Browsing by Author "Pauwels, K."

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    Big and lean is beautiful: a conceptual framework for data-based learning in marketing management
    (Emerald Publishing Limited, 2019-09-19) Soyer, E.; Pauwels, K.; Seggie, Steven Head; Entrepreneurship; Rindfleisch, A.; Malter, A. J.; SEGGIE, Steven Head
    While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn't necessarily improve their decision-making. Managers would still be vulnerable to confirmation bias, control illusions, communication problems, and confidence issues (the 4Cs). The authors argue that traditional remedies for such biases don't go far enough and propose a lean start-up approach to data-based learning in marketing management. Specifically, they focus on the marketing analytics component of Big Data and how adaptations of the lean start-up methodology can be used in some combination with such analytics to help marketing managers improve their decision-making and innovation process. Beyond the often discussed technical obstacles and operational costs associated with handling Big Data, this chapter contributes by analyzing the various learning and decision-making problems that can emerge once the 4Vs of Big Data have materialized.
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    ArticlePublicationOpen Access
    How do line extensions impact brand sales? The role of feature similarity and brand architecture
    (Springer, 2023-11) Sezen, B.; Pauwels, K.; Ataman, Mehmet Berk; Business Administration; ATAMAN, Mehmet Berk
    Brand architecture decisions have important performance implications but have seen little quantitative research. In particular, there is little empirical evidence on how the strength of the link established among clusters of products within the company’s portfolio impact the sales effects of typical marketing actions such as line extensions. This paper quantifies the effect of different brand architecture choices and product feature similarity in moderating the impact of line extensions on brand sales. Based on categorization theory, the authors hypothesize that brand name similarity and feature similarity, both independently, and in interaction, increase brand cannibalization. The empirical analysis in three consumer packaged-goods categories shows that it is more critical to minimize the feature similarity than brand name similarity to limit cannibalization and generate higher incremental sales from line extensions. Controlling for feature similarity, line extensions introduced under sub-brands cause greater cannibalization.