بخش‌بندی و تعیین نیمرخ مشتریان شعب بانک کشاورزی اهواز با استفاده از الگوریتم شبکه‌های عصبی خودسازمانده
محورهای موضوعی : مدیریت بازرگانی
1 - استادیار، گروه مدیریت بازرگانی، واحد امیدیه، دانشگاه آزاد اسلامی، امیدیه، ایران.
کلید واژه: بخش‌بندی, نیمرخ مشتریان, بانک کشاورزی, الگوریتم شبکه‌های عصبی خودسازمانده.,
چکیده مقاله :
هدف از انجام این پژوهش، بخش‌بندی و تعیین نیمرخ مشتریان با استفاده ازالگوریتم شبکه‌های عصبی خودسازمانده (مورد مطالعه: مشتریان شعب بانک کشاورزی شهرستان اهواز) است. تحقیق حاضر در گروه روش‌های کمّی قرار می‌گیرد. روش پژوهش براساس هدف از نوع كاربردي و براساس چگـونگي جمـع‌آوري داده‌هـا توصـيفی (غيرآزمايشي) - پيمايشي و از نظر مکان نیز یک تحقیق میدانی است. جامعه آماری این تحقیق مشتریان بانک کشاورزی شهرستان اهواز می‌باشند. ازآنجایی‌که جامعه آماری این تحقیق نامحدود است، حجم نمونه بر اساس فرمول کوکران جامعه نامحدود، به تعداد 384 نفر تعیین شده است. ابزار پژوهش، پرسشنامه است. در این پژوهش روایی محتوا از طریق نظرخواهی از استاد راهنما و سایر اساتید صاحب نظر در قلمرو موضوعی این پژوهش صورت گرفت و پس از اعمال این نظرات در مورد محتوای پرسشنامه و رفع اشکالات موجود، پرسشنامه مور تأیید واقع شد. تجزیه و تحلیل داده‌ها به روش تحلیل شبکه عصبی SOM در نرم افزار MATLAB انجام شده است. پس از اجرای تحلیل داده‌ها، يافته‌هاي پژوهش به شناسايي پنج دستة متفاوت از مشتریان با ویژگی‌های رفتاری و جمعیت شناختی متفاوت شد. این 5 بخش عبارتند از: مشتریان کلان سنتی و ارزشمند، مشتریان طلایی، مشتریان کم ارزش، مشتریان الماسی وفادار و مشتریان خاص.
The purpose of this research is to segment and determine the profile of customers using a self-organizing neural network algorithm (case study: customers of branches of Agricultural Bank of Ahvaz). The present research is included in the group of quantitative methods. The research method is based on the purpose of the applied type and the method of collecting descriptive (non-experimental) data - survey, and in terms of location, it is field research. The statistical population of this research is the customers of Ahvaz Agricultural Bank. Since the statistical population of this research is unlimited, the sample size is determined to be 384 people based on Cochran's unlimited population formula. The research tool is a questionnaire. In this research, content validity was done by asking the supervisor and other professors who have opinions on the subject area of this research. After applying these opinions on the questionnaire's content and solving the existing problems, Moore's questionnaire was approved. Data analysis was done by SOM neural network analysis method in MATLAB software. After the implementation of data analysis, the findings of the research identified five different categories of customers with different behavioral and demographic characteristics. These 5 segments are largely traditional and valuable customers, golden customers, low-value customers, loyal diamond customers, and special customers.
Abimbola, T., Trueman, M., Iglesias, O., Muhamad, R., Melewar, T. C., & Alwi, S. F. S. (2012). Segmentation and brand positioning for Islamic financial services. European Journal of Marketing 46(7/8), 900-921. https://doi.org/10.1108/03090561211230061
Aghaei, M. (2021). Market Segmentation in the Banking Industry Based on Customers’ Expected Benefits: A Study of Shahr Bank. Iranian Journal of Management Studies (IJMS), 14(3), 629-648. [In Persian]
Ahmadi, P., Azar, A., & Samsami, F. (2009). Drug market segmentation with neural networks approach (case study: drug market in Iran). Business Management, 2(4), 1-20. [In Persian]
Alfansi, L., & Sargeant, A. (2000). Market segmentation in the Indonesian banking sector:the relationship between demographics and desired customer benefits. Int. J. Bank Mark, 18(2), 64–74. https://doi.org/10.1108/02652320010322976
Alves Gomes, M., & Meisen, T. (2023). A review on customer segmentation methods for personalized customer targeting in e commerce use cases. Information Systems and e-Business Management, 21(1), 527–570.
Asiedu, E. (2016). A study of use and impact of market segmentation practices on bank performance:
with special reference to commercial banks in Colombia. Journal of Business and Financial Affairs, 5(162), 2167-2234. https://doi.org/10.4172/2167-0234.1000162
Baradaran, V., & Farkhi, Z. (2013). Customer segmentation in the banking industry using the developed RFMC model. Brand Management, 1(2), 135-154.
Brisoux, J. E., & Laroche, M. (2014). Strategic brand among fast food franchise: A test of two frameworks evaluations. Journal of Bussiness Research, 45(1), 221-233.
Cameron, F., Cornish, C., & Nelson, W. (2006). FSS2: A new methodology for segmenting consumers
for financial services. Journal of Financial Services Marketing, 10(3), 260-271. https://doi.org/10.1057/palgrave.fsm.4770191
Camilleri, M. A. (2018). Market Segmentation, Targeting and Positioning. Cham, Switzerland.
Chauhan, V., Yadav, R., & Choudhary, V. (2018). Analyzing the impact of consumer innovativeness and perceived risk in internet banking adoption: a study of Indian consumers. Int. J. Bank Market, 37(1), 323–339. https://doi.org/10.1108/IJBM-02-2018-0028
Currim, I. S. (2017). Using Segmentation Approaches for Better Prediction and Understanding from Consumer Mode Choice Models. Journal of Marketing Research, 18(2), 301-309.
Curry, B., & Moutinho, L. (2015). Neural Network in marketing: Modelling consumer Responses to Advertising Stimuli. European Journal of Marketing, 27(7), 5-20.
Darvishi, M., & Abbasi, N. (2022). Segmenting and determining the profile of Arvand car customers using self-organizing map model neural network algorithm. Journal of Marketing Management, 17(57), 136-137. [In Persian]
Dolnicar, S., & Leisch, F. (2013). Winter tourist segments in Austria: Indentifying stable vacation styles using bagged clustering techniques. Journal of Travel Research, 41(3), 281-290.
Farrokhi, S., & Timurpour, B. (2015). Identifying and classifying customers of Iran's banking system from the perspective of expectations and perceived value of banking services using data mining techniques. Modern Marketing Research, 1(20), 201-220.
Gordon, C. (1994). Neural Networks and Market Segmentation. Journal of Marketing Management, 10(1), 105-129.
Guillet, B. D., & Kucukusta, D. (2016). Spa market segmentation according to customer preference. International Journal of Contemporary Hospitality Management, 28(2), 418-434. https://doi.org/10.1108/IJCHM-07-2014-03745
Hunt, S. D., & Arnett, D. B. (2004). Market segmentation strategy, competitive advantage and public policy: Grounding segmentation strategy in resource-advantage theory. Australasian Marketing Journal (AMJ), 12(1), 7-25. https://doi.org/10.1016/S1441-3582(04)70083-X
Jaiswal, D., Kaushal, V., Deshmukh, A. K., Kant, R., & Kautish, P. (2023). What drives electric vehicles in an emerging market? Mark. Intell. Plan., 40(6), 738–754. https://doi.org/10.1108/MIP-11-2021-0406
Kamakura, W. A., & Wedel, M. (2000). Market segmentation: Conceptual and methodological foundations (2 ed.). Kluwer Academic Publishers.
Kaur, G., Sharma, R. D., & Mahajan, N. (2014). Segmentation of bank customers by loyalty and switching intentions. Vikalpa The Journal for Decision Makers, 39(4), 75–90.
Kotler, P., & Armstrong, G. (1992). Principles of marketing. . Prentice Hall.
Liu, J., Liao, X., Huang, W., & Liao, X. (2019). arket segmentation: A multiple criteria approach combining preference analysis and segmentation decision. M. Omega, 83, 1-13. https://doi.org/10.1016/j.omega.2018.01.008
Liu, Y., Ram, S., Lusch, R. F., & Brusco, M. (2010). Multicriterion market segmentation: A new model, implementation and evaluation. Marketing Science, 29(5), 880-894. https://doi.org/10.1287/mksc.1100.056
Mostafa, M. (2009). Shades of green: A psychographic segmentation of the green consumer in Kuwait using self-organizing maps. Expert Systems with Applications, 36(8), 11030-11038.
Mostafa, M. M., & El-Masry, A. A. (2013). Citizens as consumers: Profiling egovernment services users in Egypt via data mining techniques. International Journal of Information Management, 33(4), 627-641.
Raiter, O. (2021). Segmentation of Bank Consumers for Artificial Intelligence Marketing. International Journal of Contemporary Financial Issues, 1(1), 39–54.
Tagvi Fard, M. T., & Khajund, S. (2012). Cluster segmentation of bank customers (case study: Saderat Bank of Iran). Business Management Explorations, 5(9), 39-64.
Zheng, X., Wu, C., & He, S. (2021). Impacts of market segmentation on the over-capacity of the thermal electricity generation industry in China. J. Environ. Manag, 279, 111761. https://doi.org/10.1016/j.jenvman.2020.111761
