Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets

Abstract

We test methods, based on cognitively-simple decision rules, that predict which products consumers select for their consideration sets. Drawing on qualitative research we propose disjunctions-of-conjunctions (DOC) decision rules that generalize well-studied decision models such as disjunctive, conjunctive, lexicographic, and subset conjunctive rules. We propose two machine-learning methods to estimate cognitively-simple DOC rules. We observe consumers' consideration sets for global positioning systems for both calibration and validation data. We compare the proposed methods to both machine-learning and hierarchical-Bayes methods each based on five extant compensatory and non-compensatory rules. On validation data the cognitively-simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates; significantly so in all but one test. An additive machinelearning model comes close on hit rate. Our results are robust with respect to format by which consideration is measured (four formats tested), sample (German representative vs. US student), and presentation of profiles (pictures vs. text). We close by illustrating how DOC-based rules can affect managerial decisions. Keywords:Consideration sets, non-compensatory decisions, consumer heuristics, statistical learning, machine learning, revealed preference, conjoint analysis, cognitive complexity, cognitive simplicity, environmental regularity, lexicography, logical analysis of data, decision trees, combinatorial optimization. Two-stage, consider-then-choose decision rules are particularly relevant in the automobile market, but modeling and forecasting such decision rules is of general interest. When consumers face a large number of alternative products, as is increasingly common in today's retail and web-based shopping environments, they typically screen the full set of products down to a smaller, more-manageable consideration set which they evaluate further (e.g., Bronnenberg and Vanhonacker 1996;DeSarbo et al., 1996; Hauser and Wernerfelt 1990; Jedidi, Kohli and DeSarbo, 1996;Mehta, Rajiv, and Srinivasan, 2003;Montgomery and Svenson 1976;Payne 1976;Roberts and Lattin, 1991;Shocker et al., 1991;Wu and Rangaswamy 2003). Consideration sets for packaged goods are typically 3-4 products rather than the 30-40 products on the market (Hauser and Wernerfelt 1990;Urban and Hauser 2004). Forecasting consideration sets can explain roughly 80% of the explainable uncertainty in consumer decision making (assuming equally likely choice within the consideration set, Hauser 1978). In complex product categories research suggests that at least some consumers use non-compensatory decision processes when evaluating 3 many products and/or products with many features (e.g., Payne, Johnson 1988, 1993). 2 CONSIDERATION SETS AND DECISION RULESIn this paper we explore machine-learning algorithms based on non-compensatory decision rules that model decisions by consumers in the consideration stage of a consider-thenchoo...

A Network‐Based Approach to Modeling and Predicting Product Coconsideration Relations

“…The decision context and a large amount of uncertainty alter decision rules. Existing literature primarily focuses on inferring decisionrule heuristics [20][21][22], such as the cognitive simplicity rule [23], which has been shown to be effective in automobile and web-based purchasing. There are three approaches to uncover consideration decision-rule heuristics [15].…”

Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning

“…Therefore, marketing researchers have started to embrace machine learning methods for estimation of classic marketing problems in recent years (e.g., Cui and Curry 2005;Evgeniou et al 2005;Evgeniou et al 2007;Hauser et al 2010). …”

“…Specifically, our algorithm aims to uncover a set of partworths estimates that are consistent with consumer's answers to these questions. Historically, researchers have often used conjunctive rules to capture consumer's decision rules in the consideration process (e.g., Hauser et al 2010;Dzyabura and Hauser 2011). And it has been less common to use part-worths to characterize consumer's responses to consideration questions.…”

“…In the literature, conjunctivelike criteria are often used to examine answers to consideration questions (e.g., Bettman 1970;Hauser et al 2010). Therefore, we carry out synthetic data experiments to examine the performance of the proposed question selection algorithm for consideration questions when the true consideration model is conjunctive.…”

Publicidad en niños y conjuntos de consideración de marca basados en la memoria

“…Es un acuerdo en la literatura que la toma de decisión de compra se trata de un proceso evaluativo de atributos desagregado en secuencias (Hauser y Wernerfelt, 1990;Roberts y Lattin, 1991;Hauser, et al, 2010).…”