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An Empirical Study on Key Factors Affecting Churn Behavior with the Voices of Contact Center Customers

고객센터 상담내용 분석을 통한 이탈 요인에 관한 실증 연구

  • Jang, Moonkyoung (College of Business Administration, Seoul National University) ;
  • Yoo, Byungjoon (College of Business Administration, Seoul National University) ;
  • Lee, Jaehwan (College of Business Administration, Seoul National University)
  • Received : 2017.10.13
  • Accepted : 2017.11.22
  • Published : 2017.11.30

Abstract

Along with IT development, customers are getting more easily to express their opinions using various IT channels. In this situation, complaint management is a pressing issue for companies to acquire and maintain loyal customers with low cost. Most of previous studies have investigated customer complaint information by quantitative variables such as demographic information, transaction information, or complaint frequency, but studies focusing on qualitative aspects of complaint information are limited. Therefore, this paper considers the possibility for customers to leave even when they complain occasionally or briefly. This paper analyzes the quantitive aspects as well as the qualitative aspects using sentiment analysis with Exit-voice theory. The dataset contains 268,364 inquiries of 46,235 customers obtained from a contact center of a private security company in Korea. This paper carries out logistic regression and the results imply that the customers's explicit response and their implicit sentiment have different effect on customers leave. This study is expected to provide useful suggestions for the effective complaint management.

최근 고객들은 다양한 IT 채널을 이용해 의견을 자유롭게 표현할 수 있게 되었다. 이에 따라 기업은 고객들의 의견 중 특히 부정적인 의견을 효율적으로 관리하기 위해 큰 노력을 기울이고 있다. 기존의 연구들은 주로 고객의 불만 제기 횟수나 상담시간 등의 양적 데이터를 이용하여 고객 이탈 방지에 관해 연구하였으나, 고객이 실제로 언급한 불만 내용을 분석한 연구는 한정적이다. 따라서 본 연구는 고객 불만 데이터를 중심으로 고객 이탈에 영향을 주는 요인들을 알아보기 위해 이탈-항의 이론(Exit-voice theory)을 바탕으로 보안업체의 고객센터로 접수된 46,235명 고객의 268,364건의 상담내용을 양적인 측면뿐이 아니라 질적인 측면에서도 실증 분석하였다. 감성 분석 방법을 활용하여 상담내용의 감성값을 도출하고, 도출된 감성 사전을 이용해 고객의 만족 여부를 명시적으로 알기 어려운 상담내용의 고객 만족도를 예측하였다. 마지막으로 이 감성 값이 고객의 이탈에 미치는 영향을 로지스틱 회귀분석을 통해 분석하였다. 분석 결과 고객이 만족 여부를 명확히 응답하지 않은 경우에도 감성 분석을 이용해 계산된 각 감성값이 고객 이탈에 서로 다른 영향을 준다는 것을 알 수 있었다. 향후 본 연구에서 제시한 감성 분석을 통한 만족도 예측방법으로 고객의 숨은 의도를 효과적으로 파악하여 고객 불만 관리에 실무적으로 활용될 수 있을 것으로 기대한다.

Keywords

References

  1. Bennington, L., Cummane, J., and Conn, P., “Customer satisfaction and call centers: an Australian study,” International Journal of Service Industry Management, Vol. 11, No. 2, pp. 162-173, 2000. https://doi.org/10.1108/09564230010323723
  2. Chebat, J., “Silent Voices: Why Some Dissatisfied Consumers Fail to Complain,” Journal of Service Research, Vol. 7, No. 4, pp. 328-342, 2005. https://doi.org/10.1177/1094670504273965
  3. Cho, K. and Byun, D., “Methodology for selecting happy call customer: a case study,” The Study of Resional Development, Vol. 38, No. 1, pp. 87-104, 2006.
  4. Cho, S.-H. and Park, K.-H., “An Empirical Study on Enhancing User Satisfaction of Customer Service Information Systems,” The Journal of Society for e-Business Studies, Vol. 18, No. 2, pp. 257-277, 2013. https://doi.org/10.7838/jsebs.2013.18.2.257
  5. Crawford, A., Plural policing: The mixed economy of visible patrols in England and Wales, The Policy Press, 2005.
  6. De Waard, J., “The private security industry in international perspective,” European Journal on Criminal Policy and Research, Vol. 7, No. 2, pp. 143-174, 1999. https://doi.org/10.1023/A:1008701310152
  7. Einwiller, S. A. and Steilen, S., "Handling complaints on social network sites-An analysis of complaints and complaint responses on Facebook and Twitter pages of large US companies," Public Relations Review, Vol. 41, No. 2, pp. 195-204, 2015. https://doi.org/10.1016/j.pubrev.2014.11.012
  8. Farrell, D., “Exit, voice, loyalty, and neglect as responses to job dissatisfaction: A multidimensional scaling study,” Academy of Management Journal, Vol. 26, No. 4, pp. 596-607, 1983. https://doi.org/10.2307/255909
  9. Fornell, C. and Wernerfelt, B., “A model for customer complaint management,” Management Science, Vol. 7, No. 3, pp. 287-298, 2000.
  10. Hirschman, A., Exit, Voice and Loyalty: Responses to Decline in Firms, Organization and States, Cambridge, Mass: Harvard University Press, 1970.
  11. Jones, T. and Newburn, T., Private security and public policing, Oxford: Clarendon Press, 1998.
  12. Keiningham, T. L., Aksoy, L., Wallin Andreassen, T., Cooil, B., and Wahren, B. J., “Call center satisfaction and customer retention in a co-branded service context,” Managing Service Quality: An International Journal, Vol. 16, No. 3, pp. 269-289, 2006. https://doi.org/10.1108/09604520610663499
  13. Kim, Y. S. and Jeong, S. R., "Intelligent VOC Analyzing System Using Opinion Mining," Journal of Intelligent Information Systems, Vol. 19, No. 3, pp. 113-125, 2013 https://doi.org/10.13088/jiis.2013.19.3.113
  14. Kucuk, S., "Consumer exit, voice, and 'power' on the internet," Journal of Research for Consumers, Vol. 15, pp. 1-13, 2008.
  15. Loader, I., "Private security and the demand for protection in contemporary Britain," Policing and Society: An International Journal, Vol. 7, No. 3, pp. 143-162, 1997. https://doi.org/10.1080/10439463.1997.9964770
  16. Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., and Kaushansky, H., “Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry,” Neural Networks, IEEE Transactions, Vol. 11, No. 3, pp. 690-696, 2000. https://doi.org/10.1109/72.846740
  17. Ng, K. and Liu, H., “Customer retention via data mining,” Artificial Intelligence Review, Vol. 14, No. 6, pp. 569-590, 2000. https://doi.org/10.1023/A:1006676015154
  18. Park, S. H., Lee, Y. S., and Shin, H. J., "The Impact of Service Failures and Customer Complaints on Financial Performance of the Firm," Journal of the Korean Production and Operations Management Society, Vol. 22, pp. 1-20, 2011
  19. Rusbult, C. E., Farrell, D., Rogers, G., and Mainous, A. G., “Impact of Exchange Variable on Exit, Voice, Loyalty, and Neglect: An Integrative Model of Responses to Declining Job Satisfaction,” Academy of Management Journal, Vol. 31, No. 3, pp. 599-627, 1988. https://doi.org/10.2307/256461
  20. Singh, J., “A Typology of Consumer Dissatisfaction Response Styles,” Journal of Retailing, Vol. 66, No. 2, pp. 57-100, 1990.
  21. Song, H. S., Kim, J. K., Cho, Y. B., and Kim, S. H., “A personalized defection detection and prevention procedure based on the self-organizing map and association rule mining: Applied to online game site,” Artificial Intelligence Review, Vol. 21, No. 2, pp. 161-184, 2004. https://doi.org/10.1023/B:AIRE.0000021067.66616.b0
  22. Spencer, D., "Employee voice and employee retention," Academy of Management Journal, Vol. 29, No. 3, pp. 488-502, 1986. https://doi.org/10.2307/256220
  23. Tax, S. S., Brown, S. W., and Chandrashekaran, M., “Customer Evaluations of Service Complaint Experiences: Implications for Relationship Marketing,” Journal of Marketing, Vol. 62, No. 2, pp. 60-76, 1998. https://doi.org/10.2307/1252161
  24. Trubik, E. and Smith, M., "Developing a model of customer defection in the Australian banking industry," Managerial Auditing Journal, Vol. 15, No. 5, pp. 199-208, 2000. https://doi.org/10.1108/02686900010339300
  25. Yeon, J. H., Lee, D. J., Shim, J. H., Lee, S. G., “Product Review Data and Sentiment Analytical Processing Modeling,” The Journal of Society for e-Business Studies, Vol. 16, No. 4, pp. 125-137, 2011. https://doi.org/10.7838/jsebs.2011.16.4.125
  26. Yu, Y., Kim, Y., Kim, N., and Jeong, S. R., "Predicting the Direction of the Stock Index by Using a Domain‐Specific Sentiment Dictionary," Journal of Intelligence and Information Systems, Vol. 19, No. 1, pp. 92-110, 2013.