Call Data Analytics Using Big Data
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One of the important strategies that can increase the business success rate is “customer monitoring” in which customer service representatives (CSRs) measure the satisfaction of customers. Here, the only way to know about customer’s experience (CX) is conducting the surveys which are either through phone calls or sending emails. The main challenge of customer monitoring is to know about customer’s experience, and the customer service representatives record their phone calls and analyze those recorded calls by converting them into text files. They maintain a large amount of memory to store a huge number of audio files and text files. This results in data tempering, corruption of data, unauthorized access to tables, columns and rows and burden of managing the data. In this paper, we replace the recorded files with direct phone calls. Now, we can convert the phone calls to text files with the help of speech-to-text (STT) algorithm, then analyze a huge amount of text files using Hadoop MapReduce Framework and apply the text similarity algorithms for getting better results to improve the business.
KeywordsCustomer monitoring Speech-to-text algorithm Hadoop MapReduce Text similarity algorithms
The call data used in the study is taken from Airtel Customer Care Center in Hyderabad.
- 3.Shim, J.P., Koh, J., Fister, S., Seo, H.Y.: Phonetic analytics technology and big data: real-world cases. Commun ACM (2016)Google Scholar
- 4.Walker, W., Lamere, P., Kwok, P., Raj, B., Singh, R., Gouvea, E., Wolf, P., Woelfel, J.: Sphinx-4: a flexible open source framework for speech recognition (2004)Google Scholar
- 6.Elgendy, N., Elragal, A.: Big data analytics: a literature review paper. In: ICDM 2014, LNAI 8557, pp. 214–227 (2014)Google Scholar
- 7.Karakus, B., Aydin, G.: Call center performance evaluation using big data analytics. In: ISNCC, IEEEXplore (2016)Google Scholar
- 8.Chowdhury, T.R., Arumugam, A.S.P., Lee, J.: Analysis of call data record (CDR) using Hadoop cluster. In: ASEE-NE’15 (2015)Google Scholar
- 9.Pal, A.R., Saha, D.: Detection of slang words in e-Data using semi-supervised learning. Int. J. Artif. Intell. & Appl. 4(5) (2013)Google Scholar
- 11.Pradhan, N., Gyanchandani, M., Wadhvani, R.: A review on text similarity technique used in ir and its application. Int. J. Comput. Appl. 120(9) (2015)Google Scholar