Despite the growing availability of scanner-panel data, surveys remain the most common and inexpensive method of gathering marketing metrics. The purpose of this paper is to explore the size, direction and correction of response errors in retrospective reports of category buying.
Self-reported purchase frequency data were validated using British household panel records and the negative binomial distribution (NBD) in six packaged goods categories. The log likelihood theory and the fit of the NBD model were used to test an approach to adjusting the errors post-data collection.
The authors found variations in systematic response errors according to buyer type. Specifically, lighter buyers tend to forward telescope their buying episodes. Heavier buyers tend either to over-use a rate-based estimation of once-a-month buying and over-report purchases at multiples of six or to use round numbers. These errors lead to overestimates of penetration and average purchase frequency. Adjusting the aggregate data for the NBD, however, improves the accuracy of these metrics.
In light of the importance of purchase data for decision making, the authors describe the inaccuracy problem in frequency reports and offer practical suggestions regarding the correction of survey data.
Two novel contributions are offered here: an investigation of errors in different buyer groups and use of the NBD in survey accuracy research.
The authors thank Professor Gerald Goodhardt for helpful comments and Kantar UK Worldpanel for providing the data for the study. They also acknowledge the first author’s support through an Australian Government Research Training Program Scholarship.
Ludwichowska, G., Romaniuk, J. and Nenycz-Thiel, M. (2017), "Systematic response errors in self-reported category buying frequencies", European Journal of Marketing, Vol. 51 No. 7/8, pp. 1440-1459. https://doi.org/10.1108/EJM-07-2016-0408
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