Analysis of cross category dependence in market basket selection

Russell, Gary J.; Petersen, Ann

doi:10.1016/s0022-4359(00)00030-0

Cited by 257 publications

(202 citation statements)

References 15 publications

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“…Using this data, retailers (and researchers) could analyze market baskets crosssectionally -item co-occurrences, complements and substitutes, cross-category dependence etc (see, e.g., Blattberg et al 2008;Russell and Petersen 2000); analyze aggregate sales and inventory movement patterns by SKU (e.g., Anupindi, Dada and Gupta 1998 in a vending machine scenario); compute elasticities for prices and shelf space at the different levels of aggregation such as category, brand, SKU etc (e.g., Hoch et al 1995 on store-level price elasticities; Bijmolt et al…”

Section: New Sources Of Datamentioning

confidence: 99%

The Role of Big Data and Predictive Analytics in Retailing

Bradlow

Gangwar

Kopalle

et al. 2017

Journal of Retailing

353

181

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The paper examines the opportunities in and possibilities arising from Big Data in retailing, particularly along five major data dimensions -data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of Big Data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.2

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Section: New Sources Of Datamentioning

confidence: 99%

The Role of Big Data and Predictive Analytics in Retailing

Bradlow

Gangwar

Kopalle

et al. 2017

Journal of Retailing

353

181

View full text Add to dashboard Cite

show abstract

“…Russell and Petersen (2000) remark in their conclusion that their model is not applicable to a larger number of product categories. However, studies show that ignoring purchased categories signicantly biases the estimates of parameters included in the model.…”

Section: Literature Review On Multicategory Purchase Incidence Modelsmentioning

confidence: 97%

“…Although research in explanatory market basket analysis may include several steps of consumer decision making, i.e., purchase incidence, brand choice, and purchase quantity, our work focuses on multicategory purchase incidence, on whether to buy models (Seetharaman et al, 2005), as in the seminal paper of Russell and Petersen (2000;RP). The benet of their multivariate logit (MVL) model is the translation of a joint basket purchase decision into a consistent set of conditional category purchase decisions (conditional choice specication).…”

Section: Literature Review On Multicategory Purchase Incidence Modelsmentioning

confidence: 99%

“…Hruschka (1991) and Hruschka et al (1999) as well as Dippold and Hruschka (2010) neither integrate heterogeneity in the model structure nor allow for customer-specic covariates. Likewise, Russell and Petersen (2000) do not account for latent heterogeneity. However, they include observable sources of heterogeneity as model covariates by letting customers dier in time since last category purchase, category loyalty, and mean basket size.…”

Section: Literature Review On Multicategory Purchase Incidence Modelsmentioning

confidence: 99%

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A Model of Heterogeneous Multicategory Choice for Market Basket Analysis

Dippold

Hruschka

2013

Review of Marketing Science

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Based on market basket data, multicategory purchase incidence models analyze demand interdependencies between product categories. We propose a nite mixture multivariate logit model to derive segment-specic intercategory eects of market basket purchase. Under the assumption that only a fraction of intercategory eects are signicant, we exclude irrelevant eects by variable selection. This leads to a detailed description of consumers' shopping behavior that varies over segments not only w.r.t. parameters' values but also w.r.t. included interaction eects. We nd that a homogeneous model would overestimate the intensity of interaction between product categories.

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“…This work was of exploratory nature and based on pairwise association measures between product categories. The first truly multivariate models which aimed to explain consumers' multi-category choice decisions appeared in the early 1990s (e.g., Hruschka 1991) and were later extended to include predictors, especially marketing variables (Hruschka et al 1999;Manchanda et al 1999;Russell and Petersen 2000). But also exploratory approaches evolved parallel with the availability of increasingly large shopping basket databases and computational advances.…”

mentioning

confidence: 99%

Editorial

Hruschka

Reutterer

2017

J Bus Econ

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To the best of our knowledge work on multi-category purchase behavior began in the mid 1970s (Böcker 1975). This work was of exploratory nature and based on pairwise association measures between product categories. The first truly multivariate models which aimed to explain consumers' multi-category choice decisions appeared in the early 1990s (e.g., Hruschka 1991) and were later extended to include predictors, especially marketing variables (Hruschka et al. 1999;Manchanda et al. 1999;Russell and Petersen 2000). But also exploratory approaches evolved parallel with the availability of increasingly large shopping basket databases and computational advances. The applied methodologies include data compression techniques like association rule mining (e.g. Brijs et al. 2004; Kamakura 2012), collaborative filtering (e.g., Mild and Reutterer 2003) or various machine learning approaches (e.g., Reutterer et al. 2006Reutterer et al. , 2007.In our call-for papers we addressed several research gaps in the literature on multi-category purchase behavior which are relevant for this special issue. Of course, we did not expect submitted papers to completely eliminate these gaps. But we think that the papers included in this issue contribute to narrow several of these gaps. Two papers deal with expenditure shares or loyalty segments in contrast to the extant literature with its focus on purchase incidence.

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Analysis of cross category dependence in market basket selection

Cited by 257 publications

References 15 publications

The Role of Big Data and Predictive Analytics in Retailing

The Role of Big Data and Predictive Analytics in Retailing

A Model of Heterogeneous Multicategory Choice for Market Basket Analysis

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