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Complaint Evaluation

  • Bernd Stauss
  • Wolfgang Seidel
Chapter
Part of the Management for Professionals book series (MANAGPROF)

Abstract

Complaint evaluation can be used to systematically exploit the information potential that is contained in complaints. Basically, it comprises two areas: complaint analysis and prioritization of problems. The task in complaint analysis is to investigate the total complaint volume quantitatively with respect to certain important characteristics. For this purpose univariate and bivariate methods can be used. The univariate methods include absolute and relative frequency distributions, as well as location parameters such as arithmetic mean and median. Bivariate methods such as cross-tabulations permit the examination of correlations between two variables.

In order to provide detailed information, it makes sense to apply univariate frequency distributions and bivariate methods also to subsets of complaints (such as complaints about specific problems or complaints of a special customer group). In addition, the temporal dimension of the complaint analysis must also be considered (time-period and time series analyses).

For the prioritization of problems, customer-oriented and company-oriented variants of Frequency-Relevance Analyses (FRAC) are available. The customer-oriented variants link the frequency of problems that have occurred with the importance that the customer attaches to the problem. Company-oriented frequency-relevance analyses are available if the most urgent problems are determined by the company on the basis of experience values for a longer period of time.

References

  1. Birla M (2013) FedEx delivers: how the world’s leading shipping company keeps innovating and outperforming the competition. Wiley, HobokenGoogle Scholar
  2. Cooper M (2011) From architecture to action: the FedEx service quality journey. http://www.transportation.northwestern.edu/docs/2011/2011.10.25.FedEx_UE.pdf. Accessed 2 Dec 2017
  3. Stauss B, Seidel W (2005) Non-complaining behavior of lost service customers. In: Paper presented at SERVSIG research conference, University of Singapore, Singapore 2–4 Jun 2005Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bernd Stauss
    • 1
  • Wolfgang Seidel
    • 2
  1. 1.Catholic University of Eichstätt-IngolstadtIngolstadtGermany
  2. 2.servmark consultancyIngolstadt and MunichGermany

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