An empirical test of theories of price valuation using a semiparametric approach, reference prices, and accounting for heterogeneity

In this paper we estimate and empirically test different behavioral theories of consumer reference price formation. Two major theories are proposed to model the reference price reaction: assimilation contrast theory and prospect theory. We assume that different consumer segments will use different reference prices. The study builds on earlier research by Kalyanaram and Little (1994); however, in contrast to their work, we use parametric and semiparametric approaches to detect the structure of the underlying data sets. The different models are tested using a program module in GAUSS that was able to account for heterogeneity. The model types were calibrated by a simulation study. The calibrated modules were then used to analyze real market data.price valuation, semiparametric approach, reference prices, heterogeneity

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.market basket analysis, multivariate logit model, brand choice behavior, marketing-mix

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.nested logit model, utility maximization nested logit, nonnormalized nested logit, simulation study

Estimation with the Nested Logit Model: Specifications and Software Particularities

Due to its ability to allow and account for similarities betweenpairs 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 ofsimulation studies with the nested logit model. In simulation studies with the nested logit model using NNNL software (e. g. PROC MDC in SAS(c) ), 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.nested logit model, utility maximization nested logit, non-normalized nested logit, simulation study

"Investigating the Competitive Assumption of Multinomial Logit Models of Brand Choice by Nonparametric Modeling"

The Multinomial Logit (MNL) model is still the only viable option to study nonlinear responsiveness of utility to covariates nonparametrically. This research investigates whether MNL structure of inter-brand competition is a reasonable assumption, so that when the utility function is estimated nonparametrically, the IIA assumption does not bias the result. For this purpose, the authors compare the performance of two comparable nonpara-metric choice models that differ in one aspect: one assumes MNL com-petitive structure and the other infers the pattern of brands' competition nonparametrically from data.

Nonparametric modeling of buying behavior in fast moving consumer goods markets

"From the statistical point of view a nonparametric formulation of a brand choice model (NDE) is a powerful alternative to the logit model. But in the marketing context, researchers in general want to have parameter values to make predictions or to estimate market shares. This leads to a semiparametric model (GAM) formulation with two possible ways of using the results. One is to perform estimation of choice probabilities, but there one is confronted with the same problem as in the nonparametric approach, because no parameters are estimated for the nonparametric part of the model. The second possibility of a semiparametric model formulation overcomes this problem. In addition, with the estimation results a modified parametric model formulation can be estimated. This also gives the possibility to work with the parameter values to estimate market shares or make predictions. Especially for this use of modeling, the underlying data structure should be detected correctly. Therefore, two different estimation algorithms for a GAM were presented and the application of the semiparametric model to a real data set was reported. The estimations were made by the two common algorithms, backfitting and marginal integration, and are compared to each other. An interaction effect in the variable price in the data set was discovered, which leads to the need of additional studies of the data set." (author's abstract

Preventing tourists from canceling in times of crises

Tourism destinations experiencing a crisis are vulnerable to trip cancelations and sudden drops in demand. Little is known about trip cancelations and how to prevent them. Specifically, it is unclear whether the effectiveness of different prevention approaches varies across crises and tourists segments. Using a conjoint design, the present study investigates the comparative stated effectiveness of different prevention approaches in situations where different crises hit a destination. Results indicate that certain prevention actions indeed have the potential to reduce cancelations. The most effective approach is change of accommodation-especially so when combined with an upgrade-followed by information updates and finally the provision of security devices or security staff. The effectiveness of approaches varies across tourists and crises

A decision-support tool for recommending promising categories for targeted promotions

When making marketing mix decisions, marketing managers of companies that offer a broad range of product categories, such as traditional offline and online retailers, mail-order companies, or financial service providers, often need to select one or a few focal categories out of all the possible ones offered. This interest is further fuelled by opportunities offered by the Internet or modern customer loyalty programs using smart card technologies, making it easier as well as cheaper for companies to implement micromarketing strategies. These recent developments have lead to a shift in the managerial requirements of direct marketers: they want to find out which specific products or categories need to be featured in promotional activities customized for specific (groups of) customers. In this study, we present a decisionsupport tool that assists direct marketers in selecting subsets of promising categories from the large assortment they typically offer for inclusion in targeted promotions. The proposed analytical approach combines conventional wisdom of market basket analysis in a novel two-stage procedure (Boztug and Reutterer, 2008). In a first (exploratory) step, jointly purchased product categories across the entire assortment are identified by looking at pronounced cross-category interrelationships in the observed frequency patterns. Customers are next assigned to the identified shopping basket prototypes and we allow them to be members of multiple prototypes. This data-compression step is followed by a second (explanatory) step where the cross-category effects in response to marketing actions are modeled across the pre-selected categories. Our procedure takes both interdependencies in purchase behaviour across categories and customer heterogeneity with respect to cross-category effects in response to marketing actions into account. For calibrating the model we obtained purchase transaction data of a major online grocery retailer for almost 4 year, resulting in a customer base of 17,312 households (purchased at least 3 times in the observation period). For the same retailer and time period, we also have detailed information on price and other important marketing-mix variables. A total number of 302,632 retail transactions with pick-any choices among an assortment of 121 categories are first subject to the data compression step. This first stage revealed 13 interesting and distinct prototypes which were subject to estimation of segmentspecific multivariate MNL models. Currently, we are about to empirically test the resulting recommendations derived from the above suggested two-stage approach vis-à-vis alternative approaches in a controlled field experiment conducted in cooperation with a major online grocery retailer. The experimental setup is projected to consist of a control group (no recommendations) and different experimental groups of which one group will receive recommendations derived by our suggested twostage approach and other groups will receive recommendations coming from other recommender systems that differ in their degree of intelligence. During the conference, we will present the underlying mechanism of our decision-support tool as well as show some preliminary results of its performance

A combined approach for segment-specific market basket analysis

Market baskets arise from consumers shopping trips and include items from multiple categories that are frequentlychosen interdependently from each other. Explanatory models of multicategory choice behavior explicitly allow for suchcategory purchase dependencies. They typically estimate own and across-category effects of marketing-mix variables onpurchase incidences for a predefined set of product categories. Because of analytical restrictions, however, multicategorychoice models can only handle a small number of categories. Hence, for large retail assortments, the issue emerges of howto determine the composition of shopping baskets with a meaningful selection of categories. Traditionally, this is resolvedby managerial intuition. In this article, we combine multicategory choice models with a data-driven approach for basketselection. The proposed procedure also accounts for customer heterogeneity and thus can serve as a viable tool for designingtarget marketing programs. A data compression step first derives a set of basket prototypes which are representative forclasses of market baskets with internally more distinctive (complementary) cross-category interdependencies and areresponsible for the segmentation of households. In a second step, segment-specific cross-category effects are estimatedfor suitably selected categories using a multivariate logistic modeling framework. In an empirical illustration, significantdifferences in cross-effects and price elasticities can be shown both across segments and compared to the aggregate model