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    Classification of Customer Demands by Organizational Workflows
    (IEEE, 2018) Arslan, Halil; Kaynar, Oguz; Sahin, Sumeyye
    Corporate firms that aim to be permanent in the long term should not lose existing customers and need to win new customers. The increase in the number of firms offering similar services also increases the alternatives for customers. For this reason, there is no guarantee of long-term working with a customer. It has become compulsory to manage customer demands for companies that are in a highly competitive environment. In order to be able to sustain customer loyalty in the long term, they should better recognize them and provide quick returns to their demands. Firms are using help desk applications to manage these demands. Help desk applications are systems that aim to provide information and support to customers or end users about firms' services. In this study, customer demands were analyzed using text mining and machine learning algorithms and classified according to organizational workflow. The data sets used in the study and customer demands were obtained from help desk belonging to Detaysoft which offers live support to over 300 customers.
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    Classification of Customer Demands by Using Doc2Vec Feaure Extraction Method
    (IEEE, 2019) Arslan, Halil; Kaynar, Oguz; Sahin, Sumeyye
    Companies provide after-service communication with their customers through help desk systems they provide in call centers and websites. In companies with large networks, the amount of data collected by using these communication tools is increasing day by day.The separation of these collected requests and feedbacks to be transferred to the relevant units has become a very time consuming process. The prolongation of this process in customer-oriented companies can lead to customer loss.Therefore, it has importance to transfer, evaluate and return requests from such firms. In this study, firstly, two different models were used to obtain the features from the demands of customers. The features obtained were classified by multi-layer artificial neural network and it was provided the related demand was transferred to the related unit. Thus, the usability of the Doc2Vec model, which can be used as an alternative to the classic word bag, is examined in Turkish text classification studies.