Brand and Product Management

Product discussion boards are a rich source of information about consumer sentiment about products, which is being increasingly exploited. Most sentiment analysis has looked at single products in isolation, but users often compare different products, stating which they like better and why. We present a set of techniques for analyzing how consumers view product markets. Specifically, we extracted relative sentiment analysis and comparisons between products, to understand what attributes users compare products on, and which products they prefer on each dimension.

In this paper, we examine the pattern of correlation among consumer price sensitivities for customer purchase incidence decisions across complementary product categories. We use a hierarchical Bayesian multivariate probit model to uncover this pattern. We estimated this model using purchase incidence data for six categories involving three pairs of complementary products.

In many services (e.g., the wireless service industry), consumers choose a service plan according to their expected consumption. In such situations, consumers experience two forms of uncertainty. First, they may be uncertain about the quality of their service provider and can learn about it after repeated use of the service. Second, they may be uncertain about their own usage of minutes and learn about it after observing their actual consumption.

In recent years, product discussion forums have become a rich environment in which consumers and potential adopters exchange views and information. Researchers and practitioners are starting to extract user sentiment about products from user product reviews. Users often compare different products, stating which they like better and why.

Three experiments investigate the emotions that arise from buying or not buying at an unintended purchase opportunity and how they color evaluations of affective advertising appeals that are viewed subsequently. We demonstrate that buying can cause happiness tempered with guilt, while not buying causes pride. Consistent with the felt affect, respondents who had bought at time 1 subsequently prefer happiness appeals to pride appeals, while those who had refrained prefer pride appeals.

Purpose — The purpose of this paper is to develop a comprehensive model that combines brand knowledge and brand relationship perspectives on brands and shows how knowledge and relationships affect current and future purchases.

Design/methodology/approach — The paper uses structural equation modeling to test the significance of the overall model and the specified paths.

American manufacturers often employ specialized agencies to create and produce advertising campaigns. This paper focuses on a critical juncture in the creation of American advertising: the meeting between the manufacturer (client) and the advertising agency, where advertising ideas are presented, discussed, and selected. Although the participants enter these meetings with the common goal of reaching agreement on the ideas that will be advanced to the next step in the creative development process, the attendees have additional, sometimes conflicting, professional and personal objectives.

Event sponsors often do not receive proper credit for their efforts. This issue was examined in a field study involving over 300 baseball fans attending minor league games during the summer season. Signal detection analyses reveal that, even among such sports fans, the ability to correctly discriminate actual official sponsors of the home team from matched foils, although above chance, was rather poor. Consistent with recent laboratory findings, sponsor identification responses were further found to be heavily influenced by the mere plausibility of the brand as a potential sponsor.

We develop discrete choice models that account for parameter driven preference dynamics. Choice model parameters may change over time because of shifting market conditions or due to changes in attribute levels over time or because of consumer learning. In this paper we show how such preference evolution can be modeled using hierarchial Bayesian state space models of discrete choice. The main feature of our approach is that it allows for the simultaneous incorporation of multiple sources of preference and choice dynamics.