A brand choice model that incorporates both reference and observed prices is proposed for frequently purchased products. The model is composed of a probability-of-purchase component and a reference-price-formation component. Empirical testing of the model using coffee UPC scanner panel data demonstrates that for two of the three brands, the model predicts probability of purchase better than do standard demand models that utilize only current observed brand prices.

Considerable theoretical justification for consumers' use of psychological reference points exists from the research literature. From a managerial perspective, one of the most important applications of this concept is reference price, an internal standard against which observed prices are compared. In this paper, we propose three empirical generalizations that are well-supported in the marketing literature. First, there is ample evidence that consumers use reference prices in making brand choices. Second, the empirical results on reference pricing also support the generalization that consumers rely on past prices as part of the reference price formation process. Third, consistent with other research on loss aversion, consumers have been found to be more sensitive to “losses,” i.e. observed prices higher than reference prices, than “gains.” We also propose topics for further research on reference prices.reference price, brand choice, decision making

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The authors explore product category and customer characteristics that affect consumers' likelihood of engaging in unplanned purchases. In addition, they examine consumer activities that can exacerbate or limit these effects. The authors employ a hierarchical modeling approach to test their hypotheses using a data set of in-store intercept interviews conducted with 2300 consumers across 28 stores. The results show that category characteristics, such as purchase frequency and displays, and customer characteristics, such as household size and gender, affect in-store decision making. Moreover, although the analysis reveals that the baseline probability of an unplanned purchase is 46%, the contextual factors can drive this probability as high as 93%. The results support the predictions that list use, more frequent trips, limiting the aisles visited, limiting time spent in the store, and paying by cash are effective strategies for decreasing the likelihood of making unplanned purchases.

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