A Model of Heterogeneous Multicategory Choice for Market Basket Analysis

Dippold, Katrin; Hruschka, Harald

doi:10.1515/roms-2012-0001

Cited by 15 publications

(8 citation statements)

References 66 publications

(83 reference statements)

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“…The evaluation framework introduced by Wang et al (2016) offers a promising starting point for endeavors toward this direction, which we leave open for future research. It also would be beneficial to combine our approach, which is primarily concerned with data compression, with a more predictive approach for modeling customers' multi-category choice decisions (such as the work by Manchanda et al 1999;Dippold and Hruschka 2013;Hruschka 2013). Further promising extensions of our approach could also concentrate on making the segmentation approach dynamic in order to account for changes in customers' purchasing habits.…”

Section: Discussion and Future Researchmentioning

confidence: 99%

A data mining framework for targeted category promotions

Reutterer

Hornik

March³

et al. 2016

J Bus Econ

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This research presents a new approach to derive recommendations for segment-specific, targeted marketing campaigns on the product category level. The proposed methodological framework serves as a decision support tool for customer relationship managers or direct marketers to select attractive product categories for their target marketing efforts, such as segment-specific rewards in loyalty programs, cross-merchandising activities, targeted direct mailings, customized supplements in catalogues, or customized promotions. The proposed methodology requires customers' multi-category purchase histories as input data and proceeds in a stepwise manner. It combines various data compression techniques and integrates an optimization approach which suggests candidate product categories for segment-specific targeted marketing such that cross-category spillover effects for non-promoted categories are maximized. To demonstrate the empirical performance of our proposed procedure, we examine the transactions from a real-world loyalty program of a major grocery retailer. A simple scenario-based analysis using promotion responsiveness reported in previous empirical studies and prior experience by domain experts suggests that targeted promotions might boost profitability between 15 % and 128 % relative to an undifferentiated standard campaign. Keywords Cross-category purchases Á Target marketing Á Customized coupons Á Clustering Á Association rule mining JEL Classification C52 Á C55 Á M3

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Section: Discussion and Future Researchmentioning

confidence: 99%

A data mining framework for targeted category promotions

Reutterer

Hornik

March³

et al. 2016

J Bus Econ

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“…This table also indicates whether observed heterogeneity (by, e.g., socio-demographic variables) or Almost all publications applying multivariate logit models analyze no more than five categories simultaneously and do not use mixture models to account for latent heterogeneity of households (Russell and Petersen 2000;Boztug and Hildebrandt 2008;Boztug and Reutterer 2008). The more recent work of Dippold and Hruschka (2013) is an exception with respect to both the number of categories and the consideration of latent heterogeneity. These authors develop and estimate a finite mixture version of the multivariate logit model.…”

Section: Literature Overviewmentioning

confidence: 99%

Multi-category purchase incidences with marketing cross effects

Hruschka

2016

Rev Manag Sci

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We focus on cross effects of marketing variables and cross category dependences for multi-category decisions which households take during a shopping trip to a retail store. A cross effect is defined as the effect which a marketing variable used for a certain product category exerts on purchases of another category. Using Dirichlet process mixture models with multivariate probit components we analyze purchase incidences of 24,047 shopping visits of a random sample of 1500 households. Independent variables of these models encompass marketing variables for 25 product categories and household attributes. We discuss differences between the two best performing models, a full model which includes both cross effects and cross category dependences, and a related restricted model which ignores cross effects. We obtain several high and significant differences with respect to category constants and cross category dependences between these two models. We also present explanations for the larger (in absolute terms) cross effects of features or displays. We demonstrate that by ignoring cross effects management runs the risk to obtain in many product categories too optimistic forecasts of sales revenue changes due to promotions. In contrast to previous related work suggesting not to use promotions which are not tailored to individual households in any of the investigated categories, we obtain support for such promotions in at least 48 % of the 25 product categories. In addition, based on the full model we demonstrate that often different categories are appropriate for promotions which are targeted at household clusters.

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“…Russell and Petersen (2000), as well as Boztug and Hildebrandt (2008), estimate MVL models without latent heterogeneity for a maximum of four and six categories, respectively. Dippold and Hruschka (2013) analyze 31 categories by one MVL model and account for latent heterogeneity. In a related publication Hruschka (2013) analyzes both purchase incidences and expenditures of individual households in 28 categories by a multivariate tobit (MVT) model, which can be seen as a combination of a multivariate probit and a multivariate regression model.…”

Section: Introductionmentioning

confidence: 99%

Multicategory Purchase Incidence Models for Partitions of Product Categories

Hruschka

2016

Journal of Forecasting

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We analyze multicategory purchases of households by means of heterogeneous multivariate probit models that relate to partitions formed from a total of 25 product categories. We investigate both prior and post hoc partitions. We search model structures by a stochastic algorithm and estimate models by Markov chain Monte Carlo simulation. The best model in terms of cross‐validated log‐likelihood refers to a post hoc partition with two groups; the second‐best model considers all categories as one group. Among prior partitions with at least two category groups a five‐group model performs best. Effects on average basket value differ for the model with five prior category groups from those for the best‐performing model in 40% and 24% of the investigated categories for features and displays, respectively. In addition, the model with five prior category groups also underestimates total sales revenue across all categories by about 28%. Copyright © 2016 John Wiley & Sons, Ltd.

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A Model of Heterogeneous Multicategory Choice for Market Basket Analysis

Cited by 15 publications

References 66 publications

A data mining framework for targeted category promotions

A data mining framework for targeted category promotions

Multi-category purchase incidences with marketing cross effects

Multicategory Purchase Incidence Models for Partitions of Product Categories

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