients. As a unit delay in

the gold services/products should cost more than the

one in the silver services/products, the γ

is set

greater than the γ

value, and γ

greater than the γ

Here is the net utility expression:

12 3 1 1 2

( , , ) 52. 8 20.

uxx x x x x

=+− +

−

+− +

−−

The solutions are as follows: x

=0.5, x

=0.3 and

=0.1. The maximum objective function with these

values is 29.0. These results can be interpreted as

follows; when the 50% of the resources are allocated

to n

Champions, 30% of the resources to n

Demanders, and 10% of the resources to n

Acquintances, both customers and MNO obtain

maximum net utility value which is 29.0. From the

definition, we calculated that 10% of the resources

are idle and they can be allocated for the n

Parasites.

Table 1: The simulation results.

Customer

segments

Champions Demanders Acquintances Parasites

Resource

0.5 0.3 0.1 0.1

Service

classes

G S B G S B G S B G S B

Resource

0.44 0.05 0.008 0,28 0,9 0,09 0,097 0.001 0.001 0,097 0.001 0.001

Net utility

22.7040 15.7301 11.5482 11.5482

Now, these allocated resources have to be

distributed among three different classes of

services/products. At this point, we can utilize the

same objective function, or define another one; but

now we should set the total capacity to 0.5 for the

Champions, to 0.3 for the Demanders and 0.1 for the

Acquintances and the Parasites. The obtained results

are summarized in Table 1. All these x

values

calculated by simulations are the input values of the

price equations.

5 CONCLUSION

In recent years, a number of social, technological

and economic trends have promoted the demand of

mobile communication services. Mobile commerce

is one of the subset of these services. M-commerce

brought many new opportunities and also challenges

to carry out one-to-one customer relationship in e-

business. New actors and new value-added services

enlarged the Internet value-chain and new mobile

value chain is produced. The composition of the

mobile commerce value chain and the relationships

between the actors is extremely complicated; so in

this paper we concentrated on the direct relationship

between the Mobile Network Operator that plays the

role of an intermediator between value chain

members and end-users. For m-commerce to reach

its full potential, MNOs must offer pricing strategies

123 1 2 3 1 1 1

22 12 33 12 3

)()()()()..()

(). ( )(). ( )

(,,

iii i i i i i i

ii ii ii ii i

ux x x ux ux ux x dx

xdxx xdxx x

γγ

=++−

−+−+

ICE-B 2006 - INTERNATIONAL CONFERENCE ON E-BUSINESS

124

of the m-commerce services of maximum

effectiveness to customers.

In this paper, we proposed a CRM-based

framework in order to price different types of mobile

services/products for customers with different

profiles. In the model, customers are first segmented

according to their CRM values and are offered

various service levels. Furthermore for each

customer segment, the services/products are also

differentiated according to their resource

requirements and delay tolerances. The aim is to

suggest a unit price for each service/product class in

each customer segment, which provides the revenue

maximization for MNOs and the network resource

usage optimization. We believed that in a

competitive business environment, tracking the

CRM values of customers and offering the service

according to them is the most beneficial way to

increase services quality and accordingly the

revenues.

There is much work to be done in this area. First

of all, it could be possible to build an architecture to

dynamically track and analyze the CRM variables

belonging to each customer. Since it requires a large

data warehouse, some data mining mechanisms can

be proposed in order to capture customer profiles

and attributes. Besides, these mechanisms could be

useful in measuring customer segments’ utility

functions.

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