Modeling total distribution velocity

IF 4 Q2 BUSINESS
Martin Hirche, Franziska Völckner, Giang Trinh, Sebastian Göbl
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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.

Abstract Image

总配电速度建模
对于零售商和供应商来说,跟踪分销速度(指每增加一个分销点所获得的市场份额)对于评估其产品在市场中的表现非常重要。常见的分销速度模型使用分销范围指标。然而,分销范围指标缺乏有意义地区分竞争品牌所需的可变性。本文提出了一种使用加权总分销建立分销速度模型的新方法,它结合了分销广度和分销深度。我们利用美国市场的零售扫描仪数据,涵盖了五种渠道类型 12,049 家商店中的 1682 个品牌,提出了总分布模型,该模型更易于指定,能更好地揭示品牌之间的分布差异,从而提高竞争基准。这种基于总体分布的新颖建模方法为在不同市场环境中制定竞争基准提供了有效的分析工具,具有举足轻重的贡献。它使品牌能够通过在总分销中的合理支出份额来提高其市场份额。这些发现凸显了总分销指标作为衡量竞争性分销覆盖率的有用性,可为产品组合和品类管理决策提供支持。
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来源期刊
CiteScore
5.40
自引率
16.70%
发文量
46
期刊介绍: Data has become the new ore in today’s knowledge economy. However, merely storing and reporting are not enough to thrive in today’s increasingly competitive markets. What is called for is the ability to make sense of all these oceans of data, and to apply those insights to the way companies approach their markets, adjust to changing market conditions, and respond to new competitors. Marketing analytics lies at the heart of this contemporary wave of data driven decision-making. Companies can no longer survive when they rely on gut instinct to make decisions. Strategic leverage of data is one of the few remaining sources of sustainable competitive advantage. New products can be copied faster than ever before. Staff are becoming less loyal as well as more mobile, and business centers themselves are moving across the globe in a world that is getting flatter and flatter. The Journal of Marketing Analytics brings together applied research and practice papers in this blossoming field. A unique blend of applied academic research, combined with insights from commercial best practices makes the Journal of Marketing Analytics a perfect companion for academics and practitioners alike. Academics can stay in touch with the latest developments in this field. Marketing analytics professionals can read about the latest trends, and cutting edge academic research in this discipline. The Journal of Marketing Analytics will feature applied research papers on topics like targeting, segmentation, big data, customer loyalty and lifecycle management, cross-selling, CRM, data quality management, multi-channel marketing, and marketing strategy. The Journal of Marketing Analytics aims to combine the rigor of carefully controlled scientific research methods with applicability of real world case studies. Our double blind review process ensures that papers are selected on their content and merits alone, selecting the best possible papers in this field.
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