Marketing decisions are often based on empirically collected data and the goodness of data plays an important role here. Data quality can be affected by several types of errors, which are distinguished in particular into systematic and random errors, and is also influenced by the sample size. Accordingly, market researchers have to consider the goodness of data and should know which factors will have which kind of influence. In our paper, the influence of different factors of the goodness of data in the vibrant retail context will be investigated within a Monte Carlo experiment. For this purpose, a real empirical data set (n=1,500) of a survey regarding buying behavior in stationary and online shopping is used as “true” data and will be compared with “generated” data. The “generated” data are randomly disturbed and systematically varied alternatives of the “true” data. The data sets will be compared with respect to their conformity values and allow influence estimations.