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A CRM APPLICATION IN GSM SECTOR
by
Nesibe ŞAHİNAhmet ÖNCE
June 2004
A CRM APPLICATION IN GSM SECTOR
by
Nesibe ŞAHİNAhmet ÖNCE
A thesis submitted to
The Industrial Engineering Department
of
Fatih University
June 2004Istanbul, Turkey
ii
A CRM APPLICATION IN GSM SECTOR
Nesibe ŞAHİNAhmet ÖNCE
Bachelor Thesis - Industrial EngineeringJune 2004
Supervisor: Ass. Prof. Erkan Topal
ABSTRACT
CRM and applications of CRM in some sectors are examined. Information about data analysis and data mining techniques are gathered. Simulated three-month-data of calling times of a GSM operator is evaluated, most used tariffs according to Pareto analysis are determined and focused on these tariffs. Statistical distributions of calling times of these tariffs and central tendency values are determined. These three month data is compared in between. After that, the cities that use this GSM operator mostly are considered. The distributions of calling times of these cities’ most used tariffs are determined. These data is evaluated and reports that cover all of these evaluations are prepared. According to these reports, existing conditions are determined, problem in the current system are defined and proposals to improve this system are done.
Keywords: Customer Relationship Management, CRM, Data Mining, GSM, Calling
Times, Statistical Distributions.
iii
GSM SEKTÖRÜNDE BİR CRM UYGULAMASI
Nesibe ŞAHİNAhmet ÖNCE
Lisans Tezi – Endüstri MühendisliğiHaziran 2004
Tez Yöneticisi: Yard. Doç. Erkan Topal
ÖZ
Müşteri ilişkileri Yönetimi(CRM) ve bazı sektörlerdeki CRM uygulamaları incelendi. Veri analizi ve veri değerlendirmesi ile ilgili bilgi edinildi. Aslına uygun şekilde modellenmiş bir GSM şirketinin üç aylık arama süreleri değerlendirildi, en çok kullanılan tarifeleer Pareto analizine gore belirlendi. Bu tarifelerdeki konuşma sürelerinin istatiksel dağılımları ve merkezi eğilim değerleri belirlendi. Bu üç aylık data kendi aralarında kıyaslandı. Daha sonra, bu GSM operatörünün en çok kullanıldığı şehirler tespit edildi. Bu veriler değerlendirildi ve bütün bu değerlendirmeleri kapsayan raporlar hazırlandı. Bu raporlara göre; varolan durum tespit edildi, şimdiki sistemin problemleri belirlendi ve sistemi geliştirmek için önerilerde bulunuldu.
Anahtar Kelimeler: Müşteri ilişkileri Yönetimi, CRM , GSM, Arama Süreleri,
İstatistik Dağılımlar
iv
TO MY FATHER, MOTHER AND MY LITTLE BROTHERS
(NESİBE ŞAHİN)
TO MY FAMILY AND FIANCÊE
(AHMET ÖNCE)
v
ACKNOWLEDGEMENT
WE WANT TO THANK ERKAN TOPAL AND HATİCE UÇAR FOR THEIR GREAT SUPPORTS
ALSO WE WANT THANK OUR PARENTS FOR THEIR MORALLY SUPPORTS
vi
TABLE OF CONTENTS
ABSTRACT.....................................................................................................................iii
ÖZ.....................................................................................................................................iv
DEDICATION…………………………………………………………………………vii
ACKNOWLEDGEMENT…………………………………………………………….viii
LIST OF TABLES……………………………………………………………………....x
LIST OF FIGURES……………………………………………………………………..xi
CHAPTER 1 INTRODUCTION...................................................................................1
1.1 CUSTOMER RELATIONSHIP MANAGEMENT (CRM)...................................1
CHAPTER 2 CUSTOMER RELATIONSHIP MANAGEMENT...............................3
2.1 HISTORY OF CRM MARKET..............................................................................3
2.1.1 Major Vendors..................................................................................................5
2.2 MISCELLANEOUS DEFINITIONS OF CRM......................................................6
2.3 DRIVERS FOR CRM APPLICATION..................................................................9
2.3.1 Reasons For Adopting CRM: The Business Drivers........................................9
2.3.2 Cost Goals......................................................................................................10
2.4 THE CRM INDUSTRY........................................................................................11
2.4.1 Size Of The CRM Industry.............................................................................11
2.4.2 Vendors...........................................................................................................12
2.5 INFORMATION TECHNOLOGIES FOR CRM.................................................13
2.5.1 IT Factors of CRM.........................................................................................14
2.6 RETURN ON INVESTMENT OF IMPLEMENTATION...................................15
2.6.1 Cost And Time...............................................................................................15
2.6.2 Benefits...........................................................................................................16
2.6.3 CRM: Commitment To Customer & Shareholder Value...............................17
2.6.4 ROI Of CRM Project......................................................................................18
2.7 PRINCIPLES OF CRM.........................................................................................18
2.8 CRM ISSUES........................................................................................................19
2.8.1 Customer Privacy...........................................................................................19
vii
2.8.2 Technical Immaturity.....................................................................................20
2.9 CASE STUDIES...................................................................................................21
2.9.1 Amazon.Com..................................................................................................21
2.9.2 Dell.................................................................................................................21
2.9.3 Volkswagen....................................................................................................22
2.9.4 Wells Fargo....................................................................................................23
CHAPTER 3 DATA MINING.....................................................................................25
3.1 What Is Data Mining?...........................................................................................25
3.1.1 Overview Of Data Mining..............................................................................26
3.1.2 Semma............................................................................................................27
3.1.3 Business Intelligence Using Data Mining......................................................30
3.2 PRIVACY.............................................................................................................31
3.2.1 OECD Principles of Data Collection..............................................................31
CHAPTER 4 CASE STUDY.......................................................................................33
4.1 Introduction...........................................................................................................33
4.2 Steps Of Our Work................................................................................................33
4.2.1 STEP 1: Determining The Most Used Tariffs And The Cities That The
Operator Is Used Most.............................................................................................33
4.2.2 STEP 2: Finding Statistical Values Of The Calling Times............................33
4.2.3 STEP 3: Preparing Reports of These Data.....................................................36
4.2.4 STEP 4: Evaluate These Reports....................................................................38
4.2.5 STEP 5: Tariff Recommendations For The GSM Operator...........................40
CHAPTER 5 CONCLUSION.....................................................................................42
5.1 A General Look To Crm........................................................................................42
5.2 Results Of Case Study...........................................................................................42
5.2.1 Identification Of Problems............................................................................42
5.2.2 Proposals To These Problems........................................................................43
APPENDIX A………………………………………………………………………….44
APPENDIX B………………………………………………………………………….45
APPENDIX C………………………………………………………………………….93
viii
APPENDIX D……………………………………………………………………….100
APPENDIX E………………………………………………………………………..103
REFERENCES………………………………………………………………………104
ix
LIST OF TABLES
TABLES
Table 2. 1 Estimated CRM Market Size..........................................................................11
Table 2. 2: The Major CRM Vendors.............................................................................13
Table 2. 3: IT Factors in CRM........................................................................................15
Table 2. 4: Annual CRM Expenses (in $million)............................................................16
Table 2. 5: Cost Allocations............................................................................................16
Table 2. 6: Benefits of CRM project...............................................................................17
x
LIST OF FIGURES
FIGURES
Figure 2. 1: CRM Market Size........................................................................................11
Figure 3. 1: Data pyramid................................................................................................26
Figure 3. 2 A Wireless Telco Has Evolved Their BI.....................................................31
figure 4. 1: one variable analysis window.......................................................................34
figure 4. 2: a sample of Analysis Summary window.......................................................34
figure 4. 3: a sample of summary statistic window.........................................................35
figure 4. 4: a sample of Analysis summary window.......................................................36
figure 4. 5: a sample of Density trace diagram................................................................37
figure 4. 6: a sample of Histogram diagram....................................................................37
figure 4. 7: a sample of scatter plot.................................................................................37
xi
CHAPTER 1
INTRODUCTION
1.1 CUSTOMER RELATIONSHIP MANAGEMENT (CRM)
Before the advent of the supermarket, the mall, and the automobile, people went
to their neighborhood general store to purchase goods. The proprietor and the small
staff recognized the customer by name and knew the customer's preferences and wants.
The customer, in turn, remained loyal to the store and made repeated purchases. This
idyllic customer relationship disappeared as the nation grew, the population moved
from the farm communities to large urban areas, the consumer became mobile, and
supermarkets and department stores were established to achieve economies of scale
through mass marketing.
Although prices were lower and goods more uniform in quality, the relationship
between the customer and the merchant became nameless and faceless. The personal
relationship between merchant and customer became a thing of the past. As a result,
customers became fickle, moving to the supplier who provided the desired object at
lowest cost or with the most features.
The last several years saw the rise of Customer Relationship Management
(abbreviated CRM) as an important business approach. Its objective is to return to the
world of personal marketing. The concept itself is relatively simple. Rather than market
to a mass of people or firms, market to each customer individually. In this one-to-one
approach, information about a customer (e.g., previous purchases, needs, preferences
and wants) is used to frame offers that are more likely to be accepted. This approach is
made possible by advances in information technology.
1
Remember that CRM is an abbreviation for Customer Relationship Management,
not Customer Relationship Marketing. Management is a broader concept than marketing
because it covers marketing management, manufacturing management, human resource
management, service management, sales management, and research and development
management. Thus, CRM requires organizational and business level approaches –
which are customer centric – to doing business rather than a simple marketing strategy.
CRM involves all of the corporate functions (marketing, manufacturing, customer
services, field sales, and field service) required to contact customers directly or
indirectly. The term “touch points” is used in CRM to refer to the many ways in which
customers and firms interact.
2
CHAPTER 2
CUSTOMER RELATIONSHIP MANAGEMENT
2.1 HISTORY OF CRM MARKET
Before 1993, CRM included two major markets:
1. Sales Force Automation (SFA) and
2. Customer Services (CS).
Sales Force Automation was initially designed to support salespersons in
managing their touch points and to provide them with event calendars about their
customers. SFA’s meaning expanded to include opportunity management that is
supporting sales methodologies and interconnection with other functions of the
company such as production. Followings is the range to sales force automation
capabilities currently available.
1) Sales Force Automation Capabilities
Contact Management : Maintain customer information and contact histories for
existing customers. May include point in the sales cycle and in the customer’s
replenishment cycle.
Activity Management : Provide calendar and scheduling for individual sales people
Communication Management : Communicate via E-mail and fax
Forecasting : Assist with future sales goals, targets, and projections
Opportunity Management : Manage leads and potential leads for new customers
Order Management : Obtain online quotes and transform inquiries into orders
3
Document Management : Develop and retrieve standard and customizable
management reports and presentation documents
Sales Analysis : Analyze sales data
Product Configuration : Assemble alternate product specifications and pricing
Marketing Encyclopedia : Provide updated information about products, prices,
promotions, as well as soft information about individuals (e.g., influence on buying
decisions) and information about competitors
Compared to SFA, Customer Service (CS) is an after sales activity to satisfy
customers. The goal of Customer Service is to resolve internal and external customer
problems quickly and effectively. By providing fast and accurate answers to customers,
a company can save cost and increase customer loyalty and revenue. As mentioned
below, customer services include call center management, field service management,
and help desk management.
2) Customer Services Capabilities
Call Center Management
Provide automated, end-to-end call routing and tracking
Capture customer feedback information for performance measurement, quality
control, and product development.
Field Service Management
Allocate, schedule, and dispatch the right people, with the right parts, at the right
time
Log materials, expenses, and time associated with service orders
View customer history
Search for proven solutions
Help Desk Management
Solve the problem by searching the existing knowledge base
Initiate, modify, and track problem reports
Provide updates, patches, and new versions
4
Today, CRM includes all customer-facing applications, including:
Sales Force Automation (SFA),
Customer Service (CS),
Sales and Marketing Management (SMM), and Contact & Activity
Management..
2.1.1 Major Vendors
The major vendors changed over time. In 1993, the leaders of SFA were Brock
Control, Sales Technologies, and Aurum. Since then, Brock Control changed its name
to Firstwave Technologies, Inc. In 1998, Sales Technologies merged with Walsh
International and now is consolidated into SYNAVANT Inc. to provide pharmaceutical
and healthcare industry relationship management service. Aurum was merged into
Baan, which in turn was acquired by Invensys plc in July 2000.
In the CS area, Scopus, Vantive and Clarify were the major vendors. However,
things also changed rather rapidly:
Siebel merged with Scopus in 1995 and dominated the consolidated CRM
market with 68% market share.
Vantive was bought by Peoplesoft in 1999.
Clarify was bought by Nortel in 1999.
In 1998, the CRM market was divided by Siebel, Vantive (now PeopleSoft),
Trilogy, and Clarify (now Nortel), and Oracle (in that order) plus fewer than 20 other
companies with small market shares.
At the beginning of 2000, Siebel Systems Inc. was the market leader with a 35%
share. Vantive (PeopleSoft) and Clarify Inc. (Nortel) followed. SAP and Oracle
Corporation were introducing new application to the market based on their software
development capabilities. Recent entrants offering Web applications and services
include Silknet Software, E.piphany, and netDialog.
2.2 MISCELLANEOUS DEFINITIONS OF CRM
5
“CRM is a business strategy comprised of process, organizational and technical
change whereby a company seeks to better manage its enterprise around its customer
behaviors. It entails acquiring and deploying knowledge about customers and using this
information across the various customer touch points to increase revenue and achieve
cost reduction through operational efficiencies.”
“CRM is a business philosophy which provides a vision for the way your
company wants to deal with your customers. To deliver that vision, you need a CRM
strategy which gives shape to your sales, marketing, customer service and data analysis
activities. For most companies, the aim of a CRM strategy is to maximize profitable
relationships with customers by increasing the value of the relationship for both the
vendor and the customer.”
“CRM is the establishment, development, maintenance and optimization of the
long term mutually valuable relationships between customers and organizations.
Successful CRM focuses on understanding the needs and desires of the consumer and is
achieved by placing these needs at the heart of business by integrating them with the
organization’s strategy, people, technology, and business processes.”
“The art of creating e-dialogues.” Vic Guerrieri, vice president of sales at
Remedy.
“Managing profitable relationships.” Jim Goldfinger, vice president of CRM
strategy at PeopleSoft.
“Sensing and responding to customers in real-time.” Jon Wurfl, director of CRM
communications at SAP.
“It’s a business approach that builds customer loyalty and retention.” John
O’Connell, chairman and CEO of Staffware.
“Conquering barriers that prevent customers and companies from knowing each
other.” Margaret Gerstenkorn, business development associate at Oncontact Software.
6
“Our industry has not done a good enough job to make that value proposition
clear. I define CRM as a business approach that integrates PPT (people, process and
technology) to maximize relations with all customers. It’s not a one-off, but a complete
approach that coordinates customer-facing operations like sales, marketing, and
customer service. It should help sales, raise productivity, and improve employee
morale.” Barton Goldenberg, founder and president of ISM.
“CRM is a company’s ability to continuously maximize the value of its customer
franchise by effectively allocating scarce resources to specific customers or customer
segments in those areas viewed as having a significant impact on the profit-impacting
behaviors of customers or segments. Successful application of CRM leads to
economically efficient acquisition of additional customers and relationships;
improvement in relationship profitability; and longer periods of retention, the three key
dimensions of value of customer franchises.” Cap Gemini Ernst & Young.
“CRM is a business strategy that goes beyond increasing transaction volume. Its
objectives are to increase profitability, revenue, and customer satisfaction. To achieve
CRM, a company wide set of tools, technologies, and procedures promote the
relationship with the customer to increase sales. Thus, CRM is primarily a strategic
business and process issue rather than a technical issue.”
CRM consists of three components:
customer,
relationship, and
management.
CRM tries to achieve a ‘single integrated view of customers’ and a ‘customer-
centric approach’.
Customer: The customer is the only source of the company’s present profit and
future growth. However, a good customer, who provides more profit with less resource,
is always scarce because customers are knowledgeable and the competition is fierce.
Sometimes it is difficult to distinguish who is the real customer because the buying
decision is frequently a collaborative activity among participants of the decision-making
7
process. Information technologies can provide the abilities to distinguish and manage
customers. CRM can be thought of as a marketing approach that is based on customer
information.
Relationship: The relationship between a company and its customers involves
continuous bi-directional communication and interaction. The relationship can be short-
term or long-term, continuous or discrete, and repeating or one-time. Relationship can
be attitudinal or behavioral. Even though customers have a positive attitude towards the
company and its products, their buying behavior is highly situational. For example, the
buying pattern for airline tickets depends on whether a person buys the ticket for their
family vacation or a business trip. CRM involves managing this relationship so it is
profitable and mutually beneficial. Customer lifetime value (CLV) is a tool for
measuring this relationship.
Management: CRM is not an activity only within a marketing department. Rather
it involves continuous corporate change in culture and processes. The customer
information collected is transformed into corporate knowledge that leads to activities
that take advantage of the information and of market opportunities. CRM required a
comprehensive change in the organization and its people.
Specific software to support the management process involves:
Field service,
E-commerce ordering,
Self service applications,
Catalog management,
Bill presentation,
Marketing programs, and
Analysis applications.
All of these techniques, processes and procedures are designed to promote and
facilitate the sales and marketing functions.
2.3 DRIVERS FOR CRM APPLICATION
8
2.3.1 Reasons For Adopting CRM: The Business Drivers
Competition for customers is intense. From a purely economic point of view,
firms learned that it is less costly to retain a customer than to find a new one. The oft-
quoted statistics go something like this:
By Pareto’s Principle, it is assumed that 20% of a company's customers
generate 80% of its profits.
In industrial sales, it takes an average of 8 to 10 physical calls in person to sell
a new customer, 2 to 3 calls to sell an existing customer.
It is 5 to 10 times more expensive to acquire a new customer than obtain
repeat business from an existing customer. For example, according to the
Boston Consulting Group, the costs to market to existing Web customers is
$6.80 compared to $34 to acquire a new Web customers.
A typical dissatisfied customer tells 8 to 10 people about his or her experience.
A 5% increase in retaining existing customers translates into 25% or more
increase in profitability.
In the past, the prime approach to attracting new customers was through media
and mail advertising about what the firm has to offer. This advertising approach is
scattershot, reaching many people including current customers and people who would
never become customers. For example, the typical response rate from a general mailing
is about 2%. Thus, mailing a million copies of an advertisement yields only 20,000
responses on average.
Another driver is the change introduced by electronic commerce. Rather than the
customer dealing with a salesperson either in a brick and mortar location or on the
phone, in electronic commerce the customer remains in front of their computer at home
or in the office. Thus, firms do not have the luxury of someone with sales skills to
convince the customer. Whereas normally it takes effort for the customer to move to a
competitor’s physical location or dial another 1-800 number, in electronic commerce
firms face an environment in which competitors are only a few clicks away.
9
2.3.2 Cost Goals
Major cost goals of CRM include:
Increase revenue growth through customer satisfaction.
Reduce costs of sales and distribution
Minimize customer support costs
The following examples illustrate tactics to achieve these goals;
1. To increase revenue growth
Increase share of wallet by cross-selling.
2. To increase customer satisfaction
Make the customer’s experience so pleasant that the customer returns to you for
the next purchase.
3. To reduce cost of sales and distribution
Target advertising to customers to increase the probability that an offer is
accepted.
Use web applications to decrease the number of direct sales people and
distribution channels needed.
Manage customer relationships rather than manage products (a change in
marketing).
4. To minimize customer support costs
Make information available to customer service representatives so they can
answer any query.
Automate the call center so that representatives have direct access to customer
history and preferences and therefore can cross-sell.
2.4 THE CRM INDUSTRY
10
2.4.1 Size Of The CRM Industry
Estimates of the size of the CRM industry are shown in Table1 and plotted in
Figure 1. These illustrations show forecasts made in the 1997 to 2000 period by a
number of industry research groups. It is important to realize that the forecasters
generally did not specify what they included in their estimates. Therefore, it is not
possible to tell which expenditures (e.g., hardware, software, mailing, personnel, call
centers …) and which revenues are included in the numbers shown.
Not all values shown in Table 1 are forecasts; some of the values shown were obtained
by taking the forecaster’s growth rate and then interpolating. Interpolated values are
shaded.
Table 2. 1 Estimated CRM Market Size
Figure 2. 1: CRM Market Size
Clearly the forecasts shown vary significantly as they reach the out years because
they are based on different assumptions of the size of the current market at the time of
11
the forecast and the growth rate inferred from the numbers presented. The important
point, is that the market is growing and is multibillion.
2.4.2 Vendors
A few years ago, technology vendors had their own specialties. For example,
Siebel was in sales force automation, Remedy was in helpdesk systems, Davox was in
call center systems, eGain was in e-mail management, and BroadVision was in the
front-end application area. Today, however, there is no specific boundary of vendors.
All vendors are trying to expand their products over the entire CRM area. For example,
Siebel says it can do everything, Davox moved into customer contact management, and
BroadVision is trying to integrate backward with ERP.
Most of CRM vendors came from two different origins:
Back-End Application
Traditional ERP vendors (SAP AG, Oracle Corporation, Baan (now Invensys
plc), and PeopleSoft) acquire, build, and partner their CRM application for ERP
functionality.
Front-End Application
Some companies started with front-end solutions such as personal information
management system (PIMS). Siebel, BroadVision, and Remedy are in this
category.
Starting in late 1998, with the fast development of e-business, many of the larger
players acquired or merged with mid-sized companies to allow them to offer full service
across the entire CRM “sandbox”. Table 2 lists some of the major categories and
players.
Table 2. 2: The Major CRM Vendors
12
2.5 INFORMATION TECHNOLOGIES FOR CRM
CRM differs from the previous method of database marketing in that the database
marketing technique tried to sell more products to the customer for less cost. The
database marketing approach is highly company centric. However, customers were not
kept loyal by the discount programs and the one-time promotions that were used in the
database-marketing programs. Customer loyalty is, indeed, very difficult to obtain or
buy. The CRM approach is customer-centric. This approach focuses on the long-term
relationship with the customers by providing the customer benefits and values from the
customer’s point of view rather than based on what the company wants to sell.
The basic questions that CRM tries to answer are:
1. What is the benefit of the customer?
2. How can we add the customer’s value?
Four basic tasks are required to achieve the basic goals of CRM.
13
1. Customer Identification
To serve or provide value to the customer, the company must know or identify the
customer through marketing channels, transactions, and interactions over time.
2. Customer Differentiation
Each customer has their own lifetime value from the company's point of view and each
customer imposes unique demands and requirements for the company.
3. Customer Interaction
Customer demands change over time. From a CRM perspective, the customer’s long-
term profitability and relationship to the company is important. Therefore, the company
needs to learn about the customer continually. Keeping track of customer behavior and
needs is an important task of a CRM program.
4. Customization / Personalization
“Treat each customer uniquely” is the motto of the entire CRM process. Through the
personalization process, the company can increase customer loyalty. Jeff Bezos, the
CEO of Amazon.com, said, “Our vision is that if we have 20 million customers, then we
should have 20 million stores.” The automation of personalization is being made
feasible by information technologies.
2.5.1 IT Factors of CRM
Traditional (mass) marketing doesn’t need to use information technologies
extensively because there is no need to distinguish, differentiate, interact with, and
customize for individual customer needs. Although some argue that IT has a small role
in CRM, each of the four key CRM tasks depends heavily on information technologies
and systems. Table 3 shows this relationship for the marketing processes, for the goals,
for traditional mass marketing, for CRM, and for the information technologies used in
CRM.
14
Table 2. 3: IT Factors in CRM
2.6 RETURN ON INVESTMENT OF IMPLEMENTATION
2.6.1 Cost And Time
The 1999 Cap Gemini and IDC survey also found that, the average total
investment in CRM of 300 U.S. and Europe companies was $3.1 million. More than
69% of the companies surveyed spent less than $5 million, and more than 13% of the
companies spent over $10 million.
The cost of implementing a CRM system is easily double the Enterprise Resource
Planning (ERP) implementation cost. Average implementation time for an ERP system
is 23 months and the cost of ownership over the first 2 years is from 0.4% to 1.1% of
company revenue.
As shown in Tables 4 and 5, based on GartnerGroup data, the implementation cost
of CRM depends on the industry, project size, and application requirements. According
to GartnerGroup, the average implementation cost of CRM can be between $15,000 and
$35,000 per user in a three-year project.
15
Table 2. 4: Annual CRM Expenses (in $million)
Table 2. 5: Cost Allocations
2.6.2 Benefits
The principal benefits of CRM are to
Improve the organization’s ability to retain and acquire customers
Maximize the lifetime value of each customer (share of wallet)
Improve service without increasing cost of service.
CRM is composed of four continuous processes; customer identification, customer
differentiation, customer interaction, customization. Each process provides distinctive
benefits to the organization. To obtain all of these benefits, sales, marketing, and service
functions need to work together. The benefits are shown in Table 6.
16
Table 2. 6: Benefits of CRM project
Anderson Consulting, based on a survey of more than 500 executives in six
industries (communications, chemicals, pharmaceuticals, electronics/high-tech, forest
products and retail), believes that a 10% improvement of overall CRM capabilities can
add up to $35 million benefits to a $1 billion business unit.
More than 57% of CEOs in another survey with 191 respondents believe that the
major objective of CRM is customer satisfaction and retention. Another 17% said it is
designed to increase cross selling and up selling.
2.6.3 CRM: Commitment To Customer & Shareholder Value
Customer relationship management (CRM) is that part of an enterprise’s business
strategy that enables the entire enterprise to understand, anticipate and manage the
needs of any current and potential customers. CRM is not an event or a technology, or
even an application or a process.
Ideally, CRM is a comprehensive strategy that integrates all areas of business that
touch the customer – though mainly, it is limited to marketing, sales, customer service
and field support — through the integration of people, process and technology. To be
successful, CRM requires acquiring and distributing knowledge about one’s customers
across the enterprise, to balance costs, revenue and profits with customer satisfaction.
Obviously, business processes and key technologies are required to optimize CRM
strategies.
17
In sum, CRM is four things that provide competitive advantage to the enterprise:
o Organizationally, CRM is a strategic focus on the
behavior of, and communication with, the customer.
o Technologically, CRM is based on the use of data
mining to identify customer preferences and behavior.
o In business processes, CRM is the use of this data
to improve efficiencies and effectiveness in marketing, sales and support.
o CRM is a commitment to drive customer
satisfaction and shareholder satisfaction simultaneously. Such action implies
allocating scarce resources to provide a seamless, high-quality experience for a
company’s most valuable customers, and shedding the least desirable
customers.
2.6.4 ROI of CRM Project
It necessary to wait-and-see to determine the Return On Investment (ROI) of
CRM since CRM does not bring any direct monetary benefits after implementation.
Rather, CRM requires a large amount of initial investment in hardware and software
without any immediate cost saving or revenue improvement. The benefits of CRM need
to be measured on a long-term basis. CRM is designed to build long-term relationships
with customers and to generate long-term benefits through increased customer
satisfaction and retention.
A survey of 300 companies conducted at a CRM conference concluded that CRM
is not a cheap, easy, or fast solution. More than two-thirds of CRM projects end in
failure. However, the successful third can obtain up to a 75 % return on investment.
2.7 PRINCIPLES OF CRM
18
The overall processes and applications of CRM are based on the following basic
principles.
Treat Customer Individually,remember customers and treat them individually.
CRM is based on philosophy of personalization. Personalization means the
content and services to customer should be designed based on customer
preferences and behavior.’ Personalization creates convenience to the customer
and increases the cost of changing vendors.
Acquire and Retain Customer Loyalty through Personal Relationship
Once personalization takes place, a company needs to sustain relationships with
the customer. Continuous contacts with the customer – especially when designed
to meet customer preferences – can create customer loyalty.
Select “Good” Customer instead of “Bad” Customer based on Lifetime Value
Find and keep the right customers who generate the most profits. Through
differentiation, a company can allocate its limited resources to obtain better
returns. The best customers deserve the most customer care; the worst customers
should be dropped.
In summary, personalization, loyalty, and lifetime value are the main principles of
CRM implementation.
2.8 CRM ISSUES
2.8.1 Customer Privacy
Customer privacy is an important issue in CRM. CRM deals with large amounts of
customer data through various touch points and communication channels. The
personalization process in CRM requires identification of each individual customer and
collections of demographic and behavioral data. Yet, it is the very information that most
customers consider personal and private. The individual firm is thus caught in an ethical
dilemma. It wants to collect as much information as possible about each customer to
further its sales, yet in doing so it treads at and beyond the bounds of personal privacy.
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Privacy issues are not simple. There are overwhelming customer concerns, legal
regulations, and public policies around the world. Still it is unclear and undetermined
what extent of customer privacy should be protected and shouldn’t be used, but four
basic rules might be considered.
The customer should be notified their personal information is collected and will
be used for specific purposes.
The customer should be able to decline to be tracked.
The customer should be allowed to access their information and correct it.
Customer data should be protected from unauthorized usage.
Some companies provide ‘customer consent form’ to ask the customer to agree to
information collection and usage. Providing personalized service to customer is a way
to satisfy customers who provided their personal information. All of these efforts are
designed to build trust between the company and its customers.
2.8.2 Technical Immaturity
The concept, technologies, and understanding of CRM are still in its early adapter stage.
Most of the CRM technologies are immature and the typical implementation costs and
time are long enough to frustrate potential users.
Many software and hardware vendors sell themselves as complete CRM solution
providers but there is little standardized technologies and protocols for CRM
implementation in the market. Even the scope and extent of ‘what CRM includes’ differ
from vendor to vendor; each has different implementation requirements to achieve the
customer’s expectations.
CRM is one of the busiest industries which occurs frequent merger and
acquisition. Many small companies merge together to compete with large vendor. Large
companies such as PeopleSoft acquired small vendor to enter this ‘hot’ CRM market.
Due to these frequent merger and acquisition, the stable technical support from the
market becomes rare. Vendors publish new version – maybe more integrated software –
of CRM software as frequently as they can and customers should pay for that.
20
Often these technical immaturities or unstable conditions are combined with the
customer requirements which are frequently unclear and lead the project failure. These
technical immaturities may be overcome over time, but the process may be long and
painful.
2.9 CASE STUDIES
2.9.1 Amazon.Com
When you try to buy something from Amazon.com, you can see the following
statement; “Customers who bought this item also bought these items.” If you have any
previous purchasing experience with Amazon.com, the company will support a
‘Welcome to Recommendations’ Web page.
The personalized Web pages, vast selection of products, and low price lead
customer loyalty and long-term relationship of Amazon.com. More than 20 million
people have purchased at Amazon.com. The percentage of returning customers is about
15 to 25 percent, compared with 3 to 5 percent for other ebusiness retailers.
Amazon.com assembles large amounts of information on individual customer
buying habits and personal information. Based on a customer’s previous purchases and
Web surfing information, Amazon.com recommends books, CDs, and other products.
Sometimes a customer buys additional products because of this information. Through
its ‘1-Click’ system, which stores personal information such as credit card number and
shipping address, Amazon.com simplifies the customer buying process.
Like the corner merchant of old, Jeff Bezos, the founder of Amazon.com, believes
the Internet store of the future should be able to guess what the customer wants to buy
before the customer knows. He wants to make Amazon.com Web site that smart and
that personal.
2.9.2 Dell
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Since 1983, Dell Computers has operated on two simple business ideas: sell
computers direct to individual customers and manufacture computers based on the
customer’s order. The individual customer can make his/her system unique and obtain it
directly from the company.
If the system has a problem, the user can contact the Dell Web site directly and get
personalized services by using the customer system service tag number, which is on the
side of the computer. These personalized services also provide related information and
make software downloads available. In addition, a call center provides technical
assistance at multiple levels. If the first level technician cannot resolve the problem, the
customer is routed to a more skilled contact.
Dell is organized by customer segment, such as education, government, small
business, large business, and home, instead of by product lines.
Dell developed ‘Premier Dell.com’ that covers entire processes of computer
ownership: purchasing, asset management, and product support. Premier pages support
online purchasing, standard management, price quotes, and order management.
2.9.3 Volkswagen
Volkswagen AG is the largest automobile maker in Europe. More than 36 million
vehicles carry on their logo. Like other automobile manufacturers, the company is well
informed about its customers and heavily depends on this information. However, they
lose contact with the car owner after the first change of ownership (after an average 3.7
years). As a result, the company does not have current information about many of its
customers.
In 1988, the company started its ‘Customer Come First’ marketing strategy. Under
this strategy, all of the decision-making processes are based on the ‘Voice of Customer.’
The company carefully monitored their response to advertisements, customer
expectations, and customer satisfaction. Customer forums and focus group are used to
hear the customer voice.
Volkswagen developed services such as service guarantee, the emergency plan,
the mobility guarantee, the customer club, and toll-free service phone. All advertising
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media are designed toward two-way communication. This allows the company to obtain
useful information such as lifestyle, demographic, and behavioral data.
The company maintains a central database to provide club card, bonus point
programs, club shops, and Volkswagen magazine. Every contact points with a customer
gives the company more information about the customer, so the company can constantly
improve the quality and value of the customer database.
2.9.4 Wells Fargo
Banking differs from other industries because the average relationship between
customer and bank lasts much longer on the. For example, in the auto industry, the
relationship between the customer and the company is becoming weaker over time. You
don’t need to contact the car dealer or manufacturer once a week or a month. You can
change your oil or maintain your car with different service station. However, once you
open your account in a specific bank, your relationship or dependence to the bank
increases. You may write checks more frequently, have direct deposit, transfer money,
pay bills, and withdraw money. The bank contacts you regularly by sending you your
monthly statement. You can obtain credit card or investment opportunities from the
bank.
Wells Fargo is one of the leading banks which transforms these relationships into
opportunities. It was the first bank which started 24-hour phone banking service and
opened branches in the local supermarket and Starbucks coffee house. Wells Fargo
always tried to provide more touch points to its customers and a one-stop shopping
environment.
Since 1993, Wells Fargo tried to integrate all of its back-end customer information
into its Customer Relationship System. Previously, customer information was managed
by several different backend system. Software was organized by account number, with
each backend system using its own numbering system. Customer service agents found it
difficult to integrate customer information when they received a request to transfer from
one account to another. They had to log on to several different system to obtain the
information and do the transactions requested. In the new system, the service agent can
access all required information by using the customer’s social security number instead
23
of the account numbers. These changes increase convenience for both customers and
service agents.
Wells Fargo provides Internet banking. It built a Web site as a new contact point
in 1995 and provided advanced technologies to its customer. By using online banking,
customers can manage their account anytime and anywhere. Online banking also saves
operating cost of the bank branches.
In the future, Wells Fargo will try to build online customer communities (similar
to America Online or the World Wide Web) in its banking service by responding to
customers’ needs with new technologies. By providing more power to manage their
account and money, Wells Fargo expects to increase customer loyalty and obtain long
term mutual benefits with its customers.
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CHAPTER 3
DATA MINING
3.1 WHAT IS DATA MINING?
Data mining is the semi-automatic discovery of patterns, associations, changes,
anomalies, rules, and statistically significant structures and events in data. That is, data
mining attempts to extract knowledge from data.
Data mining differs from traditional statistics in several ways: formal statistical
inference is assumption driven in the sense that a hypothesis is formed and validated
against the data. Data mining in contrast is discovery driven in the sense that patterns
and hypothesis are automatically extracted from data. Said another way, data mining is
data driven, while statistics is human driven. The branch of statistics that data mining
resembles most is exploratory data analysis, although this field, like most of the rest of
statistics, has been focused on data sets far smaller than most that are the target of data
mining researchers.
Data mining also differs from traditional statistics in that sometimes the goal is to
extract qualitative models which can easily be translated into logical rules or visual
representations; in this sense data mining is human centered and is sometimes coupled
with human-computer interfaces research.
Data mining is a step in the data mining process, which is an interactive, semi-
automated process which begins with raw data. Results of the data mining process may
be insights, rules, or predictive models.
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The field of data mining draws upon several roots, including statistics, machine
learning, databases, and high performance computing.
3.1.1 Overview of Data Mining
To convert the value of the data warehouse or data mart into strategic business
information, many companies are turning to data mining, an emerging technology based
on a new generation of software. Data mining combines techniques including statistical
analysis, visualization, induction, and neural networks to explore large amounts of data
and discover relationships and patterns that shed light on business problems. In turn,
companies can use these findings for more profitable, proactive decision making and
competitive advantage.
Data mining was designed for exploiting massive amounts of data. This process
can be more efficient if you first define what the business problem is, and then
determine the amount of data you will need to solve the problem. By taking this
"bottom up" approach to data mining and involving upper management in the
understanding of business problems and the potential ROI, the process will be much
more acceptable and the goals attainable.
SAS Institute defines data mining as the process of selecting, exploring, and
modelling large amounts of data to uncover previously unknown patterns for a business
advantage. As a sophisticated decision support tool, data mining is a natural outgrowth
of a business investment in data warehousing. The data warehouse provides a stable,
easily accessible repository of information to support dynamic business intelligence
applications.
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Figure 3. 1: Data pyramid
As the next step, organizations employ data mining to explore and model
relationships in the large amounts of data in the data warehouse. Without the pool of
validated and "scrubbed" data that a data warehouse provides, the data mining process
requires considerable additional effort to pre-process data. Although the data warehouse
is an ideal source of data for data mining activities, the Internet can also serve as a data
source. Companies can take data from the Internet, mine the data, and distribute the
findings and models throughout the company via an Intranet. Although data mining
tools have been around for many years, data mining became feasible in business only
after new hardware and software technology advances became available.
Hardware advances--reduced storage costs and increased processor speed--paved
the way for data mining's large-scale, intensive analyses. Inexpensive storage also
encouraged businesses to collect data at a high level of detail, consolidated into records
at the customer level.
Software advances continued data mining's evolution. With the advent of the data
warehouse, companies could successfully analyze their massive databases as a coherent,
standardized whole. To exploit these vast stores of data in the data warehouse, new
exploratory and modeling tools--including data visualization, neural networks, and
decision trees--were developed. Finally, data mining incorporated these tools into a
systematic, iterative process.
3.1.2 Semma
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Data mining is often seen as an unstructured collection of methods, or as one or
two specific analytic tools, such as neural networks. However, data mining is not a
single technique, but an iterative process in which many methods and techniques may
be appropriate. And--like data warehousing--data mining requires a systematic
approach.
Beginning with a statistically representative sample of the data, you can apply
exploratory statistical and visualization techniques, select and transform the most
significant predictive variables, model the variables to predict outcomes, and affirm the
model's accuracy. To clarify the data mining process, SAS Institute has mapped out an
overall plan for data mining. This step-by-step process is referred to by the acronym
SEMMA: sample, explore, modify, model, and assess.
Step 1: Sample
Extract a portion of a large data set big enough to contain the significant
information yet small enough to manipulate quickly. For optimal cost and performance,
SAS Institute advocates a sampling strategy, which applies a reliable, statistically
representative sample of the full detail data. Mining a representative sample instead of
the whole volume drastically reduces the processing time required to get crucial
business information. If general patterns appear in the data as a whole, these will be
traceable in a representative sample. If a niche is so tiny that it's not represented in a
sample and yet so important that it influences the big picture, it can be discovered using
summary methods.
Step 2: Explore
Search speculatively for unanticipated trends and anomalies so as to gain
understanding and ideas. After sampling your data, the next step is to explore them
visually or numerically for inherent trends or groupings. Exploration helps refine the
discovery process. If visual exploration doesn't reveal clear trends, you can explore the
data through statistical techniques including factor analysis, correspondence analysis,
and clustering. For example, in data mining for a direct mail campaign, clustering might
reveal groups of customers with distinct ordering patterns. Knowing these patterns
creates opportunities for personalized mailings or promotions.
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Step 3: Modify
Create, select, and transform the variables to focus the model construction process.
Based on your discoveries in the exploration phase, you may need to manipulate your
data to include information such as the grouping of customers and significant
subgroups, or to introduce new variables. You may also need to look for outliers and
reduce the number of variables, to narrow them down to the most significant ones. You
may also need to modify data when the "mined" data change. Because data mining is a
dynamic, iterative process, you can update data mining methods or models when new
information is available.
Step 4: Model
Search automatically for a variable combination that reliably predicts a desired
outcome. Once you prepare your data, you are ready to construct models that explain
patterns in the data. Modeling techniques in data mining include neural networks, tree-
based models, logistic models, and other statistical models--such as time series analysis
and survival analysis. Each type of model has particular strengths, and is appropriate
within specific data mining situations depending on the data. For example, neural
networks are good at combining information from predictors which support nonlinear
associations with a target.
Step 5: Assess
Evaluate the usefulness and reliability of findings from the data mining process.
The final step in data mining is to assess the model to estimate how well it performs. A
common means of assessing a model is to apply it to a portion of data set aside during
the sampling stage sometimes known as validation data. For a model to be considered
successful and useful, it should work for this validation sample as well as for the
training data used to construct the model. Similarly, you can test the model against
known data. For example, if you know which customers in a file had high retention
rates and your model predicts retention, you can check to see whether the model selects
these customers accurately. In addition, practical applications of the model, such as
partial mailings in a direct mail campaign, help prove its validity.
The Future
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By all accounts, data mining is a technology that is quickly gaining momentum in
the market place. The Gartner Group estimates that over the next 10 years the use of
data mining in target marketing applications will increase from less than 5% to more
than 80%. The META Group estimates that the data mining market will grow to $300
million by 1997 and to $800 million by the year 2000. However, the real promise of
data mining is that software products will increasingly be focused on business solutions.
Data mining functionality will be packaged to integrate seamlessly with existing data
warehouse and business intelligence software--with the accent on solving business
problems rather than on the enabling technology. As a result, organizations using data
mining techniques will be able to understand key business issues more thoroughly and
to present the results of analysis meaningfully to specialist marketing analysts and
general users alike. In learning more about themselves and their customers, these
organizations will see a shift towards true one-to-one relationships with the customers--
ensuring complete customer relationship management. Accurate anticipation of the
customers' actions can lead to increased effectiveness of marketing activities and
decreased financial risks.
3.1.3 Business Intelligence Using Data Mining
Companies typically begin their business intelligence (BI) journey with a focus on
understanding and measuring the outcome of past decisions. But these “rear-view
mirror” technologies can’t provide you with a clear picture of the future — they only
give you a view of the road behind you. Industry leaders are realizing that forward
looking business intelligence is imperative to making better decisions that solve
business problems and keep their companies moving in a profitable direction. They’re
evolving their BI capabilities by adding data mining technology to their operations
because they know that if they don’t — they’ll perish at the hands of competitors that
do. Data mining looks forward to tell you what is most likely to happen — giving you
the power to improve your future. The most evolved business intelligence continually
applies data mining techniques and deploys the results enterprise-wide.
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Figure 3. 2
This graph shows how a wireless telco has evolved their BI — an evolution to solving the problems that affect future profits. They began with reporting that gave them simple measurements. Added OLAP to drill-down to more detail. Focused their BI on the future with data mining. And finally deployed data mining results to their front lines to continually improve ROI.
3.2 PRIVACY
Privacy is an important issue that must be addressed in most Data Mining
exercises. Laws in many countries directly affect Data Mining and are required
knowledge—penalties are often severe. There are OECD Principles of Data Collection.
3.2.1 OECD Principles of Data Collection
o Collection limitation: Data should be obtained
lawfully and fairly, while certain very sensitive data should not be held at all
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o Data quality: Data should be relevant to the stated
purposes, accurate, complete and up-to-date; proper precautions should be taken
to ensure this accuracy
o Purpose specification: The purposes for which
data will be used should be identified, and the data should be destroyed if it no
longer serves the given purpose
o Use limitation: Use of data for purposes other
than specified is forbidden, except with the consent of the data subject or by
authority of law
o Security safeguards: Agencies should establish
procedures to guard against loss, corruption, destruction, or misuse of data
o Openness: It must be possible to acquire
information about the collection, storage, and use of personal data
o Individual participation: The data subject has a
right to access and challenge the data related to him or her
o Accountability: A data controller should be
accountable for complying with measures giving effect to all these principles
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CHAPTER 4
CASE STUDY
4.1 INTRODUCTION
Aim of this project is to establish a Customer Relationship Management System to a
GSM operator. In order to achieve this target, we analyzed some simulated calling data.
Data analyzing and data mining are very important part of CRM because by this way it
is possible to determine customer behaviors, their needs and it is possible to classify the
customers, whose calling behaviors are similar, into clusters. Our work will be
presented below into steps.
4.2 STEPS OF OUR WORK
4.2.1 STEP 1: Determining The Most Used Tariffs And The Cities That The
Operator Is Used Most
We used Pareto analysis to determine the most used and the most affective tariffs
from the simulated data. There were more than 50 tariffs in our simulated data. It was
impossible to evaluate all these tariffs so we determined the most affective ones which
are used totally more than %90 of customers.
4.2.2 STEP 2: Finding Statistical Values Of The Calling Times
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Statgraphic is used as a statistic software and all the data are sent partially to the
Statgraphic in order to find statistical values. Menus that are used in our project will be
shown below:
4.2.2.1 Statgraphic
4.2.2.1.1 Describe => Numeric Data => One Variable Analysis
From this analysis, we used analysis summary and summary statistics tables. We
obtain statistical values of our data from these tables.
Figure 4. 1: one variable analysis window
4.2.2.1.1.1 Analysis Summary
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figure 4. 2: a sample of Analysis Summary window
4.2.2.1.1.2 Summary Statistics
figure 4. 3: a sample of summary statistic window
4.2.2.1.2 Describe => Distributions => Distribution Fitting(Uncensored Data)
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4.2.2.1.2.1 Analysis Summary
figure 4. 4: a sample of Analysis summary window
4.2.3 STEP 3: Preparing Reports of These Data
36
We supported our reports with histograms, density trace and scatter plots. These
plots are obtained from one variable analysis and distribution fitting menus.
See Appendix A Pareto analysis of tariffs for 3 months
See Appendix B for report of detailed analysis of each tariff ( for month_x)
See Appendix C for reports of 3 months (statistical values and graphics)
See Appendix D for report of 3 big cities’ most used tariffs
See Appendix E for reports of the most used tariffs of month_x
figure 4. 5: a sample of Density trace diagram
figure 4. 6: a sample of Histogram diagram
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figure 4. 7: a sample of scatter plot
4.2.4 STEP 4: Evaluate These Reports
4.2.4.1 General Evaluation of Tariffs
o Except Tariff 3, customers’ calls inside GSM
company’s network is about 15-25 minutes per each person. When it is compared with
fixed-line and outside-network calls, it is seen that this value is very low. Generally
GSM companies’ primary aim is to increase inside-network calls, by this way to
increase their profit margin and number of customers. We recommend that some
changes in the application of tariffs should be applied.
o When we observe the tariffs, we see that outside-
network calls are moderately more than inside network calls. This shows that customers
are in contact with people which use other GSM operators so the existing customers of
GSM company may have a tendency to skip to other GSM operators which will make
their inside-network calls cheaper.
o This GSM operator’s customers make fixed-line
calls as the same rate of inside-network calls. This shows us that GSM operator applies
a suitable or maybe cheaper tariff to customers in calling to home phones.
o SMS usage is 1 message per day per customer
which is very small value. GSM operator may apply a cheaper tariff to SMS such as
decreasing the contour per message.
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o Special-to-tariff calls are very succesful because it
is seen that special-to-tariff calls are nearly 10 times more used than inside-network,
outside-network or fixed-line.
o GSM company is more succesful in month_y than
month_x. There is an increasing trend on every category of calling in month_y.
Especially special-to-tariff calls are increased dramatically. According to these data, it
can be said that company’s promotion and some applications which will attract
customers calling behaviours are succesful in month_y.
4.2.4.2 Evaluation of Each Tariff Privately
o It is observed that fixed-line calls are higher in
Tariff 1, but the inside-network and outside-network calls are lower compared with
other tariffs. So we can assume that in this tariff there may be a price discount in fixed-
line calls.
o Tariff 2 is the most used second tariff in all three
months and in this tariff, it is seen that special-to-tariff is used most when it is compared
with the other tariffs. But the inside-network, fixed-line, outside-network calls are not
used much. According to the assumption that special-to-tariff is in calling to some
determined numbers freely, we can judge that this customer type is using the GSM
operator just for special-to-tariff calls, which is not desired for the GSM operator.
o Tariff 3 is different from the other tariffs that we
examined. In tariff 3, there is no special to tariff calls so the rate of inside-network calls
are very high. Also outside-network calls are nearly 2 times more than the other 6
tariffs. Similarly, Fixed-line calls and the average number of SMS are much more than
the other 6 tariffs. So we can say that this tariff is succesful in every category of calling
behaviours.
39
o We estimate that cheaper calling prices or maybe
free calling in inside-network is appliable in Tariff 3. There is no special-to-tariff
application in this tariff. Also we can say that customers of this tariff are active users of
GSM operator which means that these users are using this GSM operator during whole
day.The clues that support our thesis are the average calling times of outside-network
(nearly 2 times more than other tariffs) and average number of messages(nearly 2 times
more than other tariffs). Tariff 3 is more succesful than the other tariffs; Tariff 3 is able
to make the customers to use the GSM operator actively.
o Our assumptions on Tariff 3 which may make this
tariff succesful;
Price of SMS is cheaper than normal price which is applied in many tariffs
The average number of SMS used is an indicator of target customers are
youngsters which are very fond of sending messages
Outside-network calls may also be cheaper than normal price
Fixed-line calls are the same level with the other tariffs. So it can be
assumed that there is no special price applied in these calls.
o All calling times of Tariff 6 and number of
SMS are lower than the other tariffs. Even Special-to-tariff calls are limited to less than
4 hours which is very low compared with other tariff’s special-to-tariff calls.
o Tariff 4, tariff 5, tariff 7 doesn’t attract
attention because they are average not so low not so high values are seen in these
tariffs.
4.2.5 STEP 5: Tariff Recommendations For The GSM Operator
o Tariff A; inside network calls are cheaper. This
tariff is used by many GSM operators in Turkey. These kind of tariffs is affective to
increase the number of customers of GSM operator.
o Tariff B; calling the selected numbers are
cheaper. This kind of tariffs may attract customers and may increase the rate of GSM
40
usage. In this tariff, GSM operator gives customers the chance of selecting limited
number of numbers(2 or 3), and charges callings less.
o Tariff C; the more you talk, the lower you pay.
The price of calling is relevant with the time duration of your call such as; calls less
than 3 minutes are 400.000 TL/minute, call duration between 3 and 7 minutes are
300.000TL/minute, calls more than 7 minutes are 200.000 TL/minute. This kind of
tariffs’s aim is to increase the calling duration so that increase profit.
o Tariff D; time restricted tariff. Cheaper pricing
is used in some slice of time such as; nights (between 23.00 to 08.00). generally hours,
which are not prefered by the customers to make calls, are prefered for these tariffs’
cheaper pricing. The aim of this tariff is to make users call in that not used times with
attractive pricing. This may reduce the calling traffic of network.
o Tariff E; pre-paid short message packages.
Operator gives customers the chance of paying less to number of messages if the
customers prefer to pay a fixed price for those messages. In this tariff messages are
charged less however it increases the revenue. For example; there is a package which is
called “70 message package”. If the normal price of 70 messages are 7 million, you pay
just 4 million. However if you don’t use all of these 70 messages, the payment will not
change. So it can be said that this kind of tariffs are usefull to garanty some amount of
revenue
o Tariff F; pre-paid calling-time-packages. This
tariff has the same mentality with Tariff E. Customer pay less for calling if he/she
chooses prepaid packages. Such as; 100 minute package or 200 minute package.
Customer pays less than the normal price for that much calling times but if he/she don’t
use that much time the payment will not dicrease.
o Tariff G; special tariffs to some customer
segments. GSM operator may offers special tariffs to different customer segments such
as; students, university students, policemen, teachers ...etc. According to the assumption
of these customers will be generally in contact with people who are in the same
profession. GSM operator may offer less price in calling to the people with the same
profession.
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42
CHAPTER 5
CONCLUSION
5.1 A GENERAL LOOK TO CRM
The present is an era of company loyalty to the customer in order to obtain
customer loyalty to the company. Consumers are more knowledgeable than ever before
and, because the customer is more knowledgeable, companies must be faster, more
agile, and more creative than a few years ago. The Internet allows information to be
obtained almost instantaneously. The Internet permits firms to establish a personalized
customer experience through online help, purchase referrals, quicker turn-around on
customer problems, and quicker feedback about customer suggestions, concerns, and
questions.
CRM is very hard to implement throughout a company. The IT department needs
extensive infrastructure and resources to implement CRM databases successfully.
Executives must be willing to support the CRM implementation process forever because
CRM never ends.
5.2 RESULTS OF CASE STUDY
5.2.1 Identification Of Problems
o Calling times are very insufficient
o SMS usage is very low(less than global montly
SMS/user which is 36)
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o Too many tariffs exist in the current condition. It
is imposible to promote all these tariffs to potential customers.
o Inside-network calls are very low in some tariffs
lower than outside-network calls which shows that the users of GSM operator has a
tendency to skip to other GSM operators which will make their inside-network calls
cheaper.
5.2.2 Proposals To These Problems
GSM company should try to increase the number of customers by promotions,
advertisements and succesful tariffs.
o Tariffs should be promoted to top customers
o GSM operator should gain customer loyalty and
reliability. It will be advantage in the condition which the tariffs and opportunities are
very similar with other GSM operators, customer will probably choose the most reliable
one. Besides, if the GSM operator have a good image on people, than it is posiible to
charge more than the other GSM operators in the market.
o Standardization is necessary; most used and
succesful tariffs should be determined and GSM operator should go through these
tariffs.
o GSM operator may apply a cheaper tariff to SMS
such as decreasing the contour per message.
o Pre-paid SMS packages will increase the number
of usage of SMS
o Pre-paid calling times packages will increase
average calling times
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REFERENCES
o Gray, P, and Byun, J, Customer Relationship
Management, California, 2001
o CFO research service, Mining the Value in CRM
Data, CFO publishing corporation, 2002
o Brown, M, and Brocklebank, J, Data Mining,
http://www.cis.upenn.edu/~dale/lProlog/index.html
o SBSS BI, Solving business problems with Statistics and Data Mining, SBSS Inc, 2001
o Williams, G, An Introduction to Data Mining ,CSIRO Australia, 1999
o Grossman,R , Kasif, S, Moore, R, Rocke, D,and Ullman,U, Data Mining Research: Opportunities and Challenges, 1998
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