Big Data ve SPACE Matris Yöntemlerine Dayalı Akıllı Stratejiler Belirlenmesi (FASE Model)

Şirket yöneticileri için en önemli sorulardan biri, şirketleri için akıllı bir stratejiyi nasıl seçtikleridir. Bu soruyu cevaplamak için yöneticiler, kurumun geleceği üzerinde etkisi olan bazı boyutları göz önünde bulundurmalı ve ardından şirketleri için uygun bir strateji seçmelidir. Bu makale, stratejik pozisyonu değerlendirmek ve Büyük Veri teknikleri ve UZAY matrisini temel alan akıllı bir strateji seçmek için yeni bir model (FASE modeli) sunmaktadır. Piyasadaki en iyi pozisyonu elde etmek için FASE modeli, en iyi stratejiyi seçmeyi kolaylaştırır: saldırgan, muhafazakâr, savunmacı ve rekabetçi stratejiler. FASE modeli, Fuzzy-Cmeans, Apriori birlik kural uyarıcısı, UZAY matrisi olmak üzere üç ana işlemden oluşur. Fuzzy-Cmeans algoritması, müşterileri RFM değerlerine ve davranışsal puanlamaya dayalı olarak kümelemek için kullanılır. Kümelenmenin sonuçları daha sonra Apriori birlik kural uyarıcısı kullanılarak müşterilerin özelliklerine göre şekillendilir. Stratejik pozisyonu değerlendirmek ve akıllı bir strateji seçmek için UZAY matrisi kullanılır. FASE modelini daha iyi anlamak için, bir bankacılık vakası seçildi ve bunun üzerine FASE modeli uygulandı.

Selecting Smart Strategies Based on Big Data Techniques and SPACE Matrix (FASE model)

One of the most important questions for managers of corporations is how they select a smart strategy for their corporations. To answer this question, Managers should consider some dimensions witch impact on the future of the corporation, and then they select a suitable strategy for their corporations. This paper presents a novel model (FASE model) for evaluating strategic position and choosing smart strategy based on Big Data techniques and SPACE matrix. In order to achieve the best position in the market, FASE model facilitates selecting the best strategy among: aggressive, conservative, defensive, and competitive strategies. FASE model consists of three main processes namely Fuzzy-Cmeans, Apriori association rule inducer, SPACE matrix. Fuzzy-Cmeans algorithm is used for clustering customers based on RFM values and behavioral scoring. The results of the clustering were then profiled on customers’ attributes using Apriori association rule inducer. A SPACE matrix was used to evaluate the strategic position and to choose smart strategy. To get a better understanding of the FASE model, the banking case has been selected and FASE model is applied over that.

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  • Fazlollahtabar H. and Abbasi A., Applying Integrated Strategic Planning and RADAR Technique to Find Optimal Course Delivery Policy in a Virtual Learning System. In: The 3rd International Conference on Virtual Learning, ICVL., (2008) 169–176.
  • Chen S-JJ., Hwang, C. L./Beckmann MJ and Krelle W., Fuzzy Multiple Attribute Decision Making: Methods and Applications. New York; Springer-Verlag New York. Inc. doi:10.1007/978-3-642-46768-4.
  • Kangas J., Pesonen M., Kurttila M. and Kajanus M., A’ WOT : Integrating The AHP With SWOT Analysis. ISAHP, (2001) 189–198.
  • Paiva E.L., Roth A.V., Fensterseifer J.E., Organizational Knowledge and The Manufacturing Strategy Process: A Resource-Based View Analysis, Journal of Operations Management, 26-1 (2008) 115–132.
  • Yüksel İ., Dagdeviren M., Using The Analytic Network Process (ANP) in a SWOT Analysis - A Case Study for a Textile Firm, Inf. Sci. (Ny), 177-16 (2007) 3364–3382.
  • Aslan I., Çınar O. and Özen Ü., Developing Strategies for The Future of Healthcare in Turkey by Benchmarking and SWOT Analysis. Procedia – Soc. Behav. Sci., 150 (2014) 230–240.
  • Law J., Managing Change and Innovation in Public Service Organisations - Edited by Stephen P. Osborne and Kerry Brown, Public. Adm., 84 (2006) 794–796.
  • Basu R., Tools for analysis-Pestle analysis in implementing quality: a practical guide to tools and techniques. Fisrt. Thomson Learning: London, (2004) 98-100
  • Zalengera C., Blanchard R.E., Eames P.C., et al. Overview of the Malawi energy situation and A PESTLE analysis for sustainable development of renewable energy, Renew. Sustain. Energy Rev., 38 (2014) 335–347.
  • Gürbüz T., A Modified Strategic Position and Action Evaluation ( SPACE ) Matrix Method, International MultiConference of Engineers and Computer Scientists, (2013) 13–16.
  • Shen L., Zhou J., Skitmore M. and Xia B., Application of a Hybrid Entropy–McKinsey Matrix Method in Evaluating Sustainable Urbanization: A China Case Study, Cities, 42 (2015) 186–194.
  • Decuseara N.R., Using The General Electric / Mckinsey Matrix In The Process Of Selecting The Central And East European Markets, Manag. Strateg., 19 (2013) 59–66.
  • Radder L. and Louw L., The SPACE matrix: A tool for calibrating competition, Long Range Plann, 31-4 (1998) 549–559.
  • Hughes A., Strategic Database Marketing. 4th ed. McGraw-Hill Education, (2012) 598
  • Stone B. and Jacobs R., Successful Direct Marketing Methods. McGraw-Hill, 2001.
  • Chuang H.-M. and Shen C., A Study on The Applications of Data Mining Techniques to Enhance Customer Lifetime Value - Based on The Department Store Industry, 2008 International Conference on Machine Learning and Cybernetics. IEEE, (2008) 168–173.
  • Farajian M.A. and Mohammadi S., Mining the Banking Customer Behavior Clustering and Association Rules Methods Using Clustering and Association Rules Methods, Int. Journal Ind. Eng. Prod. Res., 21 (2010) 239–245.
  • Wei-Jiang L., Shu-Yong D., Xue Y., Xiao-Feng W., Determination of Customer Value Measurement Model RFM Index Weights, African Jour. Bus. Manag., 15 (2011) 5567–5572.
  • Yeh I.-C., Yang K.-J. and Ting T.-M., Knowledge Discovery on RFM Model Using Bernoulli Sequence, Expert Syst. Appl., 36 (2009) 5866–5871.
  • Soeini R. and Fathalizade E., Customer Segmentation Based on Modified RFM Model in The Insurance Industry, Int. Proc. Comput. Sci. Inf. Tech. 25 (2012) 101–104.
  • Hu Y.H. and Yeh T.W., Discovering Valuable Frequent Patterns Based on RFM Analysis Without Customer Identification Information, Knowledge-Based Syst., 61 (2014) 76–88.
  • Malhotra R. and Malhotra D., Evaluating Consumer Loans Using Neural Networks, Omega, 31 (2003) 83–96.
  • Levis A.A. and Papageorgiou L.G., Customer Demand Forecasting Via Support Vector Regression Analysis, Chem. Eng. Res. Des., 83 (2005) 1009–1018.
  • Zheng T., Zhu D., Wang X. and Yu B., Panel Data Clustering and Its Application to Discount Rate of B Stock in China. In 2009 Second International Conference on Information and Computing Science, IEEE, (2009), 163–166.
  • Nie G., Chen Y., Zhang L., and Guo Y., Credit Card Customer Analysis Based on Panel Data Clustering, Procedia Comput. Sci., 1 (2010) 2489–2497.
  • Baesens B., Viaene S., Van den Poel D., Vanthienen, J. and Dedene G., Bayesian Neural Network Learning for Repeat Purchase Modelling in Direct Marketing, Eur. Jour. Oper. Res., 138 (2002) 191–211.
  • Dasgupta C.G., Dispensa G.S., Ghose S., Comparing The Predictive Performance of a Neural Network Model with Some Traditional Market Response Models, Int. Jour. Forecast, 10 (1994) 235–244.
  • Davies F., Moutinho L., Curry B., ATM User Attitudes: a Neural Network Analysis, Mark. Intell. Plan., 14 (1996) 26–32.
  • Hsieh N.C., An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers, Expert Syst. Appl., 27 (2004) 623–633.
  • Chan C.-C.H., Intelligent Spider for Information Retrieval to Support Mining-Based Price Prediction for Online Auctioning, Expert Syst. Appl., 34 (2008) 347–356.
  • Wang Y., Ma X., Lao Y. and Wang Y., A Fuzzy-Based Customer Clustering Approach with Hierarchical Structure for Logistics Network Optimization. Expert Syst. Appl., 41 (2014) 521–534.
  • Bora D.J., A Comparative Study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm, IJCTT, 10 (2014) 108–113.
  • Wong K.W., Data Mining Using Fuzzy Theory for Customer Relationship Management, WAWISR, 2001.
  • Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Springer Science & Business Media, 2013.
  • Calders T, Dexters N, Gillis J.J.M. and Goethals B., Mining Frequent Itemsets in A Stream, Inf. Syst., 39 (2014) 233–255.
  • Tan P.-N., Steinbach M. and Kumar V., Introduction to Data Mining, Boston (2006) (First Edition).
  • Vimieiro R. and Moscato P., A New Method for Mining Disjunctive Emerging Patterns in High-Dimensional Datasets Using Hypergraphs, Inf. Syst., 40 (2014) 1–10.
  • Agrawal R., Imielinski T. and Swami A., Mining Association Rules Between Sets of Items in Large Databases, SIGMOD Conference, 22 (1993) 207-216.
  • Agrawal R. and Srikant R., Fast Algorithms for Mining Association Rules in Large Databases, Ailamaki, (1994) 487–499.
  • Borocki J., Process Of Applying Modified Space Model For Defining Company ’ s Strategy, Appl. Modif. Sp. Model Defin. Co., 2 (2011) 61–68.
  • Mailvahanan R., Performance Evaluation of Printed Circuit Board Manufacturing Maquiladoras: A Return on Investment Approach, J. Oper. Manag., 9 (1990) 437–438.
  • Pizzica A.J. and Rist M., Financial Ratios for Executives: How to Assess Company Strength (2015)
  • Menezes M.B.C., Kim S. and Huang R., Return-on-Investment (ROI) Criteria for Network Design, Eur. Jour. Oper. Res., 245 (2015) 100–108.
Cumhuriyet Science Journal-Cover
  • ISSN: 2587-2680
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2002
  • Yayıncı: SİVAS CUMHURİYET ÜNİVERSİTESİ > FEN FAKÜLTESİ