ABSTRACT
This work present a data mining model for decision making in telecommunication industry. Nowadays, most sales and marketing organization, around the world face escalating competition which is forcing them not only to aggressively market special pricing programs aimed at retaining existing customers and attracting new ones but also for effective management and allocation of resources, goods and services. In this project work, several literature works were reviewed in other to identify the key performance indicators that makes data mining technique model a powerful tool to be used for carrying out data analytic tasks that will achieve a good result to support decision making in telecommunication providers. Twelve performance indicators were identified in this study. They are accuracy, interpretability, presentation quality, accessibility, consistency, easy to use, precise, concise, robustness, speed (response in time), reliability and unambiguity. The identified performance indicators were assessed and measured against existing system method use for data analytic in telecommunication providers such as Mobile Telephone Networks (MTN), Global communication (GLO), Airtel and Emerging Market Telecommunication services (EMTs). Delphi method were used as the standard of measurement in the existing system. Six performance indicators were found to be weak indicator. They are Presentation quality, accessibility, easy to use, precise, robustness and speed (response in time). The enhanced system was developed which proves to be stronger than the existing system using also Delphi method as a standard of measurement. The model developed is able to make a sales forecast of the year 2016 performance whereas the training data used for model exploratory analysis range from 2008 to 2015. The financial analysis of the proposed model produce a positive Net present value and Return of Investment is 65.85% which is very outstanding and worthy to be considered.
KEYWORDS: data mining, model, telecommunication industry, performance indicators, data mining techniques.
CHAPTER ONE
1.0 Introduction 1
1.1 Background of the Study 1
1.2 Statement of the Study 14
1.3 Aim and Objectives of the Study 15
1.4 Research Question 15
1.5 Scope of the Study 16
1.6 Significance of the Study 17
CHAPTER TWO
2.0 Literature Review 19
2.1 Historical Development of data mining techniques 19
2.2 Empirical Framework of the Study 22
2.3 Concepts, Theories, Model approach and Technology 39
2.3.1 Data mining Algorithm 59
2.4 Conceptual framework of the study 63
2.4.1 Telecommunication Service Provision Variables 63
CHAPTER THREE
3.0 Research Methodology 70 3.1 Preamble 70 3.2 Methodology 73 3.3Data Collection 76 3.3.1 Characteristicsofthepopulation 77 3.3.2 Sampling Design and Procedure 78 3.3.3 DataCollectionInstruments 80
3.4 System Analysis Procedure 81
3.4.1 Performance indicator of the existing system 92
3.4.2 Performance indicator of the proposed system 102
CHAPTER FOUR
4.0 Design and Implementation 103 4.1
Data preparation and System Analysis 103
4.1.1 Data Preparation 103
4.1.2 SystemAnalysis 104
4.1.2.1 Financial Analysis 109
4.2 System Design 117
4.2.1 Logical System Design 117
4.2.1.1 Data flows model of the Existing System 118
4.2.1.2 Use case of the existing system 121
4.2.1.3 Logical data flow model of the proposed system 122
4.2.1.4 Use case of the proposed system
4.2.1.5 High Level Models
4.2.1.6 Input/output Design
4.2.1.7 Database Design
4.2.1.8 Entity relationship diagram
4.2.1.9 System algorithm, flowcharts and data mining method 140
4.2.1.10 Program Flowchart 146 4.2.2 Physical System Design 151
4.2.2.1 Input design Screenshots 152
4.2.2.2 Output design Screenshots 155
4.2.2.3 Database logical design screenshots 156
4.2.2.4 Exploratory Analysis on Data mining Model 159
4.3 System Requirements 163
4.3.1 Hardware Requirement 164
4.3.2 Software Requirements 164
4.3.3 Database Requirements 165
4.4 Implementation and Testing 165
4.4.1 Implementation 165
4.4.2 Testing 166
4.4.2.1 Assessment/ Measurement of the New System 168
4.5 Result Discussion 169
CHAPTER FIVE
5.0 Summary, Conclusion and Recommendation 173
5.1Summary of Findings
5.2 Conclusion
5.3 Recommendation
REFERENCES
APPENDIX A
APPENDIX B
APPENDIX C
APPENDIX D
APPENDIX E
Onwuegbuchulam, C (2021). Data Mining Model for Decision Making in Telecommunication Industry: Case Study of Emerging Market Telecommunication Services (EMTs). Afribary. Retrieved from https://afribary.com/works/data-mining-model-for-decision-making-in-telecommunication-industry-case-study-of-emerging-market-telecommunication-services-emts
Onwuegbuchulam, Chinonso "Data Mining Model for Decision Making in Telecommunication Industry: Case Study of Emerging Market Telecommunication Services (EMTs)" Afribary. Afribary, 23 Feb. 2021, https://afribary.com/works/data-mining-model-for-decision-making-in-telecommunication-industry-case-study-of-emerging-market-telecommunication-services-emts. Accessed 27 Nov. 2024.
Onwuegbuchulam, Chinonso . "Data Mining Model for Decision Making in Telecommunication Industry: Case Study of Emerging Market Telecommunication Services (EMTs)". Afribary, Afribary, 23 Feb. 2021. Web. 27 Nov. 2024. < https://afribary.com/works/data-mining-model-for-decision-making-in-telecommunication-industry-case-study-of-emerging-market-telecommunication-services-emts >.
Onwuegbuchulam, Chinonso . "Data Mining Model for Decision Making in Telecommunication Industry: Case Study of Emerging Market Telecommunication Services (EMTs)" Afribary (2021). Accessed November 27, 2024. https://afribary.com/works/data-mining-model-for-decision-making-in-telecommunication-industry-case-study-of-emerging-market-telecommunication-services-emts