Customer oriented intelligent DSS based on two-phased clustering and integrated interval type-2 fuzzy AHP and hesitant fuzzy TOPSIS

ŞENVAR Ö., Akburak D., Yel N.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, cilt.39, sa.5, ss.6121-6143, 2020 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 5
  • Basım Tarihi: 2020
  • Doi Numarası: 10.3233/jifs-189084
  • Dergi Adı: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.6121-6143
  • Anahtar Kelimeler: Intelligent decision support system (IDSS), customer experience journey, clustering, fuzzy multi criteria decision making (MCDM), interval type-2 fuzzy AHP, hesitant fuzzy TOPSIS, MULTICRITERIA DECISION-MAKING, BIG DATA, SEGMENTATION, EXPERIENCE, MANAGEMENT, ANALYTICS, SYSTEM
  • Marmara Üniversitesi Adresli: Evet

Özet

Firms need to integrate multiple business functions in order to acquire, analyze, model, and evaluate information necessary for better understanding customer behaviors and making data-driven decisions to enhance the customer experience journey. This study proposes a customer oriented intelligent decision support system (IDSS) to ultimately improve the customer experience journey. Besides, a real application study is handled for a multinational company located in Turkey, considering its abrasives product sales for years of 2017 and 2018. For the data utilized in application study, the proposed methodology is constructed for customer segmentation to develop appropriate data-driven marketing strategies for customers with similar values, preferences and other factors for creating customer-centric organizations. In this regard; firstly two-phased clustering process, which involves the hierarchical multivariate average linkage clustering algorithm and partitional k-means clustering algorithm, is used to present the number of clusters on the basis of three variables (expenditure, transaction and unit cost) and then to assign the customers to the related clusters (VIP, Platinum, Gold and Bronze), respectively. Secondly, the performances of company's departments are ranked according to the preferences of customers from each segment considering 4Ps marketing mix concept via integrated methodology of interval type-2 Fuzzy AHP and hesitant fuzzy TOPSIS.