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Two Essays on Retailing Analytics in...

Pan, Yang.

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Two Essays on Retailing Analytics in Convenience Stores Using Consumer Basket Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Two Essays on Retailing Analytics in Convenience Stores Using Consumer Basket Data./
作者:	Pan, Yang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	112 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Contained By:	Dissertations Abstracts International81-04B.
標題:	Marketing. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13886279
ISBN:	9781085792288

Two Essays on Retailing Analytics in Convenience Stores Using Consumer Basket Data.
Pan, Yang.

Two Essays on Retailing Analytics in Convenience Stores Using Consumer Basket Data. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 112 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: B.

Thesis (Ph.D.)--The University of Iowa, 2019.

This item must not be sold to any third party vendors.

Loyalty programs for convenience stores generate consumer shopping histories that are both large in size and sparse in content. Analyzing such data with traditional basket models is computationally difficult since most models are not scalable to a large set of categories. However, analyzing large data with traditional models has important advantages: the models capture consumer (shopping) behaviors that assist managers in making strategic decisions. In this thesis, we develop two studies to analyze this large and sparse convenience store shopping data.In the first study, we bridge the gap between traditional basket model analysis and the challenges of large shopping data by developing a retail market basket modeling system that captures essential elements of consumer shopping behavior in a computationally attractive manner. An application of the model to convenience store basket data yields excellent results. The main outputs of the model (segmentation structure, cross-category dependence, price elasticities) align well with managerial intuition. Moreover, the model provides excellent forecasts to a holdout sample of consumers. Using the model, we examine the revenue impact of a change in promotion policy.In the second study, we add spatial extensions to the previous model to solve a more complex problem: retail location analysis. We develop a spatial basket model to analyze the spatial pattern of consumer heterogeneity across stores, and show how to use this model to predict the demand of a new store (without any data of consumer purchase history). The main outputs of the extended model also align well with managerial intuition. Additionally, the model provides excellent forecasts to the demand of the hold-out store.

ISBN: 9781085792288Subjects--Topical Terms:

536353
Marketing.
Subjects--Index Terms:

Loyalty programs

Two Essays on Retailing Analytics in Convenience Stores Using Consumer Basket Data.
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520 $a Loyalty programs for convenience stores generate consumer shopping histories that are both large in size and sparse in content. Analyzing such data with traditional basket models is computationally difficult since most models are not scalable to a large set of categories. However, analyzing large data with traditional models has important advantages: the models capture consumer (shopping) behaviors that assist managers in making strategic decisions. In this thesis, we develop two studies to analyze this large and sparse convenience store shopping data.In the first study, we bridge the gap between traditional basket model analysis and the challenges of large shopping data by developing a retail market basket modeling system that captures essential elements of consumer shopping behavior in a computationally attractive manner. An application of the model to convenience store basket data yields excellent results. The main outputs of the model (segmentation structure, cross-category dependence, price elasticities) align well with managerial intuition. Moreover, the model provides excellent forecasts to a holdout sample of consumers. Using the model, we examine the revenue impact of a change in promotion policy.In the second study, we add spatial extensions to the previous model to solve a more complex problem: retail location analysis. We develop a spatial basket model to analyze the spatial pattern of consumer heterogeneity across stores, and show how to use this model to predict the demand of a new store (without any data of consumer purchase history). The main outputs of the extended model also align well with managerial intuition. Additionally, the model provides excellent forecasts to the demand of the hold-out store.
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