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.
Standard preference models in consumer research assume that people weigh and add all attributes of the available options to derive a decision, while there is growing evidence for the use of simplifying heuristics. Recently, a greedoid algorithm has been developed (Yee, Dahan, Hauser & Orlin, 2007; Kohli & Jedidi, 2007) to model lexicographic heuristics from preference data. We compare predictive accuracies of the greedoid approach and standard conjoint analysis in an online study with a rating and a ranking task. The lexicographic model derived from the greedoid algorithm was better at predicting ranking compared to rating data, but overall, it achieved lower predictive accuracy for hold-out data than the compensatory model estimated by conjoint analysis. However, a considerable minority of participants was better predicted by lexicographic strategies. We conclude that the new algorithm will not replace standard tools for analyzing preferences, but can boost the study of situational and individual differences in preferential choice processes.
Purpose -The purpose of this paper is to determine sales drivers for different OTC product categories. Design/methodology/approach -The study uses data from both consumer and retail panels, which are gathered for various product categories. These long-term data are analyzed per product category with two specific regression models, mainly time-series analysis with VAR models and Shapley value regression. Findings -It is found that purchase intention drives sales a lot in general. Besides, it is very important to distinguish seasonal vs non-seasonal markets. The trend coefficient, which implies the stage of maturity of the market, indicates more or less saturated markets for the examples. The proposed models can be easily applied to different OTC categories without a lot of customization.Research limitations/implications -The study does not take into account different outlets (e.g. online, supermarkets) and does not estimate interaction effects between the single drivers. Practical implications -The paper provides the market researcher with a guideline on how to proceed to model OTC product categories, e.g. which data are to be used, which models are to be estimated, which conclusions can be drawn. Originality/value -The study develops an analysis approach which is readily applicable to different OTC product categories, which exhibit very distinct market characteristics. The advantage of this approach is that it applies a standardized tool kit of methods to analyze highly varying markets.
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