Business Intelligence with Social Media and Data Mining to Support Customer Satisfaction in Telecommunication Industry

In today business, meeting customer satisfaction is a must. Customer satisfaction is related with various factors such as: hope, loyalty, and complaint. Previously, surveying customer opinion in experiencing the products or services is a main tool to collect and analyze the data. Nowadays, social media applications replace survey as promising media to support and enhance customer satisfaction. Social media provides easily an accessible platform for users to share information. As a result to this, the social media has been pervasively used all over the world 24 hours a day; thus, it has generated unprecedented amounts of social data. Mining social media has its potential to extract actionable patterns that can be beneficial for business, users, and consumers. This paper reviews the basics of data mining and social media, introduces several techniques which promising to be chosen to mining social media, and illustrates some applications of data mining, specifically in telecommunication industry, to support customer satisfaction and maintain customer relationship in telecommunication industry.

[1]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[2]  Evangelos Grigoroudis,et al.  Customer satisfaction measurement in the private bank sector , 2001, Eur. J. Oper. Res..

[3]  Susan Oliver.  A model for the future of electronic commerce , 1997, Inf. Manag. Comput. Secur..

[4]  C. Fornell.  A National Customer Satisfaction Barometer: The Swedish Experience: , 1992 .

[5]  Duen-Ren Liu,et al.  Hybrid approaches to product recommendation based on customer lifetime value and purchase preferences , 2005, J. Syst. Softw..

[6]  R. Rust,et al.  Customer satisfaction, customer retention, and market share , 1993 .

[7]  Philip S. Yu,et al.  Fast algorithms for projected clustering , 1999, SIGMOD '99.

[8]  Irena Ograjenšek.  Use of Customer Data Analysis in Continuous Quality Improvement of Service Processes , 2003 .

[9]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[10]  Ashutosh Tiwari,et al.  Computer assisted customer churn management: State-of-the-art and future trends , 2007, Comput. Oper. Res..

[11]  Shourya Roy,et al.  Text classification, business intelligence, and interactivity: automating C-Sat analysis for services industry , 2008, KDD.

[13]  Huaizu Li,et al.  The impact of service quality, satisfaction, value and switching barrier on customer loyalty in Chinese mobile telecommunication industry , 2005, Proceedings of ICSSSM '05. 2005 International Conference on Services Systems and Services Management, 2005..