Active Versus Passive Loyalty: A Structural Model of Consideration Set Formation

Abstract

We offer an econometric framework that models a consumer's brand choice decision as a two-stage process: consideration set formation followed by brand selection from the brands in the consideration set. The proposed structural model of consideration set is motivated by the fact that consumers have limited information-acquisition ability. In the context of frequently purchased products (FPPs), since these product categories are characterized by frequent price promotion of varying depths of discount, a consumer faces significant uncertainty about the net utilities associated with the different brands. Thus, while a consumer might know the range of potential prices, she is unaware of the actual posted price of a brand on any given purchase occasion unless she engages in pre-evaluation price search. Since information acquisition is costly, she needs to first decide how many and which brands to search the posted prices of. In the proposed framework, the process of pre-evaluation price search is conceptualized as "brand consideration" and the set of sampled brands is referred to as the consumer's "consideration set".

A distinctive feature of the proposed specification is that it allows us to distinguish between two sources of state dependence - viz., passive and active brand loyalty. In this conceptualization, passive brand loyalty refers to state dependence arising out of "consumer lock-in" as a result of high search cost. Thus, a passively loyal consumer repeatedly buys the same brand over successive purchase occasions because her cost of searching the posted prices of other brands is very high i.e. her optimal consideration set does not include any other brand. In contrast, active brand loyalty refers to state dependence arising out of a high intrinsic preference for the selected brand. Thus, while an actively loyal consumer considers more than the selected brand on any purchase occasion (because her search costs are low), she nonetheless buys the same brand because of her high intrinsic preference for the brand. Our key theoretical results are as follows: (i) relative to low price sensitive consumers, high price sensitive consumers have larger consideration sets; (ii) the intensity of consumer search is higher in product categories characterized by greater price variability. Thus, more frequent price promotions with deep discounts lead to large consideration sets; and, (iii) a consumer does not stay in the state of inertia for long. In the case of a passively loyal consumer, we will observe phases of inertia followed by a brand switching that in turn is followed by another spell of inertia.

We use scanner panel data for liquid detergents to empirically validate the model. The key empirical results are as follows: (i) there are significant search costs that consumers incur in discovering the actual posted prices of the brands at the store. This implies that consumers do not consider (i.e. search the posted prices of) all the brands on a shopping trip; (ii) in-store display activities and feature ads reduce consumer search costs for a brand thereby significantly increasing the probability of the brand being considered. Feature advertising reduces the search costs more than in-store displays; (iii) prior consumption influence quality perception of a brand for both liquid detergents and ketchup categories. However, consumption experience yield only limited additional quality information; and, (iv) estimates of price sensitivity critically depend on whether the specification explicitly models consideration stage. In particular, a model that assumes that consumers search all the brands on all purchase occasions seriously underestimates the impact of price on brand choice decision. We also conduct a limited cross-category analysis using ketchup data set and find several interesting differences in consumer price search behavior across the two product categories analyzed.

Keywords: Consumer Inertia, Consideration Set, Brand Loyalty, Consumer Learning, Bayesian Updating, Structural Model