This article proposes that managers may use local consumer culture (LCC), orthe culture of one’s home country, in their brand-building activities by adapting thebrand’s positioning to the country image the brand targets. It introduces the concept ofbrand image–country image (BICI)fit, which measures the extent to which consumers in aspecific country perceive a brand image as being congruent with their home country’simage. Using more than 350,000 brand-respondent observations across three countries, wedevelop and empirically illustrate a multiattribute methodology for operationalizing BICIfit and provide robust evidence that BICIfit is positively associated with consumers’brandevaluations. A large number of validity and robustness tests support the proposed BICIfitmetric and thefindings derived from it. For example, wefind that age, education, beingfemale, and need for structure enhance the BICIfit effect, whereas materialism diminishes it.Furthermore, BICIfit matters more in categories that are closely tied to a local culturalcontext or that are characterized by high purchase risk. Given its multiattribute nature, theproposed BICIfit metric identifies concrete image attributes and thereby provides managerswith an effective way to develop or revise LCC positioning plans for their brands.

Find more: https://pubsonline.informs.org/doi/abs/10.1287/mksc.2019.1151?journalCode=mksc

The global importance of online advertising calls for a detailed understanding of consumer-specific responses to online ad repetitions. A key concern for advertisers is not only whether some consumers display degrees of “wearout” but also whether they can surpass a point at which additional exposures have a negative marginal effect: “weariness.” The authors examine a large-scale advertising campaign aimed at driving viewers to a target website, which comprises more than 12,000 users across over 400 websites. These data are analyzed using a flexible discrete mixture specification that accommodates different response shapes over ad stock and timing and parcels ad viewers into response classes based on their internet usage metrics. The resulting classes display varying degrees of wearout, with one subgroup, accounting for about 24% of the sample, evincing weariness. The model also estimates differential publisher effectiveness, with the most effective publisher being nine times more effective than the one 26 places down. The authors demonstrate that the finding of weariness is robust to all the model’s main components, with one key exception: heterogeneity in users’ ad response. Analysis further suggests that an appropriate “profiling and capping” strategy can improve ad deployment by as much as 15% overall for these data.

We develop a hierarchical choice model to account for the choice utility heterogeneity of individual shoppers that belong to the same household. Our model allows us to measure how much variability in purchase behavior exists among individuals in a household, and to compare this to the variability that exists across households. Because of the presence of multiple shoppers from the same household, we also extend the concept of household-level state dependence to consider state dependence at the individual level. We apply our model to five different grocery categories. We find that the intrahousehold heterogeneity in estimated brand intercepts and (to a lesser extent) price sensitivities is about 20%–30% of the interhousehold heterogeneity in these parameters. However, with promotion sensitivities, we find intrahousehold heterogeneity, in most cases, to be as large as interhousehold heterogeneity. Our state dependence results show that past brands purchased by an individual have a much stronger influence on subsequent purchases than those purchased by anyone in the household. We use our estimated utility parameters to compare the expected profitability of promotions targeted at the individual rather than at the household and find substantial (more than 50%) improvements in the incremental revenue of supermarket promotions.

The authors study the role of reference price in a setting in which both the price and the quantity are set through personal interaction during the transaction process, such as in business-to-business markets. Most studies on reference price in the marketing research literature focus on consumer packaged goods, for which prices are typically fixed during the shopping trip and the transaction does not involve personal interaction with a salesperson. In this study, the authors study the effect of reference price on the quantity purchased and also on the pricing outcome of the transaction. They estimate a simultaneous equation system of both pricing and quantity purchased. The findings are as follows: (1) Reference price effects exist on quantity purchased and on the transaction pricing outcome in business-to-business market transactions, (2) business customers react asymmetrically to price increases and price decreases, and (3) salespeople have their own reference prices that affect the transaction price. The authors also find that customer experience with the salesperson might exacerbate the loss aversion effect. They conclude by discussing the underlying reasons behind these findings and their managerial implications.

This paper develops a model that extends the traditional aggregate discrete-choice-based demand model (e.g. Berry et al. 1995) to account for varying levels of product availability. In cases where not all products are available at every consumer shopping trip, the observed market share is a convolution of two factors: consumer preferences and the availability of the product in stores. Failing to account for the varying degree of availability would produce incorrect estimates of the demand parameters. The proposed model uses information on aggregate availability to simulate the potential assortments that consumers may face in a given shopping trip. The model parameters are estimated by simulating potential product assortment vectors by drawing multivariate Bernoulli vectors consistent with the observed aggregate level of availability. The model is applied to the UK chocolate confectionery market, focusing on the convenience store channel. We compare the parameter estimates to those obtained from not accounting for varying availability and analyze some of the substantive implications.