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  1. 1. A CRM APPLICATION IN GSM SECTOR by Nesibe ŞAHİN Ahmet ÖNCE June 2004
  2. 2. A CRM APPLICATION IN GSM SECTOR by Nesibe ŞAHİN Ahmet ÖNCE A thesis submitted to The Industrial Engineering Department of Fatih University June 2004 Istanbul, Turkey ii
  3. 3. A CRM APPLICATION IN GSM SECTOR Nesibe ŞAHİN Ahmet ÖNCE Bachelor Thesis - Industrial Engineering June 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
  4. 4. GSM SEKTÖRÜNDE BİR CRM UYGULAMASI Nesibe ŞAHİN Ahmet ÖNCE Lisans Tezi – Endüstri Mühendisliği Haziran 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
  7. 7. TABLE OF CONTENTS A CRM APPLICATION IN GSM SECTOR....................................................................ii A CRM APPLICATION IN GSM SECTOR..................................................................iii TABLE OF CONTENTS................................................................................................vii 3.1 WHAT IS DATA MINING?.................................................................................25 APPENDIX A………………………………………………………………………….44 APPENDIX B………………………………………………………………………….45 APPENDIX C………………………………………………………………………….93 APPENDIX D……………………………………………………………………….100 APPENDIX E………………………………………………………………………..103 REFERENCES………………………………………………………………………104 vii
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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. 5
  15. 15. 2.2 MISCELLANEOUS DEFINITIONS OF CRM “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
  16. 16. “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 7
  17. 17. decision is frequently a collaborative activity among participants of the decision-making 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. 8
  18. 18. 2.3 DRIVERS FOR CRM APPLICATION 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 9
  19. 19. 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. 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. 10
  20. 20. 2.4 THE CRM INDUSTRY 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 11
  21. 21. 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 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. 12
  22. 22. Table 2. 2: The Major CRM Vendors 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? 13
  23. 23. Four basic tasks are required to achieve the basic goals of CRM. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. 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 The overall processes and applications of CRM are based on the following basic principles. 18
  28. 28. • 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. Privacy issues are not simple. There are overwhelming customer concerns, legal regulations, and public policies around the world. Still it is unclear and undetermined 19
  29. 29. 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
  30. 30. 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 Since 1983, Dell Computers has operated on two simple business ideas: sell computers direct to individual customers and manufacture computers based on the 21
  31. 31. 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 media are designed toward two-way communication. This allows the company to obtain useful information such as lifestyle, demographic, and behavioral data. 22
  32. 32. 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 of the account numbers. These changes increase convenience for both customers and service agents. 23
  33. 33. 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. 24
  34. 34. 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. The field of data mining draws upon several roots, including statistics, machine learning, databases, and high performance computing. 25
  35. 35. 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. Figure 3. 1: Data pyramid 26
  36. 36. 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 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 27
  37. 37. 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. 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. 28
  38. 38. 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 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 29
  39. 39. 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. 30
  40. 40. 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 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 31
  41. 41. 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 32
  42. 42. 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 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: 33
  43. 43. Statgraphic 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 Analysis Summary figure 4. 2: a sample of Analysis Summary window Summary Statistics 34
  44. 44. figure 4. 3: a sample of summary statistic window Describe => Distributions => Distribution Fitting(Uncensored Data) 35
  45. 45. Analysis Summary figure 4. 4: a sample of Analysis summary window 4.2.3 STEP 3: Preparing Reports of These Data 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 36
  46. 46. figure 4. 5: a sample of Density trace diagram figure 4. 6: a sample of Histogram diagram figure 4. 7: a sample of scatter plot 37
  47. 47. 4.2.4 STEP 4: Evaluate These Reports 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. 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. 38
  48. 48. 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. 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 39
  49. 49. 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 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. 40
  50. 50. 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. 41
  51. 51. 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) o Too many tariffs exist in the current condition. It is imposible to promote all these tariffs to potential customers. 42
  52. 52. 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 43
  53. 53. 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 44