Browsing by Author "Yel, Necla"
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Publication Metadata only Anticipation for Customer Analytics(2019-11) Şenvar, Özlem; Yel, Necla; AKBURAK, DİLEK; 275470; 193864In the digitalization era, enterprises are required to proactive customer relationships via analytical techniques to execute customer treatment strategies based on predictive analytics drawing on a wealth of data about customer behaviors. Nowadays, predictive analytics with automatic decision making concepts are integrated for better understanding preferences of customers, segmenting customers, promoting right products to right customers, improving customer experience, and driving revenue. Customer value anticipation becomes inevitable for understanding customer requirements and expectations. Anticipation can provide capacities in shaping customer perceptions and expectations to make sense of novelty by enlarging and enhancing the analytical and operational approaches to incorporate decision making. In this regard, customer analytics is handled within the perspective of the process by which data from customer behavior utilized to make key business decisions through market segmentation and predictive analytics in order to maximize organizational throughput and maintain long lasting relationships with customers. From this standpoint, this study aims to provide an overview for customer analytics considering anticipation, customer experience and big data analytics involving strategic decision making processes for customer loyalty and relationships. Moreover, customer experience quality is discussed for anticipating customer expectation.Publication Metadata only Customer Oriented Intelligent DSS Based on Two-Phased Clustering and Integrated Interval Type-2 fuzzy AHP and Hesitant Fuzzy TOPSIS(IOS Press BV, 2020) Şenvar, Özlem; AKBURAK, DİLEK; Yel, NeclaFirms 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.