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Review of Marketing Science


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A Model of Heterogeneous Multicategory Choice for Market Basket Analysis

Katrin Dippold / Harald Hruschka
Published Online: 2013-09-28 | DOI: https://doi.org/10.1515/roms-2012-0001

Abstract

Based on market basket data, using multicategory purchase incidence models, we analyze demand interdependencies between product categories. We propose a finite mixture multivariate logit model to derive segment-specific intercategory effects of market basket purchase. Under the assumption that only a fraction of intercategory effects are significant, we exclude irrelevant effects by variable selection. This leads to a detailed description of consumers’ shopping behavior that varies over segments not only with respect to (w.r.t.) parameters’ values but also w.r.t. included interaction effects. As the high number of product categories in the model prohibits exact maximum likelihood estimation, we adopt pseudo-likelihood estimation. We apply our model to a data set with 31 product categories and 1,794 households purchasing 17,280 baskets in one store. The best fitting model is determined by predictive model selection. We find that a homogeneous model would overestimate the intensity of interaction between product categories.

Keywords: finite mixture model; market basket analysis; variable selection; multivariate logistic regression; pseudo-likelihood estimation

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About the article

Published Online: 2013-09-28


Examples for this approach which deal with another multicategory problem by focusing on the covariance of coefficients across categories are the probit models of Ainslie and Rossi (1998) and Seetharaman, Ainslie, and Chintagunta (1999) or the logit models of Hansen, Singh, and Chintagunta (2006) and Singh, Hansen, and Gupta (2005). In the latter two studies, a factor analytic structure is imposed on the covariance matrix of coefficients.

There has been a long discussion on the performance of finite vs continuous mixture models (e.g. Wedel et al. 1999; for a short summary, see Varki and Chintagunta 2004). Whereas Allenby, Arora, and Ginter (1998) and Allenby and Rossi (1999) argue that FM models do not sufficiently represent consumer heterogeneity, especially when the number of segments is small and complete homogeneity within a segment is an unrealistic assumption, Andrews, Ainslie, and Currim (2002) do not find any performance superiority of the continuous over the FM models. Even for a very limited number of segments (one to three), the continuous and the discrete model recover parameters and forecast holdout data equally well. Additionally, Wedel and Kamakura (1998) and Wedel et al. (1999) stress the consistency of the FM model with the way management thinks about consumers in segments.

Segment is the marketing interpretation of a component in a FM model. Therefore, the terms segment and component are used interchangeably in the text.

In contrast to probit models, there is no biasing effect of joint non-purchase that would be the most frequently occurring event. We also remark that we follow the cross-category effect definition by Hruschka, Lukanowicz, and Buchta (1999). In contrast to Russell and Petersen (2000), cross-category effects do not depend on a household’s typical basket size. This modeling decision is justified, because (1) the inclusion of basket size resulted only in a weak improvement of the LL value for holdout data in the RP model and (2) our model already accounts for interaction effect variability by estimating different effects for different segments.

We thank one anonymous reviewer who suggested to discuss this issue and drew our attention to the latent variable interpretation of co-incidence. We also thank the editor for suggesting relevant references.

With , Z has elements, that is, all possible market baskets. Huang and Ogata (2002) observe exponents between 9 and 15 to be the limit of computation.

The general disadvantage of wrong standard errors can be easily adjusted for, as correct standard errors can be computed with bootstrapping (e.g. Efron and Tibshirani 1998).

This two-step approach is conventionally used in FM models for multicategory choice (e.g. Song and Chintagunta 2007).

See, for example, Andrews and Currim (2002) for a complete tabulation including formulas for calculations.

We argue that the homogeneous model smoothes interaction effects. The lower number of interaction effects included for the heterogeneous model might also contribute to such results. Boztuğ and Reutterer (2008) formulate a similar hypothesis, though they motivate it differently. Chib, Seetharaman, and Strijnev (2002) present the opposite effect.

Our model specification does not include RP’s category-specific household variables time since last category purchase (TIME) and loyalty (LOYAL). As this model is estimated over the purchases within one shop only neglecting purchases in other stores, we do not have complete information on a consumer’s shopping history. Therefore, the values of TIME and LOYAL would not be meaningful. Besides, we already account for heterogeneity with the FM model and do not need auxiliary measures of consumer diversity.

Test runs showed that the model parameters and especially the household assignment to segments stabilize quickly.

For reasons of comparability, PLL is also calculated for the independence model whose parameters are estimated by ML.

We only consider interaction effects larger than 0.001 in absolute size.

We thank one anonymous reviewer who recommended to compare the results of our model to those obtained by a correlational analysis.

Household income in thousand US$.

HS: high school; C: college; TS: technical school; PG: postgraduate work. SC: some college, what means that the person left college without a degree.

For model stability, variable selection is not applied to category constants or marketing-mix coefficients.


Citation Information: Review of Marketing Science, Volume 11, Issue 1, Pages 1–31, ISSN (Online) 1546-5616, ISSN (Print) 2194-5985, DOI: https://doi.org/10.1515/roms-2012-0001.

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