Please use this identifier to cite or link to this item: http://bspace.buid.ac.ae/handle/1234/1191
Title: Using Text Mining and Cluster Analysis to Improve Customers Complaints System
Authors: HASAN, SALMA
Keywords: text mining
customers complaints
customer relationship management
customer’s satisfaction
retention
cluster analysis
Issue Date: Apr-2018
Publisher: The British University in Dubai (BUiD)
Abstract: The goal of Customer relationship management in all organizations, regardless of the type of industry and service provided, is to increase customer’s satisfaction and achieve retention. Customers are sharing their opinions about products and expectations by multiple communication points, the service centers or via social media platforms. These opinions and feedback shared are valuable data to enlighten organizations about the issues and weakness points requires improvement or development. The aim of this study is to use text mining and clustering methods to improve customer’s complaints system .To this end, the raised research questions to be answered are as follow: Does the generated clusters shows clear patterns that can help to indicate the complaint category? Doses the current complaints subjects matches the complaints contents? Is there a need of creating new complaints Categories or even merging some of the complaints? The study research question answered through customer’s complaints analysis after applying text mining processes and K-means clustering technique. Based on the generated clusters analysis, the results indicated clear patterns that refers to specific complaints category and some clusters had multiple categories in one cluster. Some of the categories patterns are having similarity in keywords so it can merged together and the duplicated can be removed. The results of the complaints analysis using text mining and clustering techniques will contribute on enhancement of the quality of service provided and weakness points to focus on.
URI: http://bspace.buid.ac.ae/handle/1234/1191
Appears in Collections:Dissertations for IT Management (ITM)

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