A Fuzzy Inference System Proposal for Selecting Marketing Strategy

Abstract

Changes in customer demands and advances in technology have led to the emergence of various marketing sectors. As a result of this, the expectation of each generation from brands or companies has become different. In addition, customers have begun to consider alternatives before purchasing a product, taking into account the various features that best meet their needs. Due to changing conditions, companies have forced to consider various criteria when determining their target markets and the types of products they introduce into the market. In this study, a fuzzy inference system has been proposed to provide support for the decision-making process related to the marketing strategy of the firm. When the proposed system was evaluated by experts, it was seen that satisfactory results were achieved.

Keywords

• Fuzzy inference system,Fuzzy logic,Marketing strategy,Risk level

Pazarlama Stratejisi Seçmek için Bulanık Çıkarım Sistemi Önerisi

Öz

Müşteri taleplerindeki değişmeler ve teknolojideki gelişmeler, çeşitli pazarlama sektörlerinin ortaya çıkmasına neden olmuştur. Bunun sonucu olarak, her neslin markalardan veya şirketlerden beklentileri farklılaşmıştır. Ek olarak müşteriler, bir ürünü satın almadan önce gereksinimlerini en üst düzeyde karşılayan çeşitli özellikleri göz önünde bulundurarak alternatifleri değerlendirmeye başlamıştır. Değişen koşullar nedeniyle şirketler, hedef pazarlarını ve pazara sundukları ürün türlerini belirlerken çeşitli kriterleri göz önünde bulundurmak zorunda kalmaktadır. Bu çalışmada, firmanın pazarlama stratejisiyle ilgili karar verme sürecine destek sağlamak için bulanık bir çıkarım sistemi önerilmiştir.  Önerilen sistem uzmanlar tarafından değerlendirildiğinde tatmin edici sonuçlara ulaşıldığı görülmüştür.

Anahtar Kelimeler

• Bulanık çıkarım sistemi,Bulanık mantık,Pazarlama stratejisi,Risk seviyesi

References

1. Aghdaie, M. H., and Alimardani, M. (2015). Target market selection based on market segment evaluation: a multiple attribute decision making approach. International Journal of Operational Research, 24(3), 262-278.
2. Akın, O., and Oruç, O. (2012). A prey predator model with fuzzy initial values. Hacettepe Journal of Mathematics and Statistics, 41(3), 387-395.
3. Bang, V. V., and Joshi, S. L. (2010). Market expansion strategy–performance relationship. Journal of Strategic Marketing, 18(1), 57-75.
4. Casabayó, M., Agell, N., and Sánchez-Hernández, G. (2015). Improved market segmentation by fuzzifying crisp clusters: A case study of the energy market in Spain. Expert Systems with Applications, 42(3), 1637-1643.
5. Chen, B. C. (2017). Study on establishment of product life cycle model of cultural creative product industrialization. International Conference on Applied System Innovation (ICASI) (pp. 1259-1262), Sapporo, Japan.
6. Chen, C. T., Lin, C. T., and Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International journal of production economics, 102(2), 289-301.
7. Dalgıç, A. (2017). A RFID-Based Approach in the Effective Management of Stocks and an Implementation in FNSS Through Grouping Algorithm. Master of Science Thesis. Natural and Applied Sciences, Kırıkkale University, Kırıkkale.
8. Durmaz, Y., and İlhan, A. (2015). Growth Strategies in Businesses and A Theoretical Approach. International Journal of Business and Management, 10(4), 210.

9. Fırat, A., Türker, G. O., and Metin, I. (2014). Specification of Target Market In Small And Medium Scale Accommodation Businesses: A Study On Boutique Hotels Operating In City Of Muğla. International journal of academic research, 6(1), 88-82.
10. Forrest, J. Y. L., Buttermore, J., and Wajda, T. A. (2017). At Nash equilibrium when new market competitions appear?. Kybernetes, 46(2), 256-271.
11. Gaston-Breton, C., and Martín Martín, O. (2011). International market selection and segmentation: a two-stage model. International Marketing Review, 28(3), 267-290.
12. Guillaume, S. (2001). Designing fuzzy inference systems from data: An interpretability-oriented review. IEEE transactions on fuzzy systems, 9(3), 426-443.
13. Guillaume, S., and Charnomordic, B. (2012). Fuzzy inference systems: An integrated modeling environment for collaboration between expert knowledge and data using FisPro. Expert Systems with Applications, 39(10), 8744-8755.
14. Hamal, S. (2011). Fuzzy Analytic Network Process for Selecting Optimal Marketing Strategy. Master of Science Thesis, Institute of Pure and Applied Sciences, Marmara University, İstanbul.
15. Hayajneh, M. T., Radaideh, S. M., and Smadi, I. A. (2006). Fuzzy logic controller for overhead cranes. Engineering Computations, 23(1), 84-98.
16. Helfat, C. E., and Lieberman, M. B. (2002). The birth of capabilities: market entry and the importance of pre‐history. Industrial and corporate change, 11(4), 725-760.
17. Huang, J. J., Tzeng, G. H., and Ong, C. S. (2007). Marketing segmentation using support vector clustering. Expert systems with applications, 32(2), 313-317.
18. Jacques, P., and Hollander, A. (2017). Does the Decline Phase of the Product Life Cycle Ends Up in Death?. 11th Asia-Pacific Conference on Global Business, Economics, Finance and Business Management.
19. Kadaifci, C., Asan, U., Serdarasan, S., and Arican, U. (2019). A new rule-based integrated decision making approach to container transshipment terminal selection. Maritime Policy & Management, 46(2), 237-256.
20. Keshwani, D. R., Jones, D. D., Meyer, G. E., and Brand, R. M. (2008). Rule-based Mamdani-type fuzzy modeling of skin permeability. Applied Soft Computing, 8(1), 285-294.
21. Kokoç, M., Aktepe, A., and Ersöz, S. (2017). Determination of Significance Level of the Association Rules in Data Mining with Fuzzy Ranking. 5th International Conference on Advanced Technology & Sciences, 178-182.
22. Kotler, P., and Armstrong, G. (2010). Principles of marketing. Pearson education.
23. Kumar, A., Bansal, A., and Babbar, N. (2013). Fully fuzzy linear systems of triangular fuzzy numbers (a, b, c). International Journal of Intelligent Computing and Cybernetics, 6(1), 21-44.
24. Kumar, A., Singh, P., Kaur, A., and Kaur, P. (2010). RM approach for ranking of generalized trapezoidal fuzzy numbers. Fuzzy Information and Engineering, 2(1), 37-47.
25. Laroche, M. (2010). Advances in internet consumer behavior and marketing strategy: Introduction to the special issue. Journal of Business Research, 63(9-10), 1015-1017.
26. Lin, C. T., and Wu, C. S. (2008). Selecting a marketing strategy for private hotels in Taiwan using the analytic hierarchy process. The Service Industries Journal, 28(8), 1077-1091.
27. Liu, H. H., and Ong, C. S. (2008). Variable selection in clustering for marketing segmentation using genetic algorithms. Expert Systems with Applications, 34(1), 502-510.
28. Madera, Q., Castillo, O., García-Valdez, M., and Mancilla, A. (2017). A method based on interactive evolutionary computation and fuzzy logic for increasing the effectiveness of advertising campaigns. Information Sciences, 414, 175-186.
29. Mamdani, E. H., and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13.
30. Marketing News. (2004). AMA adopts new definition of marketing. Marketing News, 38, 1.
31. Mendel, J. M. (1995). Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83(3), 345-377.
32. Mesran, M., Syahrizal, M., Suginam, S., Kurniasih, N., Gs, A. D., Ahmar, A. S., and Rahim, R. (2018). Expert system for disease risk based on lifestyle with Fuzzy Mamdani. International Journal of Engineering and Technology, 7(2.3), 88-91.
33. Mishina, Y., Pollock, T. G., and Porac, J. F. (2004). Are more resources always better for growth? Resource stickiness in market and product expansion. Strategic Management Journal, 25(12), 1179-1197.
34. Mohaghar, A., Fathi, M. R., Zarchi, M. K., and Omidian, A. (2012). A combined VIKOR-fuzzy AHP approach to marketing strategy selection. Business Management and Strategy, 3(1), 13-27.
35. Mucuk, I. Marketing Principle, Turkmen Bookstore, Istanbul, (2010).
36. Muley, P., and Joshi, A. (2015). Application of data mining techniques for customer segmentation in real time business intelligence. International Journal of Innovative Research in Advanced Engineering, 2(4), 106-109.
37. Murray, P. W., Agard, B., and Barajas, M. A. (2017). Market segmentation through data mining: A method to extract behaviors from a noisy data set. Computers & Industrial Engineering, 109, 233-252.
38. Nilsson, L., Höjman, V., and Elfqvist, P. (2015). Customers Sustainability Demand: A comparison between convenience goods and shopping goods. Bachelor’s thesis. Jönköping University, Jönköping International Business School.
39. Papadopoulos, A., Kalivas, D., and Hatzichristos, T. (2011). Decision support system for nitrogen fertilization using fuzzy theory. Computers and Electronics in Agriculture, 78(2), 130-139.
40. Racz-Akacosi, A. (2017). Differentiation between Differentiated and Concentrated type of Marketing Strategy. Small.
41. Ray, N. (Ed.). (2015). Emerging innovative marketing strategies in the tourism industry. IGI Global.
42. Rodrigues, L., Maccari, E. A., and Lenzi, F. C. (2012). Innovation strategy for business to business market penetration. International Business Research, 5(2), 137.
43. Rodriguez, R. M., Martinez, L. & Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109-119.
44. Rodriguez, R. M., Martinez, L. and Herrera, F. (2013). A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Information Sciences, 241, 28-42.
45. Ruziye, C. O. P., Candaş, N., and Akşit, N. (2012) The Importance of Segmentation, Target Market and Positioning in Strategic Marketing Decisions: A Qualitative Study on Hotels in Bolu. Abant Izzet Baysal University Journal of Social Sciences, 24(24), 35-52.
46. Samli, A. C. (2012). International consumer behavior in the 21st century: impact on marketing strategy development. Springer Science & Business Media.
47. Slater, S. F., Hult, G. T. M., and Olson, E. M. (2010). Factors influencing the relative importance of marketing strategy creativity and marketing strategy implementation effectiveness. Industrial Marketing Management, 39(4), 551-559.
48. Takagi, T., and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, (1), 116-132.
49. Toksarı, M. (2007). Determination of the target market in the Aegean Region for the furniture sector using the Analytical Hierarchy process approach. Management and Economics: Celal Bayar University Journal of Economics and Administrative Sciences, 14 (1), 171-180.
50. Varadarajan, R. (2010). Strategic marketing and marketing strategy: domain, definition, fundamental issues and foundational premises. Journal of the Academy of Marketing Science, 38(2), 119-140.
51. Wong, H. K., and Ellis, P. D. (2007). Is market orientation affected by the product life cycle?. Journal of World Business, 42(2), 145-156.
52. Wu, C. S., Lin, C. T., and Lee, C. (2010). Optimal marketing strategy: A decision-making with ANP and TOPSIS. International Journal of Production Economics, 127(1), 190-196.
53. Yavuz, U., Hasiloglu, A. S., Kaya, M. D., Karcioglu, R., and Ersoz, S. (2005). Developing a marketing decision model using a knowledge-based system. Knowledge-Based Systems, 18(2-3), 125-129.
54. Yurdakul, M., and Yıldırım, E. (2014). Determination of the most appropriate marketing strategy with analytical network process method. Dumlupınar University Journal of Social Science, 211-226.
55. Zadeh, L. A. (1965). Information and control. Fuzzy sets, 8(3), 338-353.