Knowledge that Transforms

This article examines cross-price promotional effects in a dynamic context. Among other things, we investigate whether previously established findings hold when consumer and competitive dynamics are taken into account. Five main influential effects (asymmetric price effect, neighborhood price effect, asymmetric share effect, neighborhood share effect, and private label versus national brand asymmetry) appear jointly in the second layer of a pooled HB-VEC-VARX model, together with brand- and category-specific variables. This study tests the relative importance of these key factors across three scenarios: with no market dynamics, when only consumer dynamics are considered, and when competitive reactions are also taken into account. The results confirm all five influential effects, even if they are jointly estimated, and consumer and competitive dynamics are taken into account. National brand/private label asymmetry has the strongest influence on the cross-price promotional effects and becomes significantly stronger when consumer and competitive dynamics are taken into account. Dynamic consumer responses and competitive reactions both affect cross-brand price elasticities, and contrary to expectations, competitive reactions accumulate rather than diminish cross-price elasticities. Preemptive switching does occur; i.e., a brand's promotion in period t hurts a competitor's sales in subsequent periods. Our findings are based on an extensive data set. To attain generalizable results, we analyze 33 categories in five stores—that is, 165 store/category combinations.

Consumer new product adoption and preference evolution or learning may be influenced by intrinsic or internal factors (e.g., usage experiences, personal characteristics), external influences (e.g., social effects, media), and marketing activities of the firm. Moreover, the preference evolution in a certain category can spill over to other categories; i.e., consumers can exhibit cross-category learning. In this paper, we develop a multicategory framework to analyze the role of the above elements in the formation and evolution of consumer preferences across categories. We analyze these elements by employing multiple data sets, i.e., by combining revealed preference data (from scanner panel), stated data (from surveys measuring consumer lifestyle variables and demographics), and external influences (e.g., media mentions) in a completely heterogeneous framework while considering other facets of the learning process. By jointly estimating the model for organic purchases in six distinct food categories, we also explore the role of category differences. Results show that consumer new product adoption and learning is indeed impacted significantly and to various degrees by the aforementioned factors. We show how, by selectively encouraging purchases under various scenarios, firms can accelerate the learning process, not only for the focal category but also for other categories, thereby realizing considerable incremental profits. These results can be used by both manufacturers and retailers for more efficient allocation of marketing budgets across (new) products.

We use laboratory experiments to examine the relative performance of the English auction (EA) and the first-price sealed-bid auction (FPA) when procuring a commodity. The mean and variance of prices are lower in the FPA than in the EA. Bids and prices in the EA agree with game-theoretic predictions, but they do not agree in the FPA. To resolve these deviations found in the FPA, we introduce a mixture model with three bidding rules: constant absolute markup, constant percentage markup, and strategic best response. A dynamic specification in which bidders can switch strategies as they gain experience is estimated as a hidden Markov model. Initially, about three quarters of the subjects are strategic bidders, but over time, the number of strategic bidders falls to below 65%. There is a corresponding growth in those who use the constant absolute markup rule.

Firms that serve a large market with many diverse consumer types use discriminatory or nonlinear pricing to extract higher revenue, inducing consumers to separate by self-selecting from a large number of tariff options. But the extent of price discrimination must often be tempered by the high costs of devising and managing discriminatory tariffs, including costs of supporting consumers in understanding and making selection from a complex menu of choices. These tariff design trade-offs occur in many industries where firms face many consumer types and each consumer picks the number of units to consume over time. Examples include wireless communication services, other telecom and information technology products, legal plans, fitness clubs, automobile clubs, parking, healthcare plans, and many services and utilities. This paper evaluates alternative ways to price discriminate while accounting for both revenues and tariff management costs. The revenue-maximizing menu of quantity-price bundles can be very (or infinitely) large and hence not practical. Instead, two-part tariffs (2PTs), which charge a fixed entry fee and a per-unit fee, can extract a large fraction of the optimal revenue with a small menu of choices, and they become more attractive once the costs of tariff management are factored in. We show that three-part tariffs (3PTs), which use an additional instrument, the “free allowance,” are an even more efficient way to price discriminate. A relatively small menu of 3PTs can be more profitable than a menu of 2PTs of any size. This 3PT menu can be designed with less information about consumer preferences relative to the menu of two-part tariffs, which, in order to segment customers optimally, needs fine-grained information about preferences. Our analysis reveals a counterintuitive insight that more-complex tariffs need not always be more profitable; it matters whether the complexity is from many choices or more pricing instruments.

Quantity discount pricing is a common practice used by business-to-business and business-to-consumer companies. A key characteristic of quantity discount pricing is that the marginal price declines with higher purchase quantities. In this paper, we propose a choice-based conjoint model for estimating consumer-level willingness to pay (WTP) for varying quantities of a product and for designing optimal quantity discount pricing schemes. Our model can handle large quantity values and produces WTP estimates that are positive and increasing in quantity at a diminishing rate. In particular, we propose a tractable WTP function that depends on both product attributes and product quantity and that captures diminishing marginal WTP. We show how such a function embeds standard WTP functions in the quantity discount literature as special cases. We also demonstrate how to use the model to estimate the consumer value potential, which is the product of the premium a consumer is willing to pay and her volume potential. Finally, we propose a parsimonious experimental design approach for implementation. We illustrate the model using data from a conjoint study of online movie rental services. The empirical results show that the proposed model has good fit and predictive validity. In addition, we find that marginal WTP in this category decays rapidly with quantity. We also find that the standard choice-based conjoint model results in anomalous WTP distributions with negative WTP values and nondiminishing marginal willingness-to-pay curves. Finally, we identify four segments of consumers that differ in terms of magnitude of WTP and volume potential, and we derive optimal quantity discount schemes for a monopolist and a new entrant in a competitive market.

In recent years academic research has focused on understanding and modeling the survey response process. This paper examines an understudied systematic response tendency in surveys: the extent to which observed responses are subject to state dependence, i.e., response carryover from one item to another independent of specific item content. We develop a statistical model that simultaneously accounts for state dependence, item content, and scale usage heterogeneity. The paper explores how state dependence varies by response category, item characteristics, item sequence, respondent characteristics, and whether it becomes stronger as the survey progresses. Two empirical applications provide evidence of substantial and significant state dependence. We find that the degree of state dependence depends on item characteristics and item sequence, and it varies across individuals and countries. The article demonstrates that ignoring state dependence may affect reliability and predictive validity, and it provides recommendations for survey researchers.

This paper analyzes the competitive role of retail shopping experience in markets with consumer search costs. We examine how a retailer's advantage in providing consumer shopping experience affects its equilibrium pricing and price advertising strategies. We find that if the consumer valuation of a shopping experience is sufficiently low, its effect on retailer strategy is similar to that of quality, and the retailer with the advantage in shopping experience then deploys higher levels of price advertising. On the other hand, when the shopping experience is valuable enough for consumers, it acts akin to price advertising in that it makes it optimal for the retailer with the advantage in shopping experience to eschew price advertising. The optimal competitive investments in consumer shopping experience can be higher than that of a monopoly. The profit impact of shopping experience for a retailer depends on the level of shopping experience: for low levels, the profit impact depends on the difference in the levels between the retailers, but for high enough levels, it depends only on whether the retailer's shopping experience level is higher than that of its competitor. In this case, even small differences in shopping experience levels can result in large differences in equilibrium profits.

When social influence plays a key role in the diffusion of new product, the value of a customer often goes beyond her own product purchase. We posit that a customer's value (CV) comes not only from her purchase value (PV) but also from her influence value (IV) (i.e., CV = PV + IV). Therefore, a customer's value can be far greater than her purchase value if she exerts a considerable influence on others. Building on a two-segment influential–imitator asymmetric influence model, we develop a model framework to derive closed-form expressions for PV, IV, and CV by customer segment as well as time of adoption, and we examine their comparative statics with respect to the diffusion parameters. A key parameter of our model framework is the social apportioning parameter, δ, which determines the credit a customer receives by influencing other potential adopters. We develop an endogenous method for determining δ as a function of the new product diffusion parameters. Our model framework allows us to investigate how a firm might accelerate product purchases by providing introductory discount offers to a targeted group of potential adopters at product launch. We find that purchase acceleration frequently leads to a significant increase in total customer value.

We consider how public firms influence their stock market valuations by timing the introduction of innovative new products. Our focus is on innovation ratchet strategy—firms timing the introduction of innovations in order to demonstrate an improvement in the number of introductions over time. We document that public firms use an innovation ratchet strategy more often than do private firms and that the stock market rewards public firms for doing so. These rewards from the stock market, however, come at the expense of performance in product markets. Specifically, because firms using an innovation ratchet strategy delay some product introductions, they have significantly lower sales growth in the year they ratchet. Finally, we identify firm and market characteristics that influence the likelihood that a public firm will engage in an innovation ratchet strategy.

Firms in a variety of industries offer add-on products to consumers who have previously purchased a base product. We posit that consumers, in making their decisions as to whether to purchase add-ons that complement the base products, find a greater need for the value offered by the add-ons when the “unrecovered” value (i.e., price paid minus the benefits obtained so far) associated with the base products is higher. We conduct experiments that test the proposed hypothesis and examine the strategic implications of such consumer decision making to a firm that sells base product add-on pairs. Consistent with our hypothesis, the experiments show that a consumer's unrecovered value associated with the base product is positively correlated to his likelihood of purchasing the add-on. Formal modeling of this bias shows that firms may find penetration pricing strategies (such as loss leader pricing) suboptimal. Furthermore, the identified bias leads the firm to spend more resources toward enhancing both the base product and the add-on quality, especially so when the add-on will be offered before the consumer has a chance to extensively use the base product. Finally, the effect of competition in the base product market is also considered.