Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.     

Modeling CLV: A test of competing models in the insurance industry

Customer Lifetime Value (CLV) is one of the key metrics in marketing and is considered an important segmentation base. This paper studies the capabilities of a range of models to predict CLV in the insurance industry. The simplest models can be constructed at the customer relationship level, i.e. aggregated across all services. The more complex models focus on the individual services, paying explicit attention to cross buying, but also retention. The models build on a plethora of approaches used in the existing literature and include a status quo model, a Tobit II model, univariate and multivariate choice models, and duration models. For all models, CLV for each customer is computed for a four-year time horizon. We find that the simple models perform well. The more complex models are expected to better capture the richness of relationship development. Surprisingly, this does not lead to substantially better CLV predictions.

Ketchup with your fries? Utilizing complementary product displays to transfer attention to a focal product

Does placing a display of complementary condiments (e.g., ketchup, mustard, buns,etc.) next to a display of hamburgers make shoppers more likely to attend to the hamburgers? Prior work has established that in-store marketing strategies play an important role in drawing attention to products in a retail setting. Building on this research, the present study develops a framework to better understand how shoppers in a natural retail environment attend to displays of complementary products, and how this attention transfers to a focal product. The results suggest that assortment complexity of a display positively affects initial attention capture.. Further, initial attention capture leads shoppers to spend longer evaluating the complementary products in the display, which, in turn, increases evaluation and choice of the focal product. As such, our framework indicates that complementary product displays can be effective at increasing attention to and choice of an associated focal product.     

Incorporating choice dynamics in models of consumer behavior

This paper presents a framework for organizing and discussing factors influencing consumer choice dynamics, how these factors may be incorporated into models of buyer behavior and problems that may arise in estimating such models. The paper identifies research issues and delineates possible approaches. Proceedings of Session on Choice Dynamics at the Banff Symposium on Consumer Decision-Making and Choice Behavior. All authors share equally in content and remaining errors.     

A simple method for estimating preference parameters for individuals

This paper demonstrates a method for estimating logit choice models for small sample data, including single individuals, that is computationally simpler and relies on weaker prior distributional assumptions compared to hierarchical Bayes estimation. Using Monte Carlo simulations and online discrete choice experiments, we show how this method is particularly well suited to estimating values of choice model parameters from small sample choice data, thus opening this area to the application of choice modeling. For larger sample sizes of approximately 100–200 respondents, preference distribution recovery is similar to hierarchical Bayes estimation of mixed logit models for the examples we demonstrate. We discuss three approaches for specifying the conjugate priors required for the method: specifying priors based on existing or projected market shares of products, specifying a flat prior on the choice alternatives in a discrete choice experiment, or adopting an empirical Bayes approach where the prior choice probabilities are taken to be the average choice probabilities observed in a discrete choice experiment. We show that for small sample data, the relative weighting of the prior during estimation is an important consideration, and we present an automated method for selecting the weight based on a predictive scoring rule.     

Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.     

Modeling preference evolution in discrete choice models: A Bayesian state-space approach

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics. We show how the state space approach can include state dependence, unobserved heterogeneity, and more importantly, temporal variability in preferences using a correlated sequence of population distributions. The proposed model is very general and nests commonly used choice models in the literature as special cases. We use Markov chain monte carlo methods for estimating model parameters and apply our methodology to a scanner data set containing household brand choices over an eight-year period. Our analysis indicates that preferences exhibit significant variation over the time-span of the data and that incorporating time-variation in parameters is crucial for appropriate inferences regarding the magnitude and evolution of choice elasticities. We also find that models that ignore time variation in parameters can yield misleading inferences about the impact of causal variables. This paper is based on the first author's doctoral dissertation.     

Estimation bias in choice models with last choice feedback

The study compares two estimation methods for choice models with last choice feedback, using simulated and real data. The first method ignores the impact of unobserved heterogeneity on observed choices via presample choices, while the second method approximates this impact by a stochastic relationship. In panels with less than 10 choices per panelist, the first method overstates the impact of last choice on current choice and understates the impact of intrinsic preferences (i.e., brand intercepts). The second method performs significantly better than the first method. Under both methods, an increase in the number of heterogeneous coefficients in the model tends to increase the bias in the estimates. The largest bias occurs when lagged choice coefficients are heterogeneous.     

Structural Applications of the Discrete Choice Model

A growing body of empirical literature uses structurally-derived economic models to study the nature of competition and to measure explicitly the economic impact of strategic policies. While several approaches have been proposed, the discrete choice demand system has experienced wide usage. The heterogeneous, or mixed, logit in particular has been widely applied due to its parsimonious structure and its ability to capture flexibly substitution patterns for a large number of differentiated products.We outline the derivation of the heterogeneous logit demand system. We then present a number of applications of such models to various data sources. Finally, we conclude with a discussion of directions for future research in this area.     

Overcoming data limitations for store choice modelling.: Exploiting retail chain choice data by means of aggregate logit models

This paper reports on research aimed at exploiting certain data sources for store choice modelling purposes. Many databases, such as some consumer panels, only record the firm chosen by consumers and not the specific store at which they shop. Four alternative approaches are proposed in order to use this raw information for studying patronage determinants at store level: (a) an ordinary logit model in which chain utility is averaged across stores within; (b) an ordinary logit model in which the choice set is assumed to be composed of the nearest store for each chain; (c) a straightforward application of an aggregate logit model; and (d) the application of an aggregate logit model with choice sets spatially bounded by a distance threshold representing the maximum distance that consumers are willing to travel for shopping. The models are empirically tested in the context of spatial choice behaviour. Goodness of fit indicators reveal that only models (b), (c) and (d) acceptably represent competitive interaction dynamics. As performance of (b) is slightly better than that of (c), it seems that a priori the ‘nearest store assumption’ is a better approach than the modelling of aggregate choice structures. However, when the latter approach is applied with more reliable choice sets, as suggested in model (d), the best performance is achieved. The results thus lead us to think that the aggregate logit model is a promising methodology for solving the problem at issue, but subject to an appropriate definition of the consumers’ choice sets. In fact, such an approach provides a more suitable modelling solution to the extent that the saturation and the intra-firm store heterogeneity become more intense, because these situations presumably imply that consideration sets include several stores from the same chain.     

Choice and self: how synchronic and diachronic identity shape choices and decision making

Research on the role of identity in choice varies widely across fields like psychology, philosophy, consumer behavior, and economics, in both the key questions addressed and the methods of investigation. Although a large literature has established how salient aspects of identity affect attitudes and norms, less is known about how beliefs concerning identity are shaped and how these beliefs affect decision making. In this review, we cover recent insights into these issues and summarize some newer, developing approaches to understanding (i) how people judge the persistence of identity, (ii) how beliefs about future changes in identity are formed and how they affect choices, (iii) the formation of beliefs about future changes in identity and how these beliefs affect decisions, and (iv) the historical and economic antecedents of identity norms and their consequences for economic behavior. We introduce a distinction between synchronic and diachronic approaches, and highlight important unresolved questions that will help these fields to more fully understand the role that identity plays in shaping choices.     

Forecasting the World Economy in the Short Term

(1433) Audrone Jakaitiene and Stephane Dees Forecasting the world economy is a difficult task given the complex interrelationships within and across countries. This paper proposes a number of approaches to forecast short‐term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of forecast accuracy and, second, at checking whether methods forecasting directly aggregate variables (direct approaches) outperform methods based on the aggregation of country‐specific forecasts (bottom‐up approaches). Overall, all methods perform better than a simple benchmark for short horizons (up to 3 months ahead). Among the forecasting approaches used, factor models appear to perform the best. Moreover, direct approaches outperform bottom‐up ones for real variables, but not for prices. Finally, when country‐specific forecasts are adjusted to match direct forecasts at the aggregate levels (top‐down approaches), the forecast accuracy is neither improved nor deteriorated (i.e. top‐down and bottom‐up approaches are broadly equivalent in terms of country‐specific forecast accuracy).     

Forecasting U.S. Recessions with Probit Stepwise Regression Models

Yield spreads have been repeatedly used in the literature as the top candidates in predicting future recessions. In this paper, we show that existing model specifications are good but fall short of the performance of more complete models. Applying a probit stepwise regression procedure to a large number of economic indicators, we find models that dramatically outperform those used in the literature. Due to a time series that only began in 1964Q1 and very few historical recessions, any model specification may capture only a few of the economy’s many aspects and thus can potentially be biased. Nevertheless, models with better statistical properties should have a better chance to capture the occurrence of recession. Our chosen models are not immune to statistical limitations but should forecast better than the existing models in the literature.     

A neural network based price sensitive recommender model to predict customer choices based on price effect

The impact of price and price changes should not be ignored while designing algorithms for predicting customer choice. Consumer preferences should be modeled with consideration of price effects. Businesses need to consider for efficient prediction of an individual's purchase behaviour. Personalized recommendation systems have been studied with machine learning algorithms. However, the price-aware personalized recommendation has received little attention. In this paper, we attempt to capture insightful economic results considered in the marketing and economics disciplines by employing modern machine learning architecture for predicting customer choice in a large-scale supermarket context. We extract personalized price sensitivities and examine their importance in consumer behaviour. The employed data collected from a supermarket chain in Germany consists of implicit feedback based on customer-product interactions and the price of every interaction. We propose a two-pathway matrix factorization (2way-MF) model that is price-aware and tries to memorize customer-product interaction's implicit feedback. The proposed models achieve better model performance than standard Matrix Factorization models widely used in the industry. The approach was re-validated with data from supermarket chain in Taiwan. Other industries can adopt the proposed framework of modeling customer's preferences based on price sensitivity. We suggest that further research and analyses could help understand the cross-price elasticities.     

Adapting training to meet the preferred learning styles of different generations

This article considers how training professionals can respond to differences in training preferences between generational groups. It adopts two methods. First, it surveys the existing research and finds generally that preferences for training approaches can differ between groups and specifically that younger employees are perceived to leverage technology more at work. Second, it uses a qualitative grounded theory approach to examine the perspectives of a sample of older and a sample of younger workers in the United States. It finds that both samples perceive a greater comfort level of younger employees in leveraging technology‐enabled training and that the older sample were more comfortable with on‐the‐job and mentorship development efforts than more formal approaches. The implication for practice is that training needs to be more innovative so that, for any particular training intervention, potential trainees have a degree of freedom of choice of learning style. Possible approaches are discussed.     

The impact of personal,market- and product-relevant factors on patronage behaviour in the automobile tyre replacement market

Using a qualitative and quantitative approaches, we investigate the choice behaviour of car owners regarding tyre replacement. In the first part of this study, focus groups are used to draw important insights about sources of tyre replacement and achieve an in-depth understanding of motivations and determinants of consumers’ auto workshop choice. Based on the outcome of the focus groups, we develop and test a conceptual model for car tyre replacement patronage. The behaviour observed with regards to tyre replacement indicates that consumers prefer a one-stop solution for their tyre replacement needs in terms of purchase and affixing of the tyre. Additionally, the mechanic plays a key role in providing information and influencing tyre brand choice of the consumer. Overall, the perceived importance of factors such as price, age, proximity (to tyre shop) and professionalism of service (i.e. service quality) had a direct effect on consumers’ willingness to travel to a tyre shop; while dealer reputation, personal contact (i.e. interpersonal service quality), quick service and gender were found to have mediated effects via dealer trust or brand involvement.     

Towards a brain-to-society systems model of individual choice

Canonical models of rational choice fail to account for many forms of motivated adaptive behaviors, specifically in domains such as food selections. To describe behavior in such emotion- and reward-laden scenarios, researchers have proposed dual-process models that posit competition between a slower, analytic faculty and a fast, impulsive, emotional faculty. In this paper, we examine the assumptions and limitations of these approaches to modeling motivated choice. We argue that models of this form, though intuitively attractive, are biologically implausible. We describe an approach to motivated choice based on sequential sampling process models that can form a solid theoretical bridge between what is known about brain function and environmental influences upon choice. We further suggest that the complex and dynamic relationships between biology, behavior, and environment affecting choice at the individual level must inform aggregate models of consumer choice. Models using agent-based complex systems may further provide a principled way to relate individual and aggregate consumer choices to the aggregate choices made by businesses and social institutions. We coin the term “brain-to-society systems” choice model for this broad integrative approach.     

Asymmetric Perceptions of Ethical Frameworks of Men and Women in Business and Nonbusiness Settings

This paper examines the relationship between individuals' gender and their ethical decision models. The study seeks to identify asymmetries in men's and women's approaches to ethical decision making and differences in their perceptions of how same-sex and other-sex managers would likely act in business and nonbusiness situations that present an ethical dilemma. Results indicate that the models employed by men and women differ in both business and nonbusiness settings, that both sexes report changing models when leaving business settings, and that women were better predictors of both sex's likely ethical models.     

Where to refuel: Modeling on-the-way choice of convenience outlet

For many goods consumers do not make a special trip to a store. Especially for a convenience good such as fuel they will buy the product while on-the-way to some final destination. This paper introduces on-the-way choice of retail outlet as a form of convenience shopping. It presents a model of on-the-way choice of retail outlet and applies the model in the context of fuel retailing to explore its implications for segmentation and spatial competition. The model allows analyzing how choice of retail outlet varies not only with spatio-temporal variables (distance, detour, local competition and agglomeration) but also with trip-related characteristics such as time of day and prior awareness of one's purchase need. The model is a latent class random utility choice model. An application to gas station choices observed in a medium-sized Asian city show the model to fit substantially better than existing models. The empirical results indicate consumers may adopt one of two decision strategies. When adopting an immediacy-oriented strategy they behave in accordance with the traditional gravity-based retail models and tend to choose the most spatially convenient outlet. When following a destination-oriented strategy they focus more on maintaining their overall trip efficiency and so will tend to visit outlets located closer to their main destination and are more susceptible to retail agglomeration effects. The paper demonstrates how the model can be used to inform segmentation and local competition analyses that account for variations in these strategies as well as variations in consumer type, origin and time of travel. Simulations of a duopoly setting further demonstrate the implications.