Journal of Business Management Perspective

Journal of Business Management Perspective

Rapid assessment of customers and their classification with the OPportunity LOsess Based POlar COordinate Distance Sort (OPLO-POCOD SORT) and Net Promoter Score (NPS)

Document Type : Original Article

Authors
Reza Sheikh 1
Soheila Senfi 2
1 Associate Professor of Management Department, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran
2 M.A. of Management, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran
10.48308/jbmp.2024.234864.1581
Abstract
Objective:Today, attracting and retaining loyal customers is a requirement for success in the field of competition for organizations. The result of all marketing mix activities is a set of controllable marketing tools that respond to the target market. The quick and accurate identification of customers' behavior and their arrangement in the categories of Promoters, Passives and Detractors helps managers to take timely actions according to the feedback of customers. In this research, while identifying the most important marketing mix criteria, the evaluation and arrangement of customers of a chain store is discussed using the new OPLO-POCOD SORT technique.
Methodology:This technique is one of the quantitative techniques in the field of multi-criteria decision-making. By collecting the opinions of customers and evaluating them, an attempt has been made to sorting customers into profiles and reference options of Promoters, Passives and Detractors.
Findings:This research was conducted in the form of a case study of one of the chain stores in Shahrood and by evaluating the opinions of 20 customers. In order to quickly evaluate and sort the customers based on the technique used, the customers are placed in three strategic categories of Promoters, Passives and Detractors, and due to the negative effect of the Net Promoter Score by -0.25, fundamental reforms are needed.
Conclusion:Based on the results of this research, managers can use the proposed technique of quick evaluation of customers and their classification, to carry out the process of necessary measures to maintain Promoters customers and reduce dissatisfied and Detractors customers.
Keywords
Marketing Mix
Arrangement (Classification)
Multi-Criteria Technique
Reference Profiles
OPLO-POCOD
Subjects
  1. شیخ، رضا، صنفی، سهیال، و ساری خانی، فاطمه. ) 1401(. ارزیابی و کالسه بندی تامین کنندگان برای برون سپاری بر اساس تکنیک پرومسورت.چشم انداز مدیریت بازرگانی، 21)52(، -39 .62
  2. عاملی بصیری، مرضیه و قره خانی، محسن)1393(. تحلیل ارزش مشتریان بر اساس مدل RFM با استفاده از رفتار خرید آنها: مطالعه موردی صنعت بیمه. دومین همایش ملی پژوهشهای کاربردی در علوم کامپیوتر و فناوری اطالعات. بهمن 93
  3. غالمیان، سیداکبر)1398(. استفاده از رویکرد داده کاوی در خوشه بندی مشتریان: مطالعه موردی شرکت طلوع پخش آفتاب. کنفرانس ملی پژوهشهای کاربردی در مدیریت و مهندسی صنایع.
  4. فقیه، مرتضی و رستمی، وحید ) 1398(. تحلیل رفتار خریدار اینترنتی مبتنی بر خوشه بندی با الگوریتم جهش قورباغه. کنفرانس بین المللی علوم، مهندسی، تکنولوژی و کسبوکارهای فناورانه. اردیبهشت .98
  5. کمالیان، امین رضا، امینی الری، منصور، و معزی، حامد. )1388(. بررسی تأثیر سیستم مدیریت ارتباط الکترونیکی با مشتر ی بررضایت مشتریان »موردکاو ی: شرکت چینی بهداشتی گلسار فارس«. چشم انداز مدیریت بازرگانی )چشم انداز مدیریت )پیام مدیریت((، .69-87 ،)32(9
  6. محمدی، عفت، و شیخ، رضا. )1392(. تحلیل خطای هاله ای رفتار مشتریان با استفاده از شاخص مروجان خالص )NPS )و تئوری مجموعه راف )RST( )مطالعه موردی: تلفن همراه سونی اریکسون(. مدیریت بازرگانی، 5)1(، .142-119
  7. مصلحی،سیده نیره، کفاش پور، آذر و ناجی عظیمی، زهرا)1393(. استفاده از مدل LRFM برای بخشبندی مشتریان براساس ارزش چرخه عمر آن ها. فصلنامه پژوهشهای مدیریت عمومی، )7(،25 .140-119
  8. میرمحمدی، سیدمحمد، نژندفرد، متان سادات، و ایزدخواه، محمدمهدی. ) 1395(. بخش بندی فروشگاههای زنجیره ای بر مبنای مزایا ی مورد انتظار مشتریان )مورد پژوهشی فروشگاه زنجیره ای آدان(. پژوهش های مدیر یت راهبردی، 22)61 (، .28-9
  9. Ahn, H., Ahn, J. J., Oh, K. J., & Kim, D. H. (2011). Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques. Expert Systems with Applications, 38(5), 5005-5012.
  10. Ali Yari, M., Modiri, M., Khalili Damghani, K., & Fathi Hafshjani, K. (2023). Measuring Customer Satisfaction Using Multi-Criteria Analysis Model of Customer Satisfaction to Evaluate Product Lines (Case Study: Kaveh Glass Industrial Group). Journal of Information and Organizational Sciences, 47(2), 283-304.
  11. Al-Mudimigh, A. S., Saleem, F., Ullah, Z., & Al-Aboud, F. N. (2009). Implementation of data mining engine on CRM-improve customer satisfaction. In 2009 International Conference on Information and Communication Technologies (pp. 193-197). IEEE.
  12. Al-Samirae, Z., Alshibly, M. S., & Alghizzawi, M. (2020). Excellence in drawing up marketing mix strategies for small and medium enterprises (SMEs) and their impact on the marketing performance. Business, Management and Economics Research, 6(3), 30-36.
  13. Bahador, M. H. H. (2019). The effect of marketing mix on organizations performance. In 1st Strategic Management Conference (Vol. 1, p. 10).
  14. Balestra, G., & Anna, O. (1994). Segmentation problems and neural networks, applying multiple criteria aid for decision to environmental management. In Titolo volume non avvalorato.
  15. Brans, J. P., & Mareschal, B. (1994). The PROMCALC & GAIA decision support system for multicriteria decision aid. Decision support systems, 12(4-5), 297-310.
  16. Brans, J. P., & Vincke, P. (1985). Note—A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making). Management science, 31(6), 647-656.
  17. Dam, S. M., & Dam, T. C. (2021). Relationships between service quality, brand image, customer satisfaction, and customer loyalty. The Journal of Asian Finance, Economics and Business, 8(3), 585-593.
  18. Deng, W. J., Chen, W. C., & Pei, W. (2008). Back-propagation neural network based importance–performance analysis for determining critical service attributes. Expert Systems with Applications, 34(2), 1115-1125.
  19. Faisal, A. (2024). The Impact of Service Quality Dimensions on Customers Satisfaction at Jordanian. International Journal of Business Analytics and Security (IJBAS), 4(1), 25-37.
  20. Hill, N., & Alexander, J. (2017). The handbook of customer satisfaction and loyalty measurement. Routledge.
  21. Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert systems with applications, 38(11), 14336-14345.
  22. Jacquet-Lagreze, E. (1995). An application of the UTA discriminant model for the evaluation of R&D projects. Advances in multicriteria analysis, 5, 203-211.
  23. Jacquet-Lagreze, E., & Siskos, J. (1982). Assessing a set of additive utility functions for multicriteria decision-making, the UTA method. European journal of operational research, 10(2), 151-164.
  24. Johnson, R. O. B. E. R. T., & Sullivan, A. C. (1981). Segmentation of the consumer loan market. Journal of Retail Banking, 3(3), 1-7.
  25. Kotler, P., & Armstrong, G. M. (2010). of marketing. Pearson Education India.
  26. Kotler, P., & Keller, K. L. (2009). Manajemen pemasaran.
  27. Kukanja, M., Gomezelj Omerzel, D., & Kodrič, B. (2017). Ensuring restaurant quality and guests’ loyalty: an integrative model based on marketing (7P) approach. Total Quality Management & Business Excellence, 28(13-14), 1509-1525.
  28. Kushwaha, G. S., & Agrawal, S. R. (2015). An Indian customer surrounding 7P׳ s of service marketing. Journal of Retailing and consumer services, 22, 85-95.
  29. Leninkumar, V. (2017). The relationship between customer satisfaction and customer trust on customer loyalty. International Journal of Academic Research in Business and Social Sciences, 7(4), 450-465.
  30. Marques, A., Lacerda, D. P., Camargo, L. F. R., & Teixeira, R. (2014). Exploring the relationship between marketing and operations: Neural network analysis of marketing decision impacts on delivery performance. International Journal of Production Economics, 153, 178-190.
  31. Mousseau, V., Slowinski, R., & Zielniewicz, P. (2000). A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support. Computers & operations research, 27(7-8), 757-777.
  32. Nemery, P., & Lamboray, C. (2008). ℱlow ort: a flow-based sorting method with limiting or central profiles. Top, 16(1), 90-113.
  33. Oliver, R. L. (1999). Whence consumer loyalty?. Journal of marketing, 63(4_suppl1), 33-44.
  34. Pyon, C. U., Woo, J. Y., & Park, S. C. (2010). Intelligent service quality management system based on analysis and forecast of VOC. Expert Systems with Applications, 37(2), 1056-1064.
  35. Rane, N. L., Achari, A., & Choudhary, S. P. (2023). Enhancing customer loyalty through quality of service: Effective strategies to improve customer satisfaction, experience, relationship, and engagement. International Research Journal of Modernization in Engineering Technology and Science, 5(5), 427-452.
  36. Ranggadara, I., Wang, G., & Kaburuan, E. R. (2019). Applying customer loyalty classification with RFM and Naïve Bayes for better decision making. In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 564-568). IEEE.
  37. Reinartz, W., & Kumar, V. I. S. W. A. N. A. T. H. A. N. (2002). The mismanagement of customer loyalty. Harvard business review, 80(7), 86-94.
  38. Roy, B. (1981). The optimisation problem formulation: criticism and overstepping. Journal of the Operational Research Society, 32(6), 427-436.
  39. Roy, B. (2013). Multicriteria methodology for decision aiding (Vol. 12). Springer Science & Business Media.
  40. Royne Stafford, M. (1996). Demographic discriminators of service quality in the banking industry. Journal of services marketing, 10(4), 6-22.
  41. Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234-281.
  42. Saaty, T. L. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society, 41(11), 1073-1076.
  43. Sheikh, R., & Senfi, S. (2024). A Novel Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD) Approach to Multiple Criteria Decision-Making. Journal of Mathematics, 2024.
  44. Stefano, N. M., Casarotto Filho, N., Barichello, R., & Sohn, A. P. (2015). A fuzzy SERVQUAL based method for evaluated of service quality in the hotel industry. Procedia CIRP, 30, 433-438.
  45. Sulistiani, H., Muludi, K., & Syarif, A. (2019). Implementation of Dynamic Mutual Information and Support Vector Machine for Customer Loyalty Classification. In Journal of Physics: Conference Series (Vol. 1338, No. 1, p. 012050). IOP Publishing.
  46. Thangeda, R., Kumar, N., & Majhi, R. (2024). A neural network-based predictive decision model for customer retention in the telecommunication sector. Technological Forecasting and Social Change, 202, 123250.
  47. Wei, J. T., Lin, S. Y., Weng, C. C., & Wu, H. H. (2012). A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Systems with Applications, 39(5), 5529-5533.
  48. Woodruff, R. B. (1997). Customer value: the next source for competitive advantage. Journal of the academy of marketing science, 25, 139-153.
  49. Yarimoglu, E. K. (2014). A review on dimensions of service quality models. Journal of marketing management, 2(2), 79-93.
  50. Yi, Y. (1990). A critical review of consumer satisfaction. Review of marketing, 4(1), 68-123.
  51. Zeithaml, V. A., Rust, R. T., & Lemon, K. N. (2001). The customer pyramid: creating and serving profitable customers. California management review, 43(4), 118-142.
  52. Zygiaris, S., & Hameed, Z. (2022). Service quality and customer satisfaction in the post pandemic world: A study of Saudi auto care industry. Frontiers in Psychology, 13, 842141