The role of Data Mining in Customer Relationship Management

Transcription

1 The role of Data Mining in Customer Relationship Management Mohlabeng M.R1 ISACA Faculty of ICT: Computer Science, Tshwane University of Technology, South Africa, Prof Van der Walt JS2 Faculty of ICT: Department of Computer Science, Tshwane University of Technology South Africa Abstract : This paper has said focus on the role of data mining in Customer Relationship Management. Developments in technology have made relationship promoting a reality these days. Technologies such as data warehousing, data mining, and promotion management software have made customer relationship management a fresh area where firms can achieve a competitive benefit. Mostly through data mining the extraction of unseen predictive information from massive databases, the organizations can recognize valuable customers, envisage future behaviors, and allow firms to make proactive resolutions. Keywords - Data Mining, Customer Relationship Management, Social Network I. INTRODUCTION Organizations these days operate in a forever changing and more complex environment. These changes affect the way they do business both in the private and public sectors, as a result of these changes comes a need to respond quickly to conditions brought by these changes. These changes prompt organizations to change their strategic, tactical and operational decisions, which affect the business processes (Turban et al, 2007). To deal with these changes organizations have adopted the use of Information and a Communication Technology (ICT) to their functions. Marketing function has adopted Customer relationship management (CRM) through ICT to build a profitable relationship with specific customers (Ling & Yen, 2001). CRM framework can be classified into operational and analytical (Berson et al., 2000, Teo et al., 2006), operational CRM refers to automation of business process and analytical CRM refers to the analysis of customer information about their characteristics and behaviors. This helps organizations to allocate resources to the most profitable segment of customers. Most organizations uses data mining tools to analyze data about their customers to gain hidden and valuable knowledge (Berson et al., 2000). Data mining is defined as a complicated data search capability that employs Statistical algorithms to determine patterns and associations in data (Newton s Telecom Dictionary, 2011).

2 With the use of data mining tools organizations can build database of potential customers from the Internet. Social networking site such as Facebook, Myspace, Twitter etc. have a large number of users with a lot of information about them. This paper investigates a data mining model which can be used to mine from social networks to build a good CRM by reviewing literature. II. RESEARCH METHODOLOGY A research methodology can be defined as the process and procedure a researcher will use in order to find the answers to the proposed research question. The methodology encompasses the planning process, data collection, analysis of data, and the final presentation of the results (Marczyk et al, 2005). In this section the plan of the research process and data collection method will be outlined. The study will be a qualitative study, as indicated in this study literature will be reviewed to provide indepth understanding of Data Mining and applicable mining models. III. DESIGN In the study literature will be reviewed to give an overview and a broader understanding of Data Mining and how it can benefit an organization to develop a profitable relationship with their customers, as a result the study will be an interpretive study (Orlikowski & Baroudi, 1991). Data will be collected from Capricorn College IV. DATA COLLECTION A questionnaire will be used to collect data from participants SMEs. This will enable data to be collected quickly from a large number of respondents, and the data that will be collected will be standard which makes it easy to analyse (Chisnall, 2001). This data will be analysed together with the data collected from reviewing literature on case studies. V. CUSTOMER RELATIONSHIP MANAGEMENT (CRM) Customer Relationship Management is distinct by four basics framework: Know Target, Sell, and Service. CRM is fundamentally a two-stage idea. The task of the first stage is to master the fundamentals of building customer focus. This means moving from a product orientation to a customer orientation and creating a market strategy. The focal point should is based on customer needs more than product features (IDC, Cap Gemini, 2002). Customer relationship management (CRM) is a development that supervises the communication between an organization and its customers. The main users of CRM software applications are database marketers who are looking to computerize the procedure of communicating with customers (Kurt Thearling, 2011). VI. THE BASIC STEPS OF DATA MINING FOR CRM Data mining determines patterns and relationships unknown in data, and is really part of a larger practice called knowledge discovery which explains the steps that must be taken to ensure significant outcome. Data mining software reduce the need to know the business, realize the data, or be aware of broad

3 statistical methods. Data mining confirms the findings of patterns and information that can be relied automatically (Edelstein, 2011). According to SPSS Inc (2000), the fundamental steps of data mining for effective CRM are: a. Identify the business problem. Each CRM application has one or more business objective for which you need to construct the appropriate model. An effective statement of the problem includes a means to assess the results of your CRM project. b. Construct a marketing database. Steps two through four comprise the core of the data preparation. There may be frequent iterations of the data preparation and model construction steps as you learn something from the model that suggests you modify the data. c. Discover the data. Before you can build good analytical models, you must recognize your data. Start by gathering a diversity of statistical summaries and looking at the allocation of the data. d. Arrange data for modeling. This is the final data preparation step before structuring models and the step where the most ability comes in. e. Data mining model construction. The most significant thing to keep in mind about model building is that it is an iterative procedure. You require to discover different models to find the one that is most helpful in resolving your business problem. f. Assess your results. Perhaps the mainly overvalued metric for evaluating your results is correctness. g. Integrating data mining in your CRM solution. In building a CRM application, data mining is often a little, element of the finishing product. SooperArticles (2011) has outlined the fundamental Steps of Data Mining for CRMs as follows: a. Problems In order to influence the the data mining services, you require to make sure they center on the correct area. Discover your business problems ahead of time. b. Amend your Database If you have a powerful database to go back on, well and good. This is essential for data preparation. c. Understand the Data The extract appropriate data is vital to expand a sound understanding of the existing information. This is why data mining processes is pave the way by a data discovering phase. d. Arrange Data for Mining you need to plan the raw data for the process. This is where all the raw data is collected into centralized location e. Data Mining finally, the data mining process. This is where the whole data is thoroughly examined to discover appropriate information and particular patterns through which finishing can be made. f. Evaluation of Data A data assessment method is tracked make sure that CRM systems only contain the updated information.

4 VII. DATA MINING IN CRM DM assists to decide the behavior near a specific lifecycle event locate other people in similar life stages and decide which customers are subsequent similar behavior patterns. The below diagram is the Data Mining in CRM (Seyyed Jamaleddin Pishvayi, 2004) Diagram1 Data Mining in CRM VIII. CONCLUSION In selecting appropriate technology for Customer Relationship Management, the companies must be conscious of the tradeoffs when taking into account contradictory of data mining software applications. The selection among diverse options is not as serious as the selection to use data mining technologies in a CRM initiative. Data mining symbolize the connection from the data stored through different communication with customers, and the knowledge necessary to be doing well in relationship marketing ideas. These has a influence in releasing the possible of this information, data mining executes analysis that would be too complex and timeconsuming for statisticians, and appears at earlier unknown information that are used to advance customer retention, response rates, attraction, and cross selling. Through the full execution of a CRM program, which must include data mining, organizations can enhanced loyalty, raise the value of their customers, and draw the right customers. IX. ACKNOWLEDGMENT First we would like to thank God for his strength, comfort, and knowledge that have helped us through our whole life. Without God none of this would have been possible. We would like to thank our families. We would like to thank Mr Sithembiso Mlangeni, for helping us to in assisting us with the research work. Thank you for all your encouragement during this long drawn out process. X. REFERENCES 1. G. Eason, B. Noble, and I. N. Sneddon, On certain integrals of Lipschitz-Hankel type involving products of Bessel functions, Phil. Trans. Roy. Soc. London, vol. A247, pp , April (references) 2. Bruce L. Golden (2011). Models and Applications in Operations Research Edelstein H. Data mining: exploiting the hidden trends in your data. DB2 Online Magazine. Available: (30 April 2011)

5 3. Esa Rinta-Rumsala (2010), Bringing data mining to customer relationship management of every company. VTT Technical Research Centre of Finland 4. Freeman M. The 2 customer lifecycles. Intelligent Enterprise 2000; 2(17):9. 5. IDC & Cap Gemini (2002). Four elements of customer relationship management. Cap Gemini White Paper 6. Kurt Thearling (2011). Data Mining and Customer Relationships (01 May 2011) 7. Newton s Telecom Dictionary, Harry Newton, CMP Books, (30 April 2011) 8. Smith R.H. School of Business Volume 1- Customer Relationship Management through Data Mining 9. SooperArticles (2011) Basic Steps of Data Mining for CRMs (27 May 2011) 10. SPSS Inc (2000). Building profitable customer relationships with data mining, U.S.A, CRMBPWP Seyyed Jamaleddin Pishvayi (2004). Data Mining Techniques for CRM. Tehran University 12. Marczyk G, DeMatteo D & Festinger D Essentials of Research Design and Methodology. John Wiley & Sons: Hoboken, New Jersey. 13. Orlikowski, W.J. & Baroudi, J.J. "Studying Information Technology in Organizations: Research Approaches and Assumptions", Information Systems Research (2) 1991, pp Chisnall, P. (2001) Marketing research. 6th ed. Maidenhead: McGraw-Hill. 15. Efraim, Turban, Jay E. Aronson, Ting-Peng Liang, Ramesh Sharda, 2007 Decision Support and Business Intelligence Systems - Eighth Edition, pp. 3, 753, ISBN