A model to obtain a SERVPERF scale evaluation of the CRM customer complaints: an application to the 4G telecommunications sector
The relationship between customer churn and their complaints is sufficiently contrasted in the telecom sector. Therefore, a key part of a company’s strategy is the measurement of this dissatisfaction. It is important to conduct periodic surveys on complaints in a standard form like the SERVPERF scale because it enables the organization to benchmark. Many of these complaints are stored in the company’s CRM. Our first aim is to define a model to transform CRM customer complaints, expressed in natural language, into SERVPERF scales. In the proposed model, we use the 2-tuple model, which allows computing this linguistic data without losing information. Our second purpose is to implement a prototype to apply the model in a 4G Company. As a practical conclusion, most complaints in this emerging technology (which still has some deficiencies) are related to technical aspects of the services rather than to staff.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Carrasco, R. A., Sánchez-Fernández, J., Muñoz-Leiva, F., Blasco, M. F., & Herrera-Viedma, E. (2017). Evaluation of the hotels e-services quality under the user’s experience. Soft Computing, 4(21), 995-1011. https://doi.org/10.1007/s00500-015-1832-0
Chang, S. C., & Tsai, P. H. (2016). A hybrid financial performance evaluation model for wealth management banks following the global financial crisis. Technological and Economic Development of Economy, 22(1), 21-46. https://doi.org/10.3846/20294913.2014.986771
Cid-López, A., Hornos, M. J., Carrasco, R. A., & Herrera-Viedma, E. (2015). A hybrid model for decision-making in the information and communications technology sector. Technological and Economic Development of Economy, 21(5), 720-737. https://doi.org/10.3846/20294913.2015.1056281
Cronin Jr, J. J., & Taylor, S. A. (1994). SERVPERF versus SERVQUAL: reconciling performance-based and perceptions-minus-expectations measurement of service quality. Journal of Marketing, 58(1), 125-131. https://doi.org/10.2307/1252256
De Maio, C., Fenza, G., Loia, V., & Orciuoli, F. (2016ª). Linguistic fuzzy consensus model for collaborative development of fuzzy cognitive maps: a case study in software development risks. Fuzzy Optimization and Decision Making, 1-17.
De Maio, C., Tommasetti, A., Troisi, O., Vesci, M., Fenza, G., & Loia, V. (2016b). Contextual fuzzy-based decision support system through opinion analysis: a case study at university of the Salerno. International Journal of Information Technology & Decision Making, 15(05), 923-948. https://doi.org/10.1142/S0219622016500231
Elkhalifa, L., Adaikkalavan, R., & Chakravarthy, S. (2005, March). InfoFilter: a system for expressive pattern specification and detection over text streams. ACM Symposium on Applied Computing. Santa Fe, New Mexico, USA. https://doi.org/10.1145/1066677.1066923
Ferreira, F. A., Ferreira, J. J., Fernandes, C. I., Meidutė-Kavaliauskienė, I., & Jalali, M. S. (2017). Enhancing knowledge and strategic planning of bank customer loyalty using fuzzy cognitive maps. Technological and Economic Development of Economy, 23(6), 1-17. https://doi.org/10.3846/20294913.2016.1213200
Gambetta, N., Zorio-Grima, A., & García-Benau, M. A. (2015). Complaints management and bank risk profile. Journal of Business Research, 68(7), 1599-1601. https://doi.org/10.1016/j.jbusres.2015.02.002
Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2006, May). Churn prediction using complaints data. Conference World Academy of Science, Engineering and Technology, 13, 158-163.
Hadidi, L., Assaf, S., & Alkhiami, A. (2017). A systematic approach for ERP implementation in the construction industry. Journal of Civil Engineering and Management, 23(5), 594-603. https://doi.org/10.3846/13923730.2016.1215348
Hajek, P., Olej, V., & Myskova, R. (2014). Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technological and Economic Development of Economy, 20(4), 721-738. https://doi.org/10.3846/20294913.2014.979456
Herrera, F., & Martínez, L. (2000). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6), 746-752. https://doi.org/10.1109/91.890332
Hiroshi, K., Tetsuya, N., & Hideo, W. (2004, August). Deeper sentiment analysis using machine translation technology. 20th International Conference on Computational Linguistics. Geneva, Switzerland. https://doi.org/10.3115/1220355.1220426
Jain, P. (2013). Telecommunication service quality assessment: a comparative study of Bharat Sanchar Nigam Limited and Reliance Communications Limited. Asia-Pacific Journal of Management Research and Innovation, 9(1), 99-106. https://doi.org/10.1177/2319510X13483517
Jain, P., Tomar, A., & Vishwakarma, N. (2016). Analysis of customer satisfaction in Telecom Sector on implementation of CRM with respect to users. International Journal of Engineering, Management & Medical Research, 2(3), 1-8.
Jain, S. K., & Gupta, G. (2004). Measuring service quality: SERVQUAL vs. SERVPERF scales. Vikalpa, 29(2), 25-37. https://doi.org/10.1177/0256090920040203
Jain, T. I., & Nemade, D. (2010). Recognizing contextual polarity in phrase-level sentiment analysis. International Journal of Computer Applications, 7(5), 5-11. https://doi.org/10.5120/1160-1453
Kim, S. M., & Hovy, E. (2004, August). Determining the sentiment of opinions. 20th International Conference on Computational Linguistics. Geneva, Switzerland. https://doi.org/10.3115/1220355.1220555
Lai, F., Griffin, M., & Babin, B. J. (2009). How quality, value, image, and satisfaction create loyalty at a Chinese telecom. Journal of Business Research, 62(10), 980-986. https://doi.org/10.1016/j.jbusres.2008.10.015
Lee, C. H., Wang, Y. H., & Trappey, A. J. (2015). Ontology-based reasoning for the intelligent handling of customer complaints. Computers & Industrial Engineering, 84, 144-155. https://doi.org/10.1016/j.cie.2014.11.019
Li, Y., & Liu, P. (2015). Some Heronian mean operators with 2-tuple linguistic information and their application to multiple attribute group decision making. Technological and Economic Development of Economy, 21(5), 797-814. https://doi.org/10.3846/20294913.2015.1055614
Martínez-Cruz, C., Porcel, C., Bernabé-Moreno, J., & Herrera-Viedma, E. (2015). A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Information Sciences, 311, 102-118. https://doi.org/10.1016/j.ins.2015.03.013
Maurer, C., & Schaich, S. (2011). Online customer reviews used as complaint management tool. In R. Law, M. Fuchs & F. Ricci (Eds.), Information and communication technologies in tourism. New York, NY: Springer-Wien. https://doi.org/10.1007/978-3-7091-0503-0_40
Mi, C., Shan, X., Qiang, Y., Stephanie, Y., & Chen, Y. (2014). A new method for evaluating tour online review based on grey 2-tuple linguistic. Kybernetes, 43(3/4), 601-613. https://doi.org/10.1108/K-06-2013-0123
Morente-Molinera, J. A., Mezei, J., Carlsson, C., & Herrera-Viedma, E. (2017). Improving supervised learning classification methods using multi-granular linguistic modelling and fuzzy entropy. IEEE Transactions on Fuzzy Systems, 25(5), 1078-1089. https://doi.org/10.1109/TFUZZ.2016.2594275
Naik, C. K., Gantasala, S. B., & Prabhakar, G. V. (2010). Service quality (SERVQUAL) and its effect on customer satisfaction in retailing. European Journal of Social Sciences, 16(2), 231-243.
Nakov, P., Kozareva, Z., Ritter, A., Rosenthal, S., Stoyanov, V., & Wilson, T. (2013, June). Sentiment analysis in Twitter. 7th International Workshop on Semantic Evaluation. Atlanta, Georgia, USA.
Pang, B., & Lee, L. (2004, July). A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. 42nd Annual Meeting on Association for Computational Linguistics. Barcelona, Spain. https://doi.org/10.3115/1218955.1218990
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49, 41-50. https://doi.org/10.2307/1251430
Picón, A., Castro, I., & Roldán, J. L. (2014). The relationship between satisfaction and loyalty: a mediator analysis. Journal of Business Research, 67(5), 746-751. https://doi.org/10.1016/j.jbusres.2013.11.038
Piepiorra, F. (2015). vtiger CRM v6. 2.0-User and Administration Manual. Lulu.
Prabowo, R., & Thelwall, M. (2009). Sentiment analysis: a combined approach. Journal of Informetrics, 3(2), 143-157. https://doi.org/10.1016/j.joi.2009.01.003
Shea, C., Faisal, M., Ford, R., Lin, W., & Matsuda, Y. (2008). Oracle Text Reference 11g. Release 1: Oracle Corporation.
Shen, J., Tang, S., & Zhu, H. (2010, August). The investigation in service quality management of 3G business for telecom operators. International Conference on Management and Service Science. Wuhan, China. https://doi.org/10.1109/ICMSS.2010.5577082
Snyder, M., Steger, J., & Landers, B. (2011). Microsoft Dynamics CRM 2011 step by step. Pearson Education.
Tax, S. S., Brown, S. W., & Chandrashekaran, M. (1998). Customer evaluations of service complaint experiences: Implications for relationship marketing. Journal of Marketing, 62, 60-76. https://doi.org/10.2307/1252161
Trainor, K. J., Andzulis, J. M., Rapp, A., & Agnihotri, R. (2014). Social media technology usage and customer relationship performance: a capabilities-based examination of social CRM. Journal of Business Research, 67(6), 1201-1208. https://doi.org/10.1016/j.jbusres.2013.05.002
Van der Wal, R. W. E., Pampallis, A., & Bond, C. (2002). Service quality in a cellular telecommunications company: a South African experience. Managing Service Quality: An International Journal, 12(5), 323-335. https://doi.org/10.1108/09604520210442119
Wisniewski, M. (2001). Using SERVQUAL to assess customer satisfaction with public sector services. Managing Service Quality: An International Journal, 11(6), 380-388. https://doi.org/10.1108/EUM0000000006279
Yilmaz, C., Varnali, K., & Kasnakoglu, B. T. (2016). How do firms benefit from customer complaints?. Journal of Business Research, 69(2), 944-955. https://doi.org/10.1016/j.jbusres.2015.08.038
Zadeh, L. A. (1975). The concept of a linguistic variable and its applications to approximate reasoning, Pt I. Information Sciences, 8, 199-249. Pt II, Information Sciences, 8, 301-357. Pt III, Information Sciences, 9, 43-80. https://doi.org/10.1016/0020-0255(75)90046-8