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Reseach Article

Automated Customer Query Resolver using Data Mining

by Ashwini Jadhav, Parth Wadekar, Bhakti Wani, Nikita Joshi, Parth Sagar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 13
Year of Publication: 2018
Authors: Ashwini Jadhav, Parth Wadekar, Bhakti Wani, Nikita Joshi, Parth Sagar
10.5120/ijca2018916173

Ashwini Jadhav, Parth Wadekar, Bhakti Wani, Nikita Joshi, Parth Sagar . Automated Customer Query Resolver using Data Mining. International Journal of Computer Applications. 179, 13 ( Jan 2018), 24-27. DOI=10.5120/ijca2018916173

@article{ 10.5120/ijca2018916173,
author = { Ashwini Jadhav, Parth Wadekar, Bhakti Wani, Nikita Joshi, Parth Sagar },
title = { Automated Customer Query Resolver using Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2018 },
volume = { 179 },
number = { 13 },
month = { Jan },
year = { 2018 },
issn = { 0975-8887 },
pages = { 24-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number13/28861-2018916173/ },
doi = { 10.5120/ijca2018916173 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:16.570081+05:30
%A Ashwini Jadhav
%A Parth Wadekar
%A Bhakti Wani
%A Nikita Joshi
%A Parth Sagar
%T Automated Customer Query Resolver using Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 13
%P 24-27
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In an enterprise service centre, the biggest problem is to provide accurate information to customers and solve their queries. In this case ,financial as well as human resources are consumed to a greater extent. In order to reduce this problem, there should be an efficient solution. There are some existing technologies which are used by modern enterprise centres. In an enterprise service centre, when the customer places his query, the frequently asked questions are displayed first. If the customer is not satisfied with the solution or if the required content is not available, then the call will be transferred to the enterprise service centre. As the call is placed, the human interaction between the customer and the enterprise service centre will be substantially increased. In this paper, we propose a system which reduces human interaction and provides automation for resolving queries. For this purpose we use the concept of enterprise mobility. Mobility provides exciting opportunities to interact with your customers, partners and suppliers, empower your employees and connect things to your business.

References
  1. Tang, Min, Bryan Pellom, and Kadri Hacioglu. ”Call-type classification and unsupervised training for the call center domain.” Automatic Speech Recognition and Understanding, 2003. ASRU’03. 2003 IEEE Workshop on. IEEE.
  2. Bawakid, Abdullah, and Mourad Oussalah. ”A semantic-based text clas- sification system.” Cybernetic Intelligent Systems (CIS), 2010 IEEE 9th International Conference on. IEEE.
  3. Liu, Xu-Ying, Jianxin Wu, and Zhi-Hua Zhou. ”Exploratory undersam- pling for class-imbalance learning.” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 39.2 (2009): 539-550.
  4. Rasooli, Pooya, and Amir Albadvi. ”Knowledge Management in Call Centres.” Electronic Journal of Knowledge Management 5, no. 3 (2007):323-332.
  5. Abburu, Sunitha, and G. Suresh Babu. ”A Framework for Ontology Based Knowledge Management.” International Journal of Soft Computing and Engineering (IJSCE) Volume-3, Issue-3, 2013.
  6. Luca, De, Ernesto William, Andreas Nu¨rnberger, and O. von- Guericke. ”Ontology-based semantic online classification of documents:Supporting users in searching the web.” Proc. of the European Symposium on Intelligent Technologies (EUNITE 2004), Aachen. 2004.
  7. Ferna´ndez, Alberto, Salvador Garc´ıa, Mar´ıa Jose´ del Jesus, and Francisco Herrera. ”A study of the behaviour of linguistic fuzzy rule based clas- sification systems in the framework of imbalanced data-sets.”Fuzzy Sets and Systems 159, no. 18 (2008): 2378-2398.
  8. Chawla, Nitesh V., Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. ”SMOTE: synthetic minority over-sampling technique.” Journal of artificial intelligence research 16, no. 1 (2002): 321-357.
  9. Liu, Xu-Ying, Jianxin Wu, and Zhi-Hua Zhou. ”Exploratory undersam- pling for class-imbalance learning.” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 39.2 (2009): 539-550.
  10. Banerjee, Arindam. ”An Analysis of Logistic Models: Exponential Family Connections and Online Performance.”SDM. 200
  11. Juan Li, Nazia Zaman, Ammar Rayes, Ernesto Custodio”Semantics- enhanced Online Intellectual Capital Mining Service for Enterprise Cus- tomer Centers ”2015
Index Terms

Computer Science
Information Sciences

Keywords

Knowledge management service semantic web data mining