Patterns in Consumption-based Learning about Brand Quality for Consumer Packaged Goods

Maciej Szymanowski, E Gijsbrechts

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)


In this paper, we explore the patterns of consumption-based learning about brand quality in mature consumer packaged goods (CPG) categories, as well as category and household characteristics that drive such learning. We calibrate brand-choice models with Bayesian learning on household purchases in over thirty CPG categories. This yields category- and household-specific estimates of the extent to which consumers update their knowledge on the quality of specific brands, with new consumption-based information from those brands. We then link this degree of learning to the underlying household- and category drivers. We find that learning is present and significant in almost all categories, yet varies in strength across categories and across households. Learning about brand quality is negatively associated with variety seeking. Conversely, it is stronger in categories where consumers have higher monetary (expensive items) and especially non-monetary stakes (categories with higher performance risk and involvement). In line with the `enrichment¿ hypothesis, familiarity with the category resulting from frequent category purchases increases the information extracted from new consumption experiences ¿ be it only up to a certain point. Interestingly, however, market mavens learn less ¿ a possible sign of their overconfidence. While some households learn more than others across-the-board, category factors are the strongest drivers of learning. Managerial implications are discussed.
Original languageEnglish
Pages (from-to)219-235
Number of pages17
JournalInternational Journal of Research in Marketing
Issue number3
Publication statusPublished - 2013


Dive into the research topics of 'Patterns in Consumption-based Learning about Brand Quality for Consumer Packaged Goods'. Together they form a unique fingerprint.

Cite this