Decision Rule Induction: Relieving Complexity in Detecting Defection
Suhel Ahmad1, Alpana Srivastava2, Seema Sharma3
1Suhel Ahmad is a Research Scholar at Amity University Uttar Pradesh, Lucknow Campus.
2Dr. Alpana Srivastava, Currently Working as Professor at Amity University Uttar Pradesh, Lucknow Campus.
3Dr. Seema Sharmais, Faculty in the area of Economics and Statistical Analysis in the Department of Management Studies at IIT Delhi.
Manuscript received on 11 March 2019 | Revised Manuscript received on 15 March 2019 | Manuscript published on 30 July 2019 | PP: 3119-3123 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2757078219/19©BEIESP | DOI: 10.35940/ijrte.B2757.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Customer attrition has become a serious problem globally, particularly in telecom service, resulting into substantial revenue decline. Attrition may result in accumulation ofdues as a resultof payment defaults. Proactive identification of potential attrite will help in retention as well as minimizing loss of revenue. For attrition detection many robust but complex algorithms are used. Depending on the severity of error, the complexity can be lessened and thus cost. Two methods of decision rules (1R& C5.0) are used to predict the attrition and predictive accuracy is judged withconfusion matrix. Comparison between models is made by sensitivity and specificity. It was found that 1R has a sensitivity of .60 against .69 for C5.0 and hence, the performance is not significantly different. It is suggested that 1R could be used instead of more complex algorithmsand also it can be adopted for benchmarking.
Key words: Customer Attrition;1R; C5.0; Churn; Decision Rules; Defection
Scope of the Article: Social Sciences