The Effect of Inventory on Purchase Incidence: Empirical Analysis of Opposing Forces of Storage and Consumption

Behavioral studies and recent empirical research suggest higher levels of inventory on hand can lead consumers to increase consumption. Inventory on hand is therefore posited to exert two countervailing forces on the probability of purchase incidence. First, higher levels of inventory reduce the likelihood of purchase as the consumer feels less pressure to buy. At the same time however, theory suggests higher levels of inventory may drive up the rate of consumption, thereby increasing the probability of incidence. We develop an empirical model that explicitly captures these two effects. The elasticity of purchase incidence with respect to inventory derived from the model is shown to capture these opposing forces in a simple and intuitive way. The analytical expression allows calculation of a threshold below (above) which the net effect is positive (negative). The model is estimated on ten product categories from the Stanford Market Basket database and is shown to fit better than both the standard nested logit approach and an alternative formulation developed by Ailawadi and Neslin (1998). The threshold values have plausible magnitudes and are intuitive across categories: butter, margarine and crackers have relatively low thresholds implying that inventory build up does not drive consumption; ice cream and soft drinks have relatively large thresholds (below which the inventory pressure to consume more outweighs the effect to delay purchase). Implications for retail management are discussed. --Choice Models,Consumption,Inventory,Purchase Incidence

A Market Basket Analysis Based on the Multivariate MNL Model

The following research is guided by the hypothesis, that products chosen on a shopping trip in a supermarket are an indicator of the preference interdependencies between different products or brands. The bundle chosen on the trip can be regarded as an indicator of a global utility function. More specific: the existence of such a function implies a cross–category dependence of brand choice behavior. It is hypothesized, that the global utility function related to a product bundle is the result of the marketing–mix of the underlying brands. To investigate the determinants of the choice for a certain bundle, a market basket forecast model is adopted from Russel and Petersen (2000) which uses a multivariate logistic function. The target of this paper is to apply a multivariate logistic approach to estimate a market basket model and to make a comparison between the results of the parameter estimates for a Canadian data set with a German one, which leads to a cross–cultural study. To our knowledge the adoption of this model type to a German data set is shown the first time. The estimation technique is derived from models of spatial statistics and will be explained here in much more detail than in Russel and Petersen (2000). The structure of the chosen product categories allow to discover the impact of certain marketing–mix variables and cross national comparison of market basket choice respectively product bundle buying behavior

A Market Basket Analysis Based on the Multivariate MNL Model

The following research is guided by the hypothesis, that products chosen on a shopping trip in a supermarket are an indicator of the preference interdependencies between different products or brands. The bundle chosen on the trip can be regarded as an indicator of a global utility function. More specific: the existence of such a function implies a cross-category dependence of brand choice behavior. It is hypothesized, that the global utility function related to a product bundle is the result of the marketing-mix of the underlying brands. To investigate the determinants of the choice for a certain bundle, a market basket forecast model is adopted from Russel and Petersen (2000) which uses a multivariate logistic function. The target of this paper is to apply a multivariate logistic approach to estimate a market basket model and to make a comparison between the results of the parameter estimates for a Canadian data set with a German one, which leads to a cross-cultural study. To our knowledge the adoption of this model type to a German data set is shown the first time. The estimation technique is derived from models of spatial statistics and will be explained here in much more detail than in Russel and Petersen (2000). The structure of the chosen product categories allow to discover the impact of certain marketing-mix variables and cross national comparison of market basket choice respectively product bundle buying behavior

A Combined Approach for Segment-Specific Analysis of Market Basket Data

There are two main research traditions for analyzing market basket data that exist more or less independently from each other, namely exploratory and explanatory model types. Exploratory approaches are restricted to the task of discovering cross-category interrelationships and provide marketing managers with only very limited recommendations regarding decision making. The latter type of models mainly focus on estimating the effects of category-level marketing mix variables on purchase incidences assuming cross-category dependencies. We propose a procedure that combines these two modeling approaches in a novel two-stage procedure for analyzing cross-category effects based on shopping basket data: In a data compression step we first derive a set of market basketprototypes and generate segments of households with internally more distinctive(complementary) cross-category interdependencies. Utilizing the information oncategories that are most responsible for prototype construction, segment-specificmultivariate logistic models are estimated in a second step. Based on the data-driven way of basket construction, we can show significant differences in cross-effects and related price elasticities both across segments and compared to the global (segment-unspecific) model

The Effect of Inventory on Purchase Incidence: Empirical Analysis of Opposing Forces of Storage and Consumption

Behavioral studies and recent empirical research suggest higher levels of inventory on hand can lead consumers to increase consumption. Inventory on hand is therefore posited to exert two countervailing forces on the probability of purchase incidence. First, higher levels of inventory reduce the likelihood of purchase as the consumer feels less pressure to buy. At the same time however, theory suggests higher levels of inventory may drive up the rate of consumption, thereby increasing the probability of incidence. We develop an empirical model that explicitly captures these two effects. The elasticity of purchase incidence with respect to inventory derived from the model is shown to capture these opposing forces in a simple and intuitive way. The analytical expression allows calculation of a threshold below (above) which the net effect is positive (negative). The model is estimated on ten product categories from the Stanford Market Basket database and is shown to fit better than both the standard nested logit approach and an alternative formulation developed by Ailawadi and Neslin (1998). The threshold values have plausible magnitudes and are intuitive across categories: butter, margarine and crackers have relatively low thresholds implying that inventory build up does not drive consumption; ice cream and soft drinks have relatively large thresholds (below which the inventory pressure to consume more outweighs the effect to delay purchase). Implications for retail management are discussed

Estimation with the nested logit model: specifications and software particularities

Due to its ability to allow and account for similarities between pairs of alternatives, the nested logit model is increasingly used in practical applications. However the fact that there are two different specifications of the nested logit model has not received adequate attention. The utility maximization nested logit (UMNL) model and the non-normalized nested logit (NNNL) model have different properties, influencing the estimation results in a different manner. As the NNNL specification is not consistent with random utility theory (RUT), the UMNL form is preferred. This article introduces distinct specifications of the nested logit model and indicates particularities arising from model estimation. Additionally, it demonstrates the performance of simulation studies with the nested logit model. In simulation studies with the nested logit model using NNNL software (e. g. PROC MDC in SAS), it must be pointed out that the simulation of the utility function's error terms needs to assume RUT-conformity. But as the NNNL specification is not consistent with RUT, the input parameters cannot be reproduced without imposing restrictions. The effects of using various software packages on the estimation results of a nested logit model are shown on the basis of a simulation study

Estimation with the nested logit model: specifications and software particularities

The paper discusses the nested logit model for choices between a set of mutually exclusive alternatives (e.g. brand choice, strategy decisions, modes of transportation, etc.). Due to the ability of the nested logit model to allow and account for similarities between pairs of alternatives, the model has become very popular for the empirical analysis of choice decisions. However the fact that there are two different specifications of the nested logit model (with different outcomes) has not received adequate attention. The utility maximization nested logit (UMNL) model and the non-normalized nested logit (NNNL) model have different properties, influencing the estimation results in a different manner. This paper introduces distinct specifications of the nested logit model and indicates particularities arising from model estimation. The effects of using various software packages on the estimation results of a nested logit model are shown using simulated data sets for an artificial decision situation

Nicht- und semiparametrische Markenwahlmodelle im Marketing

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A Market Basket Analysis Conducted with a Multivariate Logit Model

The following research is guided by the hypothesis that products chosen on a shopping trip in a supermarket can indicate the preference interdependencies between different products or brands. The bundle chosen on the trip can be regarded as the result of a global utility function. More specifically: the existence of such a function implies a cross-category dependence of brand choice behavior. It is hypothesized that the global utility function related to a product bundle results from the marketing-mix of the underlying brands. Several approaches exist to describe the choice of specific categories from a set of many alternatives. The models are discussed in brief; the multivariate logit approach is used to estimate a model with a German data set