CUSTOMER PERFORMANCE MEASUREMENT

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1 UNIVERSITY OF FRIBOURG SWITZERLAND UNIVERSITÄT FREIBURG SCHWEIZ DEPARTMENT OF INFORMATICS DEPARTEMENT FÜR INFORMATIK SEMINAR FOR MARKETING AND COMMUNICATION SEMINAR FÜR MARKETING & UNTERNEHMENSKOMMUNIKATION Master Thesis Presented to the Faculty of Economics, University of Fribourg, Switzerland In Partial Fulfilment of the Requirements for the Degree of Master of Arts in Management CUSTOMER PERFORMANCE MEASUREMENT Analysis of the Benefit of a Fuzzy Classification Approach in Relationship Management Author: Darius Zumstein Address: Route du Champ-des-Fontaines 24 7 Fribourg (Switzerland) dzumstein(at)gmx.ch Homepage: Mobile: +4 () Place of origin: Burgdorf/Seeberg BE Matriculation #: st Reader: Prof. Dr. Maurizio Vanetti 2 nd Reader: Prof. Dr. Andreas Meier Tutor: Nicolas Werro Date: Fribourg, 7 th of March 27

2 Abstract s are the most valuable asset of a company. As a result, customers have to be classified, analysed, evaluated, segmented and managed according to their value for the company using appropriate tools and methods of Relationship Management (CRM). This master thesis proposes fuzzy classification as a multidimensional data analysis and management method suitable for realising these CRM processes and for establishing profitable customer relationships. In contrast to other data mining and statistical methods, fuzzy classification and fcql (fuzzy Classification Query Language) allow the classification of customers into more than one class at the same time. The application of the fuzzy classification approach to widely used management tools like the SWOT, portfolio and ABC analysis and to scoring models enables a better and fairer classification, segmentation and management of customers. So far, these methods have mostly been applied uncritically with sharp classes, although sharp segmentation can obviously be very arbitrary, imprecise, unfair and discriminatory and may have negative effects. The application of the fuzzy portfolio analysis within the scope of performance measurement is especially suited to classifying, analysing, evaluating and improving important monetary customer performance indicators, like turnover, contribution margins, profit and customer equity, and non-monetary indicators, such as customer value, satisfaction, loyalty and retention. Surprisingly, little research has been done on Performance Measurement (CPM) and customer performance indicators despite the increasing theoretical and practical importance of CRM. This work discusses a holistic customer performance measurement framework with 7+ Performance Indicators (CPIs) and relevant Key Performance Indicators (KCPIs). To avoid misclassifications, to improve the quality of customer evaluations and to exploit customer potential, it is suggested to classify all indicators fuzzily. performance indicators for revenue and profitability, and customer investment, relationship, recommendation, information and cooperation indicators allow to segment customers precisely, to optimise fuzzy classified customer portfolios, to drive the proposed CRM success chain and to define customer strategies in order to increase corporate profits and growth. Key words: Fuzzy classification, fuzzy Classification Query Language (fcql), Relationship Management (CRM), analytical CRM (acrm), customer performance measurement, customer performance indicators, fuzzy customer segmentation, management tools, fuzzy portfolio analysis, fuzzy credit rating. - I -

3 Kurzfassung Kunden sind die wertvollste Ressource eines Unternehmens. Deshalb müssen Kunden durch geeignete Instrumente des Relationship Managements (CRM) entsprechend ihrem Wert für das Unternehmen analysiert, klassifiziert, beurteilt, segmentiert und behandelt werden. Diese Arbeit schlägt unscharfe Klassifikation als eine Analyse- und Managementmethode vor, um solche CRM-Prozesse umzusetzen und profitable Kundenbeziehungen aufzubauen. Die unscharfe Klassifikation und fcql (fuzzy Classification Query Language) verbinden fuzzy logic mit relationalen Datenbanken und erlauben im Gegensatz zu anderen Data Mining und statistischen Methoden, dass Kunden mehreren Klassen gleichzeitig angehören können. Wird der Ansatz der unscharfen Klassifikation auf weit verbreitete Managementinstrumente wie etwa auf die Portfolio-, SWOT-, ABC-Analyse oder Scoring-Modelle angewendet, können Kunden besser und fair klassifiziert, segmentiert und gehandhabt werden. Bis anhin wurden diese Methoden der Kundensegmentierung unkritisch mit trennscharfen Klassen durchgeführt, obwohl scharfe Segmentierung offensichtlich sehr willkürlich, ungenau und diskriminierend sein kann, und womöglich negative Auswirkungen nach sich zieht. Unscharfe Klassifikationen und unscharfe Portfolioanalysen können gerade im Rahmen des Performance Measurement nutzbringend eingesetzt werden, um monetäre Kundenkennzahlen (z.b. Umsätze, Deckungsbeiträge, Gewinne, Kundenwert) und nicht-monetäre Kennzahlen (Kundennutzen, -zufriedenheit, -loyalität oder -bindung) zu beurteilen und zu verbessern. Trotz der grossen theoretischen und praktischen Bedeutung des CRMs gibt es erstaunlicherweise wenig Literatur zu Kundenkennzahlen und zu Kundenkennzahlensystemen. Deshalb diskutiert diese Arbeit ein Performance Measurement (CPM) Framework mit 7+ Performance Indicators (CPIs) und zentralen Key Performance Indicators (KCPIs), und empfiehlt diese unscharf zu klassifizieren and zu bewerten. Kennzahlen über die Kundenprofitabilität, -investitionen und die Kundenbeziehung, sowie über das Weiterempfehlungs-, Informations- und Kooperationsverhalten von Kunden, erlauben den Verantwortlichen, unscharf klassifizierte Kundenportfolios zu optimieren, sowie an den wichtigen und richtigen Stellen der vorgeschlagenen CRM-Erfolgskette die Hebel anzusetzen und Kundenstrategien umzusetzen, um damit dem Unternehmen zu höherem Gewinn und Wachstum zu verhelfen. Stichworte: Unscharfe Klassifikation, fuzzy Classification Query Language (fcql), Relationship Management (CRM), analytisches CRM, Kundenkennzahlensystem, Kundenkennzahlen, unscharfe Kundensegmentierung, Management Tools, unscharfe Portfolioanalyse, unscharfe Kreditwürdigkeitsprüfung. - II -

4 Contents Abstract... I Contents...III List of Figures...V List of Tables...VIII List of Abbreviations...IX Acknowledgement...XI CHAPTER : INTRODUCTION.... Motivation Problem Statement Objectives Outline of the Thesis...5 CHAPTER 2: FUZZY CLASSIFICATION The Approach of Fuzzy Classification Classification as a Database Schema Extension Fuzzy Classification with Linguistic Variables Aggregation Operator Multidimensional Fuzzy Classification Dynamic Fuzzy Classification Fuzzy Classification Query Language (fcql) Introduction Fuzzy Classification Query Examples Architecture of the fcql Toolkit Advantages of Fuzzy Classification and fcql...2 CHAPTER 3: FUZZY CLASSIFICATION MANAGEMENT TOOLS Potential Business Applications for Fuzzy Classification Overview Existing Literature on Marketing and Fuzzy Classification Fuzzy Portfolio Analysis Definition Sharp Classification and Disadvantages Fuzzy Classification and Advantages Fuzzy SWOT Analysis Definition Sharp Classification and Disadvantages Fuzzy Classification and Advantages Fuzzy ABC Analysis Definition Sharp Classification and Disadvantages Fuzzy Classification and Advantages Fuzzy Scoring Methods Definition Sharp Classification and Disadvantages Fuzzy Classification and Advantages III -

5 Contents CHAPTER 4: ANALYTICAL CUSTOMER RELATIONSHIP MANAGEMENT Relationship Management (CRM) Overview The Development to the Oriented Company CRM and Management Definition of CRM Objectives and Key Points of CRM Performance Measurement Definitions Processes of Performance Measurement Performance Indicators Definitions Categories of Performance Indicators Performance Indicators in Business Practice...6 CHAPTER 5: FUZZY CUSTOMER SEGMENTATION Fuzzy Segmentation with Important Indicators Definitions Fuzzy Clustering Methods of Segmentation Selected Indicators for Fuzzy Segmentation Orientation Value Satisfaction Loyalty Retention Repurchases Add-on Selling Share of Wallet Turnover Contribution Margins Profitability Equity and Lifetime Value (CLV) Fuzzy Market Segmentation...9 CHAPTER 6: FUZZY CREDIT RATING Methods of Sharp Credit Rating Definitions Subjective Expertise Statistical Methods Disadvantages of Sharp Credit Rating Methods of Fuzzy Credit Rating Existing Literature on Fuzzy Credit Rating Fuzzy Credit Rating with fcql Other Applications for Fuzzy Classification in Banking...6 CHAPTER 7: CONCLUSION Summary Critical Remarks Outlook...7 References and Further Reading... 8 Appendix Statement IV -

6 List of Figures Figure : Application of Fuzzy Classification to Popular Management Tools... 2 Figure 2: Theoretical Classification of the Master Thesis... 5 Figure 3: Structure of the Master Thesis... 6 Figure 4: Structure of Chapter 2: Fuzzy Classification... 8 Figure 5: Classification Space defined by Attractiveness & Competitive Position.. 9 Figure 6: Concept of Linguistic Variables... Figure 7: Fuzzy Classification with Membership Functions... Figure 8: t-norms, t-conorms and Averaging Operator... 2 Figure 9: Three-Dimensional Sharp (a) and Fuzzy (b) Classification... 4 Figure : Example of Hierarchical Multidimensional Fuzzy Classification... 5 Figure : Dynamic Fuzzy Classification and Implementation of a Trigger Mechanism... 6 Figure 2: Architecture of the fqcl Toolkit... 8 Figure 3: Screenshots of the fcql Toolkit Query Panel... 9 Figure 4: Examples of Tasks and Methods of Data Mining... 2 Figure 5: Fuzzy Classification as a Promising Management Tool for Different Fields Figure 6: The Boston Consulting Group Matrix (a) and Norm Strategies (b) Figure 7: Sharp (a) and Fuzzy (b) BCG Portfolio Figure 8: Sharp (a) and Fuzzy (b) Investments Figure 9: Balancing of Fuzzy Classified Portfolios Figure 2: Sharp (a) and Fuzzy Classified (b) McKinsey/General Electrics Portfolio... 3 Figure 2: Sharp (a) and Fuzzy (b) SWOT Matrix... 3 Figure 22: Fuzzy Strength (a), Weakness (b), Opportunity (c) and Threat (d) Matrices Figure 23: Sharp (a) and Fuzzy (b) Risk Matrix Figure 24: Fuzzy ABC Analysis Figure 25: Fuzzy ABC Analysis with Different Performance Indicators Figure 26: Combination of the Fuzzy Portfolio and ABC Analysis Figure 27: Sharp (a) and Fuzzy (b) RFM Method... 4 Figure 28: Fuzzy RFM Incentives Figure 29: Structure of Chapter 4 and Figure 3: The Development to the -Oriented Company Figure 3: Applications of Fuzzy Classification in the Domain of Management Figure 32: Fuzzy Classification and Individual Marketing Figure 33: The Use of Fuzzy Classification in Typical Tasks of CRM V -

7 List of Figures Figure 34: CRM Application Architecture... 5 Figure 35: Mobile Analytical Relationship Management... 5 Figure 36: CRM Success Chain Figure 37: Dimensions of Performance Measurement Figure 38: Processes of Performance Measurement Figure 39: Measurement Dimensions of the CPIP Profit Figure 4: Measurement Dimensions of the CRI Loyalty Figure 4: Measurement Dimensions of the CReI Number of Recommendations 59 Figure 42: CRM Success Chain with 7+ Performance Indicators... 6 Figure 43: Empirical Results of Performance Measurement in Companies... 6 Figure 44: Fuzzy Methods of Cluster Analysis Figure 45: Sharp (a) and Fuzzy (b) Segments Figure 46: Methods of Segmentation Figure 47: Information Dashboard of Relevant Data Figure 48: Context of Fuzzy Segmentation Figure 49: Indicators of the CRM Success Chain for Fuzzy Segmentation Figure 5: Driving the CRM Success Chain by Optimising Fuzzy Classified Portfolios Figure 5: Fuzzy Cost-Benefit Analysis (a) and Portfolio of Orientation (b)... 7 Figure 52: Examples of Fuzzy Classified Satisfaction Portfolios Figure 53: Loyalty Ladder Figure 54: Examples of Fuzzy Classified Loyalty Portfolios Figure 55: Determinants of Retention Figure 56: Controlling Level and Indicators of Retention Figure 57: Fuzzy Classified Portfolios of Retention Indicators Figure 58: Examples of Fuzzy Classified Repurchase Portfolios Figure 59: Examples of Fuzzy Classified Add-on Selling Portfolios Figure 6: Crisp (a) and Fuzzy (b) Choice... 8 Figure 6: Examples of Fuzzy Classified Share of Wallet Portfolios... 8 Figure 62: Examples of Fuzzy Classified Turnover Portfolios... 8 Figure 63: Contribution Margin Accounting Figure 64: Fuzzy Classified Contribution Margins Portfolios Figure 65: Fuzzy Classification of Profitability Figure 66: Growth Strategies Figure 67: Examples of Fuzzy Classified Equity Portfolios Figure 68: Fuzzy Classified Satisfaction/Equity Portfolio Figure 69: Three-Dimensional Fuzzy Classification of Equity Figure 7: Fuzzy Classified (a) and Prospect (b) Lifetime Value Portfolios VI -

8 List of Figures Figure 7: Sharp (a) and Fuzzy Classified (b) Equity Pyramid... 9 Figure 72: Sharp Market Segmentation... 9 Figure 73: Basic Market-Preferences Patterns Figure 74: Fuzzy Market Segmentation of Income and Age Figure 75: Fuzzy Market Segments and Strategies Figure 76: Discriminant Function and Type I and II Errors Figure 77: Architecture of a Neural Network for Credit Rating Figure 78: Discriminant Functions in Discriminant Analysis and ANN Figure 79: Hierarchy of Creditworthiness with Weights δ and Parameters γ Figure 8: Credit Rating Hierarchy with the Degree of Importance g i of each Criterion... Figure 8: fcql as a Method of Artificial Intelligence... Figure 82: Practice-Related Example of a Hierarchy of Creditworthiness... 2 Figure 83: Examples of a Qualitative and a Quantitative Attribute of Fuzzy Credit Scoring.. 2 Figure 84: Hierarchical Fuzzy Classification of Creditworthiness... 3 Figure 85: Thee-Dimensional Sharp (a) and Fuzzy (b) Credit Rating... 5 Figure 86: Promising Management Tools, Methods and Concepts for Fuzzy Classification. 9 Figure 87: Fuzzy Classified Portfolio (a) and Fuzzy ABC Analysis (b)... Figure 88: Tools and Indicators for Performance Measurement... 3 Figure 89: The Main Challenges of Marketing Controlling in Practice VII -

9 List of Tables Table : Research Questions and Objectives... 4 Table 2: Selected Indicators for Attractiveness and Competitive Position... 9 Table 3: Absolute and Normalised Membership Degress of Smith... 2 Table 4: Membership Degress of the s... 4 Table 5: Basic Scheme of SQL and fcql... 7 Table 6: Criteria for Assessing Industry Attractiveness and Competitive Strength... 3 Table 7: Sharp ABC Analysis Table 8: Fuzzy ABC Analysis Table 9: Example of the RFM Method with Sharp Classes Table : RFM Method: Definition of Classes and Scores... 4 Table : Sharp RFM Scoring of s... 4 Table 2: Fuzzy RFM Scoring of s Table 3: Mass vs. One-to-One Marketing and Applications for Fuzzy Classification Table 4: Drivers of Value and Satisfaction... 7 Table 5: Determinants and Indicators of Equity Table 6: Interest Rates for Different Loan Categories... 5 Table 7: Sharp Classification of the Loan Applicants... 5 Table 8: Results of Research Question (RQ)... 9 Table 9: Results of Research Question 2... Table 2: Results of Research Question 3... Table 2: Results of Research Question Table 22: Results of Research Question Table 23: Results of Research Question Table 24: Results of Research Question VIII -

10 List of Abbreviations # Number acrm analytical Relationship Management AI Artificial Intelligence ANN Artificial Neural Networks BCG Boston Consulting Group BE Balance Error BP Balanced Portfolio BPR Business Process Re-Engineering BSC Balanced Scorecard C Class CAS Computer Aided Selling CCI Cooperation Indicator CCO Chief Officer CII Investment Indicator CInfI Information Indicator CIM Computer Integrated Manufacturing CLV Lifetime Value CP Performance CPI Performance Indicator CPM Performance Measurement CPMS Performance Measurement System CPIP Performance Indicator for Revenue and Profitability CR Relation CRA Relationship Analytics CRC Relationship Communication CReI Recommendation Indicator CRI Relationship Indicator CRM Relationship Management CRO Relationship Operations Cu. DB Database DBMS Database Management System DWH(S) Data Warehouse (System) e electronic EDGE Enhanced Data rates for GSM Evolution EDI Electronic Data Interface Ed(s). Editor(s) EGPRS Enhanced GPRS ( GPRS plus EDGE) ERP Enterprise Ressource Planning fc fuzzy classification FCM fuzzy-c-means (algorithm) fcmt fuzzy Classification Management Tools - IX -

11 List of Abbreviations fcql fuzzy Classification Query Language FMLE Fuzzy-Maximum-Likelihood-Estimation (algorithm) GPRS General Packet Radio Service GSM Global System for Mobile Communication HSDPA High Speed Downlink Packed Access I Indicator ICT Information and Communication Technology IM Information Management IS Information System IT Information Technology KAM Key Account Management KDD Knowledge Discovery in Databases KCPI Key Performance Indicators KPI Key Performance Indicators KSF Key Success Factor L Level MD Membership Degrees MIS Management Information System MOA Market Opportunity Analysis No. Number OLAP On-Line Analytical Processing p(p). page(s) PDA Personal Digital Assistant PM Performance Measurement PMS Performance Measurement System R&D Research & Development RDBMS Relational Database Management System RFM Recency, Frequency, Monetary value ROC(I) Return on (Investment) ROI Return on Investment ROM(I) Return on Marketing (Investment) ROQ Return on Quality ROR Return on Relationship ROS Return on Sales RQ Research Question SCM Supply Chain Management SFA Sales Force Automation SME Small and Medium Enterprises SWOT Strengths, Weaknesses, Opportunities, Threats sc sharp classification SBF Strategic Business Field SBU Strategic Business Units SQL Structured Query Language TQM Total Quality Management UMTS Universal Mobile Telecommunications System Vol. Volume WLAN Wireless Local Area Network - X -

12 Acknowledgement Firstly, I thank Nicolas Werro for asking me to write about fuzzy classification and for the excellent assistance. He gave me the opportunity for a very interesting and exciting trip into new worlds. In addition, I want to thank Prof. Dr. Andreas Meier and Prof. Dr. Maurizio Vanetti that this interdisciplinary project could be realised. I am particularly grateful to my parents, Gabriela, Beatus and Jürg, for supporting me in all the years. They made possible, what was and is so important to me. I am also thankful for all the interesting discussions and the good advice of Florian Schramm and Martin Zöller, and for the corrections of Tau Kevin Musa. This thesis is dedicated to Ela, who supported and loved me so much in the last three years the best ones of my life. - XI -

13 Chapter Introduction - -

14 Chapter : Introduction. Motivation Since Zadeh first published the article Fuzzy Sets in the Journal Information and Control in 965, much scientific research has been done in the field of fuzzy control over all the years. In basic research, many publications have been written on fuzzy logic, fuzzy sets or on fuzzy classification, on different mathematical definitions of the fuzzy classification approach, on its implementations in information systems and on diverse applications in the field of engineering. In fact, fuzzy logic and fuzzy classification is well known in many rather technical disciplines like electronics or engineering, but also in mathematics, statistics, informatics and data mining. In contrast, in marketing and business management fuzzy classification is still largely unknown and rarely used, in both theory and practice. This gap motivated to write this master thesis about the potential and benefit of fuzzy classification in business activities. Consequently, following initial questions raised, which will be discussed in this work in detail and summarised in Chapter 7: there exists an approach of fuzzy classification and the fuzzy Classification Query Language (fcql). However, where can fuzzy classification be used in business management? How could fuzzy classification be used? What for can fuzzy classification be used? Why should fuzzy classification be used? First of all, one management field, which seems to be promising for fuzzy classification, is Relationship Management (CRM). Most researchers and managers have recognised that customers are the most valuable and scarcest assets of a company. As a result, CRM has become very important in the last years. Average overall satisfaction with a tool Open market innovation Mass customisation Core competencies Strategic alliances Growth strategies (customer acquisition) Supply chain management Scenario and contingency planning TQM Offshoring Economical value-added analysis Six sigma Price optimization models Balanced scorecard Activity-based management % % 2% 3% 4% 5% 6% 7% 8% 9% % Usage of tool (in how many of the asked companies a management tool was used) Figure : Application of Fuzzy Classification to Popular Management Tools Strategic planning segmentation Benchmarking CRM Outsourcing Mission and vision statement Business process reengineering Change management Knowledge management Loyality management Promising management tools and fields for fuzzy classification Scale of satisfaction: /2: extremely/somewhat dissatisfied; 3: neither dis-/ nor satisfied; 4/5: somewhat/extremely satisfied; Source data: [Bain & Company 25, p.3]

15 Chapter : Introduction An international study of [Bain & Company 25], shown in Figure, confirms that CRM was applied in 75% of all asked companies and is the second most important management tool in business practice, behind strategic planning (79%) and before customer segmentation (72%) and performance measurement (with the balanced scorecard: 57%). Obviously, most companies were somewhat or very satisfied with the tools. In addition, the management tools seem to be promising for the fuzzy classification approach, as reasoned in the problem statement..2 Problem Statement This thesis will discuss several research questions and problems in the following domains, where fuzzy classification can be used: Fuzzy classification has not often been applied to CRM, although it seems to be suited to improve CRM. As a result, a first research question has to be answered: where, in which CRM fields and processes, how and why could fuzzy classification improve CRM? To analyse and control marketing or CRM, management needs adequate methods, instrument or tools to evaluate customers. However, what are widely used management tools and methods in business practice suited for fuzzy classification? The thesis will explain, how and why fuzzy classification can be applied to such management tools. To manage customers according to their importance for the firm, CRM and marketing need a measurement system to analyse and evaluate the performance of customers. Defining another research question and creating a new term: what is the benefit of fuzzy classification in customer performance measurement? Although many authors of literature on CRM, marketing, accounting and information management emphasise the importance of measuring customer relationships and customer performance, there exist surprisingly few reviews and little literature about the measurement of customer performance. To measure customer performance, CRM requires indicators. However, what kind of indicators does CRM need? What are important customer performance indicators for customer performance measurement? The thesis will work out a concept, how customer performance indicators could be applied, and why and what for they are relevant. segmentation, an important task of analytical CRM and data mining, seems to be particularly interesting for fuzzy classification ( what for ), because it is evidently dangerous to label and classify customers sharply just as "good" (profitable) or "bad" (unprofitable). However, this work argues, how and why fuzzy classification can be used for an exact, fair and enhanced customer segmentation. With a specific problem of customer segmentation, each loan officer of a bank is confronted: when is a loan applicant creditworthy, and when is he not? Answering this last research question, it will be shown, how and why fuzzy classification can be used for credit rating

16 Chapter : Introduction.3 Objectives This thesis tries to answer seven Research Questions (RQ) and their objectives, mentioned in the problem statement and summarised in Table. Answering the research questions and discussing all the points on the right hand side of Table, new theoretical insights should be gained about the benefit of the application of the fuzzy classification approach in customer relationship management. The main aim of this master thesis is to show the possibilities for an improved evaluation and management of customers using fuzzy classification. Table : Research Questions and Objectives # Research Questions (RQ): Objectives: analysis, discussion and evaluation of the possible spectrum of marketing concepts and management tools for the What are potential fields applications of fuzzy classification: and topics for business overview of different promising marketing fields and concepts for applications for fuzzy applications of fuzzy classification classification in marketing? overview of different potential fuzzy management tools and methods discussion of existing literature on marketing and fuzzy classification What are potential management tools and methods for fuzzy classification? What are potential fields, processes and instruments for fuzzy classification in CRM? What are the benefits of fuzzy classification in customer performance measurement? What are important customer performance indicators for customer performance measurement? How can customers be segmented fuzzily? What are the benefits of the fuzzy classification approach in credit rating? application of the fuzzy classification approach to different management tools and instruments of analysis and control: definition and advantages of fuzzy portfolio analysis definition and advantages of fuzzy SWOT analysis definition and advantages of fuzzy ABC analysis definition and advantages of fuzzy scoring methods and RFM method application of fuzzy classification to fields, tasks, processes and instruments of marketing and Relationship Management (CRM): discussion of promising marketing concepts for fuzzy classification, promising fields for fuzzy classification in customer management/crm definition, processes, architecture, objectives and key points of CRM definition of a small CRM success chain application of the fuzzy classification approach to customer performance measurement and management: definition of the term customer performance definition of the term customer performance measurement characteristics and processes of customer performance measurement customer performance measurement in business practice customer performance indicators and key customer performance indicators for holistic customer performance measurement: definition of the term customer performance indicator collection, discussion and categorisation of a comprehensive number of customer performance indicators mapping of customer performance indicators to a big CRM success chain fuzzy segmentation of customers into fuzzy customer segments using important customer performance indicators: definition of the term fuzzy customer segmentation methods and context of fuzzy customer evaluation and segmentation fuzzy customer segmentation with 2 Key Performance Indicators (KCPIs) of the CRM success chain using fuzzy portfolio analysis fuzzy credit rating approach for the evaluation of the creditworthiness of private loan applicants: discussion and disadvantages of methods of sharp credit rating discussion and advantages of methods of fuzzy credit rating example of a hierarchical fuzzy classification for credit scoring calculation of personalised interest rates using fuzzy classification The following outline shows, how the thesis is structured and organised in order to work out a clear concept by discussing the seven research questions

17 Chapter : Introduction.4 Outline of the Thesis The research questions indicate that the objects of research of this work are intersections of different fields in information management: the fuzzy classification approach with the fuzzy Classification Query Language (fcql) is actually a topic of computer science and information technology. However, this thesis discusses different business applications for fuzzy classification and is therefore an approach of business management. The main issues of the thesis, customer performance measurement and customer segmentation (research questions 4 to 6), belong both to marketing and managerial accounting. In addition, research question 7, the task of credit rating, can be assigned to finance (compare Figure 2). Fuzzy classification Objects of research Marketing Relationship Management (CRM) Information management performance measurement segmentation Credit rating Finance Accounting Figure 2: Theoretical Classification of the Master Thesis The master thesis has the following structure: The introducing Chapter 2 will summarise the essential ideas, logic, concepts and the model of fuzzy classification and the fuzzy classification Query Language (fcql). Chapter 3 discusses different potential fields for fuzzy classification (RQ ) and different fuzzy classification management tools (RQ 2): fuzzy portfolio analysis, fuzzy SWOT analysis, fuzzy ABC analysis and fuzzy scoring methods. Chapter 4 deals with the applications of fuzzy classification within analytical CRM (RQ 3), with the conception of customer performance measurement (RQ 4), and with customer performance indicators (RQ 5). In Chapter 5, the developed concept of customer performance measurement and different important indicators are applied to fuzzy customer segmentation (RQ 6). Methods of sharp and fuzzy credit rating (RQ 7) are discussed in Chapter 6. In Chapter 7, conclusion, all findings are summarised. Critical remarks and an outlook on further research questions about business applications for fuzzy classification will round off the thesis

18 Chapter : Introduction Figure 3 shows the structure of the thesis, including the seven Research Questions (RQ).. Motivation. Introduction.2 Problem Statement.3 Objectives.4 Outline 2. Fuzzy Classification 2. The Approach of Fuzzy Classification 2.2 Fuzzy Classification Query Language (fcql) 3. Fuzzy Classification Management Tools 3. Potential Business Applications for Fuzzy Classification RQ 3.2 Fuzzy Portfolio Analysis 3.3 Fuzzy SWOT Analysis 3.4 Fuzzy ABC Analysis 3.5 Fuzzy Scoring Methods RQ 2 Sharp classification and disadvantages Fuzzy classification and advantages 4. Analytical Relationship Management 4. Relationship Management (CRM) 4.2 Performance Measurement 4.3 Performance Indicators RQ 3 RQ 4 RQ 5 5. Fuzzy Segmentation 5. Fuzzy Segmentation with Important Indicators RQ Fuzzy Market Segmentation 6. Fuzzy Credit Rating 6. Methods of Sharp Credit Rating 6.2 Methods of Fuzzy Credit Rating RQ 7 7. Conclusion Figure 3: Structure of the Master Thesis - 6 -

19 Chapter 2 Fuzzy Classification - 7 -

20 Chapter 2: Fuzzy Classification 2. The Approach of Fuzzy Classification 2.. Classification as a Database Schema Extension This technical chapter recapitulates the main findings and research on fuzzy classification of the Information System Research Group at the Department of Informatics at the University of Fribourg (Switzerland). This chapter is based on the research of [Schindler 998a, Meier et al. 2, 23, 25, Werro 25, Werro et al. 25a/b, 26]. However, the concepts are explained using a new example of fuzzy classification, a customer attractiveness/competitive position portfolio. Figure 4 shows the structure of the chapter. Section 2.2 fcql Section 2.: Approach of Fuzzy Classification Subsections Keywords References Classification as a Database Schema Extension Sharp vs. Fuzzy classification Fuzzy Classification with Linguistic Variables Aggregation Operator Multidimensional Fuzzy Classification Dynamic Fuzzy Classification Fuzzy Classification Query Language (fcql) Fuzzy Classification Query Examples Architecture of the fcql Toolkit Advantages of Fuzzy Classification and fcql New example: customer attractiveness/competitive position portfolio Attribute, domain, context, database scheme, equivalence class; Sharp classification and fuzzy classification; Linguistic variables, verbal terms, membership degree, continuous and discrete member functions; Aggregation Operator, compensatory, t-norms, t-conorms Multidimensional fuzzy classification, hierarchical fuzzy classification, decomposition principle; Fuzzy classification over time, monitoring, trigger mechanism; fcql, relational database, fuzzy queries; SQL, fcql/sql basic scheme, fcql syntax; fcql toolkit, fcql tool s architecture, fcql interpreter; Improved classification, reduction of complexity, extraction of hidden data, no migration, easy to use, etc. [Chen 998] [Shenoi 995] [Meier 23a] [Zimmermann 992], [Zimmermann and Zysno 98] Schindler 998a] [Meier et al. 25a/b] [Werro et al. 25] [ Werro et al. 26] Figure 4: Structure of Chapter 2: Fuzzy Classification To define classes in the relational database schema, a context model proposed by [Chen 998] was extended [Meier et al. 2]: To every attribute A j, defined by a domain D(A j ), a context K(A j ) is added. A context K(A j ) is a partition of D(A j ) into equivalence classes. A relational database schema with contexts R(A, K) is then the set A = (A,, A n ) of attributes with associated contexts K = (K (A j ),,K n (A n )) [Shenoi 995]

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