Modeling total distribution velocity

Martin Hirche (), Franziska Völckner, Giang Trinh and Sebastian Göbl
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Martin Hirche: University of Cologne, Faculty of Management, Economics and Social Sciences
Franziska Völckner: University of Cologne, Faculty of Management, Economics and Social Sciences
Giang Trinh: University of South Australia, Ehrenberg Bass Institute
Sebastian Göbl: University of Cologne, Faculty of Management, Economics and Social Sciences

Journal of Marketing Analytics, 2025, vol. 13, issue 4, No 5, 1035-1067

Abstract: Abstract For retailers and suppliers, keeping track of distribution velocity, which refers to the market-share gains per additional point of distribution, is important to assess the performance of their products in a market. Common distribution-velocity models use distribution-breadth metrics. However, distribution-breadth metrics lack the variability needed to meaningfully differentiate competing brands. This article presents a new approach for modeling distribution-velocity using weighted total distribution, which combines distribution-breadth and distribution-depth. Using retail scanner data from the U.S. market covering a total of 1682 brands in 12,049 stores across five channel types, we propose total-distribution models that are easier to specify, better reveal the differences in distribution between brands, and thus improve competitive benchmarking. This novel modeling approach based on total distribution serves as a pivotal contribution by providing an effective analytical tool for competitive benchmarking in diverse market environments. It allows brands to increase their market-share by spending on a fair share of total distribution. These findings highlight the usefulness of a total-distribution metric as a measure of competitive distribution coverage to support product-portfolio and category-management decisions.

Keywords: Total distribution; Distribution velocity; CPG; Category management (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1057/s41270-024-00327-w

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