Document Type : Research Paper
Authors
Abstract
Today evaluation of customers to classify the quality of providing services is one of the main challenges of decision-makers in different organizations. It is difficult to respond to all customers’ demands because of increasing volume of them. On the other hand, in current competitive markets, customers are considered as capital of organizations. This issue results in purposefully study on different groups of customers in competitive markets. One of the effective ways to study the customers and provide the optimism service to them is grouping the market and clustering the customers. In this research first customers classified in appropriate clusters using neural network techniques SOM in order to provide purposefully service , so each customer can deliver proper service according to its cluster. Then by the proposed model in the paper the membership of each client in the appropriate cluster can be predicted by using DEA-DA technique. This model has provided dynamic clustering process for organizations so that by which new customers will be assessed at any moment and their proper cluster is determined with reasonable accuracy.
Keywords
اشتهاردیان، احسان اله؛ فائضی راد، محمدعلی ) 7959 (. به کارگیری شبکه عصبی مصدنوعی بدرای
قیمت گذاری شناور مجوز طر ترافیک تهران جهت مدیریت بهینه شدهر بدا هددف کداهش آلدودگی
- .790 709 ،)96( هوا. مدیریت شهری، 77
تقوا، محمدرضا؛ حسینی بامکان، سیدمجتبی ) 7954 (. ارائه خدمات مناسدب بده مشدتر یان بدالقوه بدا
استفاده از تکنیدک هدا ی داده کداو ی در حدوزه باندک داری الکترونیدک. مطالعدات مددیریت صدنعتی،
- .641 781 ،)69(5
میررفوری، سیدحبیب اله؛ مروتی شریف آبدادی، علد ی؛ اسدد یان اردکدان ی، فدائزه ) 7956 (. طراحد ی
مدلی برای ارزیابی ریسک در زنجیره تأمین با رویکرد شبکه ی عصدب ی مصدنوع ی. مطالعدات مدد یریت
- .67 7 ،)94( صنعتی، 77
Andersen, P., Petersen, N.C., (1993). A procedure for ranking efficient units in data envelopment analysis, Management Science, 39(10), 1261-1264.
Anderson, J., Narus, J. (2004). Business Market Management: Understanding, Creating and Developing Value (2nd ed). Prentice Hall, Englewood Cliffs, NJ.
Charnes, A., Cooper, W.W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 33(1), 429-444.
Chun Tsai, M., Ping Lin, S., Chan Cheng, C., Ping Lin, Y. (2009). The consumer loan default predicting model - An application of DEA-DA and neural network. Expert Systems with Applications, 36(4), 11682-11690.
Garland, R. (2005). Segmenting retail banking customers. Journal of Financial Services Marketing, 10(2), 179-191.
Ghaseminezhad, M.H., Karami, A. (2011). A novel self-organizing map (SOM) neural network for discrete groups of data clustering. Applied Soft Computing, 11(4), 3771–3778.
Godarzi, A.A., Madadi Amiri, R., Talaei, A., Jamasb, T. (2014). Predicting oil price movements: A dynamic Artificial Neural Network approach. Energy Policy, 68, 371-382.
Ju-Fang, C., Kun-Yuan, Y. (2008). Application of activity-based costing in customer profitability analysis. Proceedings of International Seminar on Business and Information Management, 1, 494-497.
Kumar, A., Jain, V., Kumar, S. A. (2014). A comprehensive environment friendly approach for supplier selection. Omega, 42(1), 109-23.
Lee, H.S., Zhu, J. (2012). Super efficiency infeasibility and zero data in DEA. European Journal of Operational Research, 216(10), 429–433.
Mazanec, J. (1992). Classifying Tourist into Market Segments: A Neural Network Approach. Journal of Travel and Tourism Marketing, 1, 39-59.
Noorizadeh, A., Mahdiloo, M. and Farzipoor Saen, R. (2013). Evaluating relative value of customers via data envelopment analysis. Journal of Business & Industrial Marketing, 28(7), 577–588.
Pfeifer, P. (2005). The optimal ratio of acquisition and retention costs. Journal of Targeting, Measurement and Analysis for Marketing, 13(2), 179-188.
Plakoyiannaki, E., Saren, M. (2006). Time and the customer relationship management process: conceptual and methodological insights. Journal of Business & Industrial Marketing, 24(4), 218-230.
Sueyoshi, T. (1999). DEA-discriminant analysis in the view of goal programming. European Journal of Operational Research, 115, 564-582.
Sueyoshi, T. (2001). Extended DEA-Discriminant Analysis. European Journal of Operational Research, 131(2), 324–351.
Sueyoshi, T. (2004). Mixed integer programming approach of extended DEA–discriminant analysi. European Journal of Operational Research, 152(1), 45–55.
در … 781 DEA-DA پویاسازی خوشه بندی مشتریان با استفاده از روش
Sueyoshi, T., Goto, M. (2012). DEA radial measurement for environmental assessment and planning: Desirable procedures to evaluate fossil fuel power plants. Energy Policy, 422-432.
Sueyoshi, T., Goto, M. (2012). Efficiency-based rank assessment for electric power industry: A combined use of Data Envelopment Analysis (DEA) and DEA-Discriminant Analysis (DA). Energy Economics, 634-644.
Sueyoshi, T., Goto, M. (2012). Returns to Scale and Damages to Scale with Strong Complementary Slackness Conditions in DEA Assessment: Japanese Corporate Effort on Environment Protection. Energy Economics, 1422-1434.
Sueyoshi, T., Goto, M. (2013). A use of DEA–DA to measure importance of R&D expenditure in Japanese information technology industry. Decision Support Systems, 54(2), 941–952.
Tisan, A., Cirstea, M. (2013). SOM neural network design - A new Simulink library based approach targeting FPGA implementation. Mathematics and Computers in Simulation, 91, 134-149.
Tone, K., Tsutsui, M., (2014). Dynamic DEA with network structure: A slacks-based measure approach, Omega, 42(1), 124-131.
Van Raaij, E.M. (2005). The strategic value of customer profitability analysis. Marketing Intelligence & Planning, 23(4), 372-381.
Wang, Z., Bian, S., Liu, Y., Liu, Z. (2013) The load characteristics classification and synthesis of substations in large area power grid. International Journal of Electrical Power & Energy Systems, 48, 71-82.
Yousefi, S., Shabanpour, H., Farzipoor Saen, R., Faramarzi, G. R. (2014). Making an ideal decision making unit using virtual network data envelopment analysis approach. International Journal of Business Performance Management, 15(4), 316-328.
Yousefi, S., Shabanpour, H., Farzipoor Saen, R. (2015). Selecting the best supply chain by goal programming and network data envelopment analysis. RAIRO-Operations Research, 49, 601–617.
قیمت گذاری شناور مجوز طر ترافیک تهران جهت مدیریت بهینه شدهر بدا هددف کداهش آلدودگی
- .790 709 ،)96( هوا. مدیریت شهری، 77
تقوا، محمدرضا؛ حسینی بامکان، سیدمجتبی ) 7954 (. ارائه خدمات مناسدب بده مشدتر یان بدالقوه بدا
استفاده از تکنیدک هدا ی داده کداو ی در حدوزه باندک داری الکترونیدک. مطالعدات مددیریت صدنعتی،
- .641 781 ،)69(5
میررفوری، سیدحبیب اله؛ مروتی شریف آبدادی، علد ی؛ اسدد یان اردکدان ی، فدائزه ) 7956 (. طراحد ی
مدلی برای ارزیابی ریسک در زنجیره تأمین با رویکرد شبکه ی عصدب ی مصدنوع ی. مطالعدات مدد یریت
- .67 7 ،)94( صنعتی، 77
Andersen, P., Petersen, N.C., (1993). A procedure for ranking efficient units in data envelopment analysis, Management Science, 39(10), 1261-1264.
Anderson, J., Narus, J. (2004). Business Market Management: Understanding, Creating and Developing Value (2nd ed). Prentice Hall, Englewood Cliffs, NJ.
Charnes, A., Cooper, W.W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 33(1), 429-444.
Chun Tsai, M., Ping Lin, S., Chan Cheng, C., Ping Lin, Y. (2009). The consumer loan default predicting model - An application of DEA-DA and neural network. Expert Systems with Applications, 36(4), 11682-11690.
Garland, R. (2005). Segmenting retail banking customers. Journal of Financial Services Marketing, 10(2), 179-191.
Ghaseminezhad, M.H., Karami, A. (2011). A novel self-organizing map (SOM) neural network for discrete groups of data clustering. Applied Soft Computing, 11(4), 3771–3778.
Godarzi, A.A., Madadi Amiri, R., Talaei, A., Jamasb, T. (2014). Predicting oil price movements: A dynamic Artificial Neural Network approach. Energy Policy, 68, 371-382.
Ju-Fang, C., Kun-Yuan, Y. (2008). Application of activity-based costing in customer profitability analysis. Proceedings of International Seminar on Business and Information Management, 1, 494-497.
Kumar, A., Jain, V., Kumar, S. A. (2014). A comprehensive environment friendly approach for supplier selection. Omega, 42(1), 109-23.
Lee, H.S., Zhu, J. (2012). Super efficiency infeasibility and zero data in DEA. European Journal of Operational Research, 216(10), 429–433.
Mazanec, J. (1992). Classifying Tourist into Market Segments: A Neural Network Approach. Journal of Travel and Tourism Marketing, 1, 39-59.
Noorizadeh, A., Mahdiloo, M. and Farzipoor Saen, R. (2013). Evaluating relative value of customers via data envelopment analysis. Journal of Business & Industrial Marketing, 28(7), 577–588.
Pfeifer, P. (2005). The optimal ratio of acquisition and retention costs. Journal of Targeting, Measurement and Analysis for Marketing, 13(2), 179-188.
Plakoyiannaki, E., Saren, M. (2006). Time and the customer relationship management process: conceptual and methodological insights. Journal of Business & Industrial Marketing, 24(4), 218-230.
Sueyoshi, T. (1999). DEA-discriminant analysis in the view of goal programming. European Journal of Operational Research, 115, 564-582.
Sueyoshi, T. (2001). Extended DEA-Discriminant Analysis. European Journal of Operational Research, 131(2), 324–351.
Sueyoshi, T. (2004). Mixed integer programming approach of extended DEA–discriminant analysi. European Journal of Operational Research, 152(1), 45–55.
در … 781 DEA-DA پویاسازی خوشه بندی مشتریان با استفاده از روش
Sueyoshi, T., Goto, M. (2012). DEA radial measurement for environmental assessment and planning: Desirable procedures to evaluate fossil fuel power plants. Energy Policy, 422-432.
Sueyoshi, T., Goto, M. (2012). Efficiency-based rank assessment for electric power industry: A combined use of Data Envelopment Analysis (DEA) and DEA-Discriminant Analysis (DA). Energy Economics, 634-644.
Sueyoshi, T., Goto, M. (2012). Returns to Scale and Damages to Scale with Strong Complementary Slackness Conditions in DEA Assessment: Japanese Corporate Effort on Environment Protection. Energy Economics, 1422-1434.
Sueyoshi, T., Goto, M. (2013). A use of DEA–DA to measure importance of R&D expenditure in Japanese information technology industry. Decision Support Systems, 54(2), 941–952.
Tisan, A., Cirstea, M. (2013). SOM neural network design - A new Simulink library based approach targeting FPGA implementation. Mathematics and Computers in Simulation, 91, 134-149.
Tone, K., Tsutsui, M., (2014). Dynamic DEA with network structure: A slacks-based measure approach, Omega, 42(1), 124-131.
Van Raaij, E.M. (2005). The strategic value of customer profitability analysis. Marketing Intelligence & Planning, 23(4), 372-381.
Wang, Z., Bian, S., Liu, Y., Liu, Z. (2013) The load characteristics classification and synthesis of substations in large area power grid. International Journal of Electrical Power & Energy Systems, 48, 71-82.
Yousefi, S., Shabanpour, H., Farzipoor Saen, R., Faramarzi, G. R. (2014). Making an ideal decision making unit using virtual network data envelopment analysis approach. International Journal of Business Performance Management, 15(4), 316-328.
Yousefi, S., Shabanpour, H., Farzipoor Saen, R. (2015). Selecting the best supply chain by goal programming and network data envelopment analysis. RAIRO-Operations Research, 49, 601–617.
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