An Integrated Entropy/VIKOR Model for Customer Clustering in Targeted Marketing Model Design (Case Study: IoT Technology Services Companies)

Subject Areas : Financial Accounting

Hossein Teimouri ¹ , Jalil Gharibi ² , Ali Hossein Zadeh ³ , Alireza Pooya ⁴

1 - Department of Management, Torbate Heidarieh Branch, Islamic Azad University, Torbate Heidarieh, Iran
2 - Department of Management, Torbate Heidarieh Branch, Islamic Azad University, Torbate Heidarieh, Iran
3 - Department of Management, Torbate Heidarieh Branch, Islamic Azad University, Torbate Heidarieh, Iran
4 - Department of Management, Torbate Heidarieh Branch, Islamic Azad University, Torbate Heidarieh, Iran|Department of Management, Ferdowsi University, Mashahd, Iran

Received: 2020-03-11 Accepted : 2020-06-10 Published : 2021-10-01

Keywords: Clustering Clients, Internet of Things, Targeted Marketing, Marketing,

Abstract :

Nowadays, marketing researchers are constantly striving to identify consumer behavior and therefore to find appropriate solutions for better and more effective sales and increase market share. In this regard, the purpose of the present study is the role of customer clustering in designing a targeted marketing model. The research method is applied and exploratory. The statistical population of the study was in the qualitative part of sales and marketing managers of IoT companies who were selected by non-random sampling method and 15 people were interviewed. The quantitative part also included all the customers of the companies surveyed. Due to the unlimited population of Morgan Table 384 persons were selected as the sample size. Data gathering tool was interview and questionnaire, which were used to assess the validity of the questionnaire by the opinions of marketing experts and Cronbach's alpha reliability. Content analysis approach was used to analyze the data in the qualitative part and PLS2 software in the structural part. The results showed that the dimensions of the model in the four main clusters were communication factors, behavioral factors, individual factors, and economic factors. Model performance is very high performance.

References:

1. Tarokh, Mohammad Jafar;; Sharifian, Kobra, (2007), Application of Data Mining in Improving Customer Relationship, Scientific-Research Quarterly Journal of Industrial Management Studies, Volume 6, Number 17, Pages 153-181


2.  Rezaei, Farzin; Ghaybdoost, Hamed, (2016), A study of the financial performance of the banking industry using the Vikor method. Quarterly Journal of Development and Transformation Management (Special Letter): 33-43.


3.  Abyssinian Mania, Akbar et al., (2014). "Study of the role of relational marketing, sensory and supportive marketing in customer loyalty", Marketing Management, No. 24-56-78.


4. Tone, K., Toloo, M., Izadikhah, M., A modified slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research, 2020, 287 (2), P. 560-571, Doi: 10.1016/j.ejor.2020.04.019.


5. Ali Heydari Buiki, Tahereh; Khademi Zare, Hassan, (2015), Development of Data Envelopment Analysis Method for Crediting Credit Customers of Banks, Journal of Modeling in Engineering, Vol. 13, No. 41, pp. 59-74


6.  Gholami, Hossein (1398). Customer classification based on factors influencing their willingness to buy. Information Technology Management Studies.5 (20) .1-20.


7.  Gholamian, Seyed Akbar, (1398). Using the data mining approach in customer clustering (Case study: Toloo Pakhsh Aftab Company). National Conference on Applied Research in Industrial Management and Engineering, Tehran, Rahnama Non-Profit Higher Education Institute,


8. Agah, M., Malekpoor, H., Bagheri, A., Investigating the Effect of Financial Constraints and Different Levels of Agency Cost on Investment Efficiency. Advances in Mathematical Finance and Applications, 2017, 2(4), P. 31-47. Doi: 10.22034/amfa.2017.536264


9. Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic multiplicative DEA model for measuring the efficiency and ranking of DMUs under VRS technology, Indian Journal of Science and Technology, 2014, 7 (11), P. 1765–1773. Doi: 10.17485/ijst/2014/v7i11.19


10.  Ghanbari, Ahmad; Ali Haroonabadi and Mahmoud Ghodratian, (2015) Identifying reputable customers in e-banking system by combining clustering and classification techniques and RFM characteristics, 3rd National Conference on Knowledge and Technology of Electrical, Computer and Mechanical Engineering of Iran, Tehran, Organizing Institute Sam Iranian Iranian Science and Technology-Based Development Conferences,


11.  Dear, Javad; Qanatian, Alireza, (2016), Evaluation of performance of South Fars Power Generation Management Company using data envelopment analysis in the presence of undesirable databases and outputs. New Research in Mathematics 2 (6), 49-67.


12. Izadikhah, M., Farzipoor Saen, R., Ranking sustainable suppliers by context-dependent data envelopment analysis. Ann Oper Res, 2020, 293, P. 607–637, Doi: 10.1007/s10479-019-03370-4


13.  Mashhadi, Massoud, (1389). Marketing Management, Tehran, Pouran Pajoohesh Publications


14. Homayoun Far, Mehdi; Goodarzvand Chegini, Mehrdad; Daneshvar, Amir, (1397), Prioritizing Green Supply Chain Suppliers Using Fuzzy MCDM Combined Approach. Operations research in its applications. 15 (2): 41-61


15. Izadikhah, Mohammad; Shamsi, Mohadeseh, (1398), Credit Ranking of Bank Legal Customers Using Improved Russell Model (Case Study: Legal Customers of Arak National Bank). New Research in Mathematics, 5 (22), 111-126.


16. Amiri, Maghsoud; Hadi Nejad, Farhad; Malek Khoyan, Shiva, (2017), Evaluation and prioritization of suppliers with a combined entropy approach, hierarchical analysis process and modified pramity (Case study: Utab Company). Operations research in its applications. 14 (4): 1-20


17. Azar, Adel; Mahdavi Rad, Alireza; Musa Khani, Morteza, (2015), Designing a Combined Data Mining Model and Multi-Criteria Decision Making (Case Study: Iran Statistics Subsidies Database). Operations research in its applications. 12 (1): 95-111


18. Ahn , J., Woodcock N., Wilson M. (2006). “Managing the Change from Marketing Planning to Customer Relationship Management”. Lomg Range Planning, 29, 675-683.


19. Azadnia, A. H., Saman, M. Z. M., Wong, K. Y., & Hemdi, A. R. (2011). Integration model of Fuzzy C means clustering algorithm and TOPSIS Method for Customer Lifetime Value Assessment. In Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on (pp. 16-20). IEEE


20. Rezaei, N., Elmi, Z., Behavioral Finance Models and Behavioral Biases in Stock Price Forecasting. Advances in Mathematical Finance and Applications, 2018, 3(4), P. 67-82. Doi: 10.22034/amfa.2019.576127.1118


21. Ansari, A., & Riasi, A. (2016). Customer clustering using a combination of fuzzy C-means and genetic algorithms. International Journal of Business and Management, 11(7), 59-66


22. Bose, I., & Chen, X. (2015). Detecting the migration of mobile service customers using fuzzy clustering. Information & Management, 52(2), 227-238


23.  Chan, C., Chai, H., (2017), "Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer." Expert systems with applications 34,4: 2754-2762.


24. Fathian, M., Azhdari, E. (2017). Extracting Customer Behavior Pattern in a Telecom Company Using Temporal Fuzzy Clustering and Data Mining. Journal of Information Technology Management, 9(3), 549-570.


25. Hung, M., Gou S. (2017). “Estimating Customer Lifetime Value Based On RFM Analysis of Customer Purchase Behavior: Case Study, Procedia Computer Science”, Vol. 3,pp.57-63.


26.  Lee, A., (2019). Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222, 175–184.


27. Tleis, M., Callieris, R., & Roma, R. (2017). Segmenting the organic food market in Lebanon: an application of k-means cluster analysis. British Food Journal, 119(7), 1423-1441


28. Hiziroglu, A., & Senbas, U. D. (2016). An Application of Fuzzy Clustering to Customer Portfolio Analysis in Automotive Industry. International Journal of Fuzzy System Applications (IJFSA), 5(2), 13-25.


29. Gholami, S., Karimiankakolaki, M., Ghobeyshavi, H., (2015), Developing Marketing Strategies Based On Risk Management by Using Dematel Technique, Social Sciences, vol 4.No.1.


30. Karbasi Yazdi, H., Mohammadian, M., Effect of Profitability Indices on the Capital Structure of Listed Companies in Tehran Stock Exchange. Advances in Mathematical Finance and Applications, 2017, 2(3), P. 1-11. Doi: 10.22034/amfa.2017.533085


31. Opricovic, S., & Tzeng, G-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.


32. Shannon CE., (1948), A mathematical theory of communication. Bell SystTech J; 27:379–423