Computing, Artificial Intelligence and Information TechnologyModeling consideration sets and brand choice using artificial neural networks
Introduction
The increasing amount of household-specific scanner data allows marketing researchers to give better explanations and make better predictions of consumer behavior. An important topic in marketing is modeling brand choice. Numerous models have been developed and occasionally used in practice. Many of these brand choice models are based on a multinomial logit model, MNL (McFadden, 1973; Guadagni and Little, 1983; Lattin and Bucklin, 1989; among others) or a multinomial probit model (Hausman and Wise, 1978; Daganzo, 1979).
A key property of these models is that households are assumed to consider the full set of available brands on each purchase occasion. However, this assumption may not be realistic as households may first reduce the number of brands to a smaller set, before making a final choice. This intermediate set, called the consideration set, thus contains a subset of available brands that a household considers to buy. The concept of consideration sets implies that the process of making a decision concerning brand choice is a two-step process. In the first step there is a reduction of the set of available brands into a consideration set. In the second step the final choice is made from this reduced set. It is important to note here that consideration sets are usually not observed. Hence, somehow one has to estimate these from observed purchase data.
In this paper, we present an econometric model for the above discussed two-stage process of brand choice, that is, the model incorporates the formation of consideration sets. Interestingly, the model we propose for brand choice turns out to have the structure of an artificial neural network (ANN).
ANNs are often used in economics and business applications. ANNs are used to describe and forecast many important variables. An important feature of ANNs is that they can be fitted to a wide range of data patterns, see also Kuan and White (1994) and Swanson and White (1995). For example, they have been used to predict bankruptcy (Zhang et al., 1999) and to highlight structural changes in time series data (Franses and Draisma, 1997; Franses and van Dijk, 2000). We refer to Vellido et al. (1999) for a survey of applications of ANNs in business. Although ANNs are valuable tools for empirical modeling, a well-known drawback of ANNs concerns the interpretation of the model parameters.
Applications of ANNs for marketing problems can be found in Hruschka (1993) and Agrawal and Schorling (1996), among others. More specific, applications of ANNs for brand choice modeling can be found in Hu et al. (1999), Heimel et al. (1998) and, more recently, Hruschka et al. (2002). West et al. (1997) compare an ANN with discriminant analysis and logistic regression. They conclude that an ANN can outperform the two statistical techniques on predicting consumer choice when the underlying choice rule is known and can give better out-of-sample forecasts when the choice rule is not known. Dasgupta et al. (1994) and Kumar et al. (1995) compare the same two statistical techniques with an ANN.
The novelty of this paper is that we address the interpretation aspects of an ANN by demonstrating that such a model can naturally arise from a two-step brand choice model involving consideration sets. This is achieved by assuming that the consideration set corresponds with the hidden layer of the network. We illustrate our model for the choice between six liquid detergent brands.
The outline of this paper is as follows. In Section 2, we present a further but brief elaboration of the concept of consideration sets. In Section 3 we propose our model, discuss how it can be evaluated and how the parameters can be estimated. In Section 4, we illustrate our model. Finally, we conclude our paper with a discussion.
Section snippets
Consideration sets
The assumption that households can reduce the total number of alternative brands into a consideration set before making a final choice has recently raised much interest in theoretical and empirical marketing research. The theory of consideration sets assumes that consumers reduce the number of alternatives from an overall set (or universal set) into smaller sets. The consideration set is the final set before the purchase decision, containing only those products the household is considering to
Consideration sets, brand choice and a neural network
As discussed in the previous section, a two-stage model for brand choice might be more appropriate than a one-stage model. In this section we present a two-stage model, which is based on the decision making process of households and which assumes consideration set formation. In 3.1 Graphical representation, 3.2 Representation, we indicate that this model has strong similarities with an ANN. This is because we assume that the unobserved hidden layer in an ANN corresponds with such a
Illustration
To illustrate the empirical usefulness of our model for a two-stage brand choice process, we demonstrate it on scanner data for six liquid detergent brands. These data are also used in Chintagunta and Prasad (1998) and can be freely downloaded.
Discussion
Considering the decision making process (mainly brand choice) as a two-stage process seems to be more appropriate than imposing that a brand is selected in one step. To describe these two stages, we proposed an econometric model, which appeared to be an ANN. The first stage, that is, the reduction of available brands into unobserved consideration sets, was transformed into a stage where for each brand the probability of consideration is determined. This resulted in the interpretation of the
Acknowledgements
We thank the editor Jyrki Wallenius, four anonymous reviewers, Bas Donkers, Dennis Fok and Richard Paap for their valuable comments and suggestions.
References (34)
- et al.
Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model
Journal of Retailing
(1996) - et al.
Comparing the predictive performance of a neural network model with some traditional market response models
International Journal of Forecasting
(1994) - et al.
Recognizing changing seasonal patterns using artificial neural networks
Journal of Econometrics
(1997) - et al.
What is the role of consideration sets in choice modelling?
International Journal of Research in Marketing
(1995) Determining market response functions by neural network modeling: A comparison to econometric techniques
European Journal of Operational Research
(1993)- et al.
Estimation of posterior probabilities of consumer situational choices with neural network classifiers
International Journal of Research in Marketing
(1999) - et al.
Two-stage discrete choice models for scanner panel data: An assessment of process and assumptions
European Journal of Operational Research
(1998) - et al.
Neural networks in business: A survey of applications (1992–1998)
Expert Systems with Applications
(1999) - et al.
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
European Journal of Operational Research
(1999) - et al.
Simulation experiments in choice simplification: The effects of task and context on forecasting performance
Journal of Marketing Research
(1998)
Studying consideration effects in empirical choice models using scanner panel data
Journal of Marketing Research
Neural Networks for Pattern Recognition
Limited choice sets, local price response and implied measures of price competition
Journal of Marketing Research
Markov chain monte carlo and models of consideration set and parameter heterogeneity
Journal of Econometrics
An empirical investigation of the dynamic “mcfadden model” of purchase timing and brand choice: Implications for market structure
Journal of Business and Economic Statistics
Multinomial Probit: The Theory and its Applications to Demand Forecasting
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2006, Journal of Retailing and Consumer ServicesArtificial neural networks and discrete choice models: Sales forecast in supermarket products
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2017, Innovative Research Methodologies in Management: Volume I: Philosophy, Measurement and ModellingApplication of a hybrid system of probabilistic neural networks and artificial bee colony algorithm for prediction of brand share in the market
2016, Industrial Engineering and Management SystemsModeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model
2010, International Journal of Computational Intelligence Systems
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