“Modeling and Forecasting Customer Complaint Data"
November 14, 2006
A Presentation by Rob Kushler

Periodic customer satisfaction surveys are used in many industries. Control charting principles can and
should be applied when interpreting the results of such surveys, in order to detect changes over time and
avoid reacting to random variation. However, traditional control charts for count data are not adequate.
Alternative probability models or quasi-likelihood methods provide a more appropriate basis for
constructing such charts. The issues involved are discussed (with occasional mounting of a soapbox), and
an implementation using standard statistical software is described and illustrated using data collected in an
automotive quality tracking study that measures customer reported Things Gone Wrong (TGW).

Rob Kushler is an Associate Professor in the Department of Mathematics and Statistics at Oakland
University. He has an M.A. in Statistics and a Ph.D. in Biostatistics, both from the University of
Michigan. His research interests include development of new methodology for statistical modeling,
computer experiments, and industrial applications of statistics. He has extensive consulting experience in
the automotive industry, and also provides statistical consulting to students and faculty on campus at
Oakland University. He is the current Treasurer of the Detroit Chapter.

Karry Roberts giving Robert Kushler a Certificate of Appreciation from the Detroit Chapter