A Customer complaint from a telecommunication company: a Bayesian data analysis
Article Sidebar
Main Article Content
صندلی اداریAbstract
This study considers a customer complaint dataset due to the technical services provided by a telecommunications company collected for 134 consecutive weeks from the first week of January 2018 up to the year 2019. The total count of weekly complaints is the sum of different causes, which characterizes compositional data. The data was analyzed assuming a Poisson regression model for the weekly total complaint count data in presence of a random factor and compositional models both under a Bayesian approach using existing MCMC (Monte Carlo Markov Chain) to get the posterior summaries of interest. The obtained results are of great importance to improve the service quality of the company.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
1. Proposal of Policy for Free Access Periodics
Authors whom publish in this magazine should agree to the following terms:
a. Authors should keep the copyrights and grant to the magazine the right of the first publication, with the work simultaneously permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 that allows the sharing of the work with recognition of the authorship of the work and initial publication in this magazine.
b. Authors should have authorization for assuming additional contracts separately, for non-exclusive distribution of the version of the work published in this magazine (e.g.: to publish in an institutional repository or as book chapter), with recognition of authorship and initial publication in this magazine.
c. Authors should have permission and should be stimulated to publish and to distribute its work online (e.g.: in institutional repositories or its personal page) to any point before or during the publishing process, since this can generate productive alterations, as well as increasing the impact and the citation of the published work (See The Effect of Free Access).
Proposal of Policy for Periodic that offer Postponed Free Access
Authors whom publish in this magazine should agree to the following terms:
a. Authors should keep the copyrights and grant to the magazine the right of the first publication, with the work simultaneously permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 [SPECIFY TIME HERE] after the publication, allowing the sharing of the work with recognition of the authorship of the work and initial publication in this magazine.
b. Authors should have authorization for assuming additional contracts separately, for non-exclusive distribution of the version of the work published in this magazine (e.g.: to publish in institutional repository or as book chapter), with recognition of authorship and initial publication in this magazine.
c. Authors should have permission and should be stimulated to publish and to distribute its work online (e.g.: in institutional repositories or its personal page) to any point before or during the publishing process, since this can generate productive alterations, as well as increasing the impact and the citation of the published work (See The Effect of Free Access).
d. They allow some kind of open dissemination. Authors can disseminate their articles in open access, but with specific conditions imposed by the editor that are related to:
Version of the article that can be deposited in the repository:
Pre-print: before being reviewed by pairs.
Post-print: once reviewed by pairs, which can be:
The version of the author that has been accepted for publication.
The editor's version, that is, the article published in the magazine.
At which point the article can be made accessible in an open manner: before it is published in the magazine, immediately afterwards or if a period of seizure is required, which can range from six months to several years.
Where to leave open: on the author's personal web page, only departmental websites, the repository of the institution, the file of the research funding agency, among others.
References
Aitchinson, J. (1982). The statistical analysis of compositional data. Journal of Royal Statistical Society, B, 139-177.
Aitchinson, J. (1986). The statistical analysis of compositional data. Chapman and Hall.
Aitchinson, J., & Shen, S.M. (1985). Logistic-normal distributions: Some properties and uses. Biometrika, 47,136-146.
Albert, J.H., & Chib, S. (1993). Bayesian analysis of binary and polychotonious response data. Journal of the American Statistical Association, 88, 669-679.
Anderson, E.W., Fornell, C., & Mazvancheryl, S. K. (2004). Customer satisfaction and shareholder value. Journal of Marketing, 68(4), 172-185.
Chevalier, J.A., & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43(3), 345-354. DOI: 10.1509/jmkr.43.3.345.
Chib, S., & Greenberg, E. (1995). Understanding the Metropolis-Hastings algorithm. The American Statistician, 49, 327-335.
Claro, D.P., Fragoso, A.F.G.R., Laban-Neto, S.A., & Claro, P.B.O. (2014). Consumer Complaints and Company Market Value. Brazilian Administration Review, 11(3), 248-263. DOI: http://dx.doi.org/10.1590/1807-7692bar2014130004.
Clayton, D.G. (1991). A Monte Carlo method for Bayesian inference in frailty models. Biometrics, 47, 467-485.
Coelho, B.R.G., Queiroz, V.G. Calazans, C.H.J., & Silva, R.L.K. (2016). A consolidação de sites de reclamação online como uma alternativa eficaz no intermédio das relações de consumo: um estudo de caso do site Reclame Aqui. Anais do Congresso de Ciências da Comunicação na Região Nordeste, Caruaru, PE, Brasil.
Crouchley, R., & Davies, R.B. (1999). A comparison of population average and random effects models for the analysis of longitudinal count data with baseline information. Journal of the Royal Statistical Society, A, 162, 331-347.
Dunson, D.B. (2000). Bayesian latent variable models for clustered mixed outcomes. Journal of the Royal Statistical Society, B, 62, 335-366.
Dunson, D.B. (2003). Dynamic latent trait models for multidimensional longitudinal data. Journal of the American Statistical Association, 98, 555-563.
Dunson, D.B., & Herring, A.H. (2005). Bayesian latent variable models for mixed discrete outcomes. Biostatistics, 6, 11-25.
Egozcue, J.J., Pawlowsky-Glahn, V., Mateu-Figueras, G., & Barcelo-Vidal, C. (2003). Isometric log-ratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279-300.
Fornell, C., & Wernerfelt, B. (1987). Defensive Marketing Strategy by Customer Complaint Management: A Theoretical Analysis, Journal of Marketing Research, 24(4), 337-346.
Gelfand, A.E., & Smith, A.F.M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398-409.
Goldenberg, J., Libai, B., Moldovan, S., & Muller, E. (2007). The NPV of bad news. International Journal of Research in Marketing, 24(3), 186-200.
Henderson, R., & Shimakura, S. (2003). A serially correlated gamma frailty model for longitudinal count data. Biometrika, 90, 355-366.
Iyengar, M., & Dey, D.K. (1996). Bayesian analysis of compositional data. Department of Statistics, University of Connecticut, Storrs, CT 06269-3120.
Iyengar, M., & Dey, D.K. (1998). Box-Cox transformations in Bayesian analysis of compositional data. Environmetrics, 9, 657-671.
Johnson, R., & Wichern, D. (1982). Applied multivariate statistical analysis. New Jersey: Prentice Hall.
Jorgensen, B., Lundbye-Christiansen, S., Song, P.X.K., & Sun, L. (1999). A state space model for multivariate longitudinal count data. Biometrika, 86, 169-191.
Kotler, P., & Armstrong, G. (2003). Princípios de Marketing. 9. ed. São Paulo: Prentice Hall.
Luo, X. (2007). Consumer negative voice and firm-idiosyncratic stock returns. Journal of Marketing, 71(3), 75-88. DOI: 10.1509/jmkg.71.3.75
Luo, X. (2009). Quantifying the long-term impact of negative word of mouth on cash flows and stock prices. Marketing Science, 28(1), 148-165. DOI: 10.1287/mksc.1080.0389
Lunn, D., Spiegelhalter, D., Thomas, A., & Best, N. (2009). The BUGS project: evolution, critique and future directions. Statistics in Medicine, 28(25), 3049-3067.
Mahfood, P.E. (1994). Transformando um cliente insatisfeito em um cliente para sempre. São Paulo: Makron Books.
Martin-Fernandez, J.A., Daunis-Estadella, J., & Mateu-Figueras, G.G. (2015). On the interpretation of differences between groups for compositional data. SORT- Statistics and Operations Research Transactions, 39, 231-252.
Martinez, E.Z., Achcar, J.A., Aragon, D.C., & Brunherotti, M.A.A. (2019). A Bayesian analysis for pseudo-compositional data with spatial structure. Statistical Methods in Medical Research, 28, 096228021986258-XX.
Martins, B.C., & Julio, G.N. (2013). A ascensão ou queda de uma marca nas mãos do novo consumidor: um estudo de caso da Americanas.com. Revista de Administração e Negócios da Amazônia, 5(2), 1-1.
Matos, C.A., & Rossi, C.A.V. (2008). Word-of-mouth communications in marketing: a meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science, 36(4), 578-596.
Mittal, V., Ross, W.T., & Baldasare, P.M. (1998). The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions. Journal of Marketing, 62(1), 33-47.
Montgomery, D.C., & Runger, G.C. (2011). Applied statistics and probability for engineers. New York: Wiley
Moustaki, I. (1996). A latent trait and a latent class model for mixed observed variables. British Journal of Mathematical and Statistical Psychology, 49, 313–334.
Moustaki, I., & Knott, M. (2000). Generalized latent trait models. Psychometrika, 65, 391 - 411.
Pimentel, R.C., & Aguiar, A.B. (2012). Persistência de lucros trimestrais: uma investigação empírica no Brasil. Brazilian Business Review, 9, 39-57.
Rayens, W.S., & Srinivasan, C. (1991). Box-Cox transformations in the analysis of compositional data. Journal of Chemometrics, 5, 227-239.
Richins, M.L. (1983). Negative word-of-mouth by dissatisfied consumers: a pilot study. Journal of Marketing, 47(1), 68-78.
Roberts, G.O., & Smith, A.F.M. (1993). Bayesian methods via the Gibbs sampler and related Markov Chain Monte Carlo methods, Journal of the Royal Statistical Society, B, Cambridge, 55(1), 3-23.
Romani, S., Grappi, S., & Dalli, D. (2012). Emotions that drive consumers away from brands: measuring negative emotions toward brands and their behavioral effects. International Journal of Research in Marketing, 29(1), 55-67.DOI: 10.1016/j.ijresmar.2011.07.001
Sammel, M.D., Ryan, L.M., & Legler, J.M. (1997). Latent variable models and mixed discrete and continuous outcomes. Journal of the Royal Statistical Society, B, 59, 667–678.
Singh, J. (1988). Consumer complaint intentions and behavior: definitional and taxonomical issues. Journal of Marketing, 52(1), 93-107.
Singh, J., & Wilkes, R.E. (1996). When consumers complain: a path analysis of the key antecedents of consumer complaint response estimates. Journal of the Academy of Marketing Science, 24(4), 350-365.
Sousa, F.J.S.F. (2011). Satisfação de Clientes - O Caso de Uma Empresa Industrial, Dissertação de Mestrado em Marketing, Faculdade de Economia da Universidade de Coimbra, Portugal.
Tjelmeland, N.D.H., & Lund, K.V. (2003). Bayesian modelling of spatial compositional data, Journal of Applied Statistics, 30, 87-100.
Trusov, M., Bucklin, R.E., & Pauwel, K. (2009). Effects of word-of-mouth versus traditional marketing: findings from an internet social networking site. Journal of Marketing, 73(5), 90-102. DOI: 10.1509/jmkg.73.5.90.
Winchester, M., Romaniuk, J., & Bogomolova, S. (2008). Positive and negative brand beliefs and brand defection/uptake. European Journal of Marketing, 42(5/6), 553-570. DOI: 10.1108/03090560810862507.