978-1-4799-6527-4/14/$31.00 ©2014 IEEE Social CRM using Web Mining Nyoman Karna, Iping Supriana, Ulfa Maulidevi Sekolah Teknik Elektro dan Informatika Institut Teknologi Bandung Bandung, Indonesia bogi@students.itb.ac.id, iping@stei.itb.ac.id, ulfa@stei.itb.ac.id Abstract—Traditional CRM (Customer Relationship Management) contains 3 modules, Marketing, Sales, and Support, which rely on the customer relationship and profiling information. While the information contained in those 3 modules is input by operator, it will be prudent to gather much more information from the Internet. We can find relationship between customers and find their profile from the Internet. This information can be used to enrich and direct the CRM to perform better in supporting the business objectives. Gathering information from the Internet means that we need Information Retrieval and Information Extraction that involve many sources from Internet, such as social media, net blog, and news. This research provides the model of data mining utilization in traditional CRM to become social CRM. This research contributes for CRM enhancement where customer centric application becomes automated. Keywords—CRM; customer relationship; customer profile; web mining; social media; semantic network I. REFERENCES [1] Ronald J. Brachman and Hector J. Levesque, Knowledge Representation and Reasoning, San Francisco: Morgan Kaufmann, 2004: 2-11. [2] John F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations, Boston: Course Technology, CENGAGE Learning, 2000: 11-29. [3] Emil Vassev and Mike Hinchey, “Knowledge Representation and Reasoning for Intelligent Software Systems,” IEEE Computer, Vol. 44, Issue 8, pp. 96-99, 2011 [4] Nhon V. Do, “Model for Knowledge Bases of Computational Objects,” IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 3, No 8, pp. 11-20, 2010. [5] Hu-Chen Liu, Qing-Lian Lin, Ling-Xiang Mao, Zhi-Ying Zhang. “Dynamic Adaptive Fuzzy Petri Nets for Knowledge Representation and Reasoning,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 43, no. 6, pp. 1399-1410, 2013. [6] Hu-Chen Liu, Long Liu, Qing-Lian Lin, Nan Liu, “Knowledge Acquisition and Representation Using Fuzzy Evidential Reasoning and Dynamic Adaptive Fuzzy Petri Nets,” IEEE Transactions on Cybernetics, Vol. 43, no. 3, pp. 1059-1072, 2013. [7] Robert Harrison and Christine W. Chan, “A Tool for Dynamic Knowledge Modeling,” 6th IEEE International Conference on Cognitive Informatics (ICCI'07), Lake Tahoo, pp. 513-521, 2007. [8] Nhon V. Do and Hien D. Nguyen, “A Knowledge Model about Relations and Application,” ISSDM 2012 : 6th International Conference on New Trends in Information Science, Service Science and Data Mining, Taipei, pp. 707-710, 2012. [9] Liu Xin, “A New Algorithm for Electronic Customer Relationship Management,” 7th International Conference on Computing and Convergence Technology (ICCCT), pp. 359-362, 2012. [10] Limei Zhang, “Data mining application in customer relationship management,” International Conference on Computer Application and System Modeling (ICCASM), pp. V14-171-V14-174, 2010. [11] Ming Ren, Zuoliang Chen, and Chuanlan Liu, “An Evolving Information System Based in Data Mining Knowledge to Support Customer Relationship Management,” IEEE Symposium on Advanced Management of Information for Globalized Enterprises (AMIGE), pp. 1- 5, 2008. [12] TMForum, Application Framework (TAM), Frameworx 13.5, http://www.tmforum.org, 2014