Jan Müller, Tatiana Mochalova, Gabriele Eckstein
Ipsos Loyalty Hamburg
Presented at the 13th Frontiers in Service conference, Taipei, Taiwan
Traditional methods of customer satisfaction research like regression analysis build on linear modeling to identify key drivers of customer satisfaction. Strong drivers with a low level of satisfaction are taken as the primary targets of intervention in order to optimize overall satisfaction. These methods assume a linear relationship between performance and satisfaction. Such an assumption is often justified. There are, however, important exceptions. Linear models fall short when it comes to the identification of saturation effects or zones where changes of performance yield a maximum effect. Real-life market-research examples are the maximum tolerable waiting time on telephone hotlines or the relationship between satisfaction and the opening hours of shops.
We present a methodology that builds on non-linear modeling to identify saturation effects. Our approach is based on least-squares estimation and bootstrapping. If adequate, a non-linear modeling of customer satisfaction does not only improve model fit but yields additional qualitative insights when the shape of the curve of the fitted function is taken into consideration. We therefore put special emphasis on the choice of the non-linear function and the interpretability of its shape. While the choice of the function depends on
the circumstances, we found that fitting an S-shaped logistic function is especially well-suited to customer satisfaction research. Its mathematical properties provide a heuristic to identify saturation zones and optimal zones of intervention.
Using real-life data, we demonstrate how a multivariate analysis of non-linear effects is conducted by integrating the approach into structural equation modeling. Saturation effects are visualized by an improved action portfolio chart that takes non-linear effects into account.