Identifying consumer heterogeneity in unobserved categories

Evaluating the impact of social media activities on human brand sales

“…We use the appearance of an artist's song in the publicly available Billboard Hot 100 chart as the criterion for selection into our sample for two reasons. First, this filtering process ensures that artists in our sample meet a threshold of popularity, and that artist heterogeneity does not become the overriding explanation for the variation in social media activity and brand sales (e.g., Blanchard et al 2012). Second, and more important, Billboard Hot 100 chart, unlike most other charts, is broad-based and includes both radio and streaming activity, in addition to weekly sales, and thus provides an accurate depiction of music culture.…”

Mental representation of brands: a schema-based approach to consumers’ organization of market knowledge

“…This creates synergies that may in turn enhance the image of the entire product line or the overall image of the firm (Keller, 2003;Park et al, 1986). The success of strategic decisions, such as brand (re)positioning, brand extensions, and brand communication design is a positive function of how well marketers understand what consumers' know about and what they expect from the brand (Blanchard et al, 2012). As Guido (2001) aptly argues, successful brand management depends on what marketers' know about the pertinent schemata employed by consumers and on their capacity to give their products characteristics that fulfil expectations within the schematic context.…”

Extracting Summary Piles from Sorting Task Data

“…More recently, researchers have also modeled the sorting data directly by making assumptions about the underlying latent categorization process regarding the items being sorted into the same piles (i.e., the piles are the realized behavior of the underlying category structures). DeSarbo, Jedidi, and Johnson (1991) and Blanchard et al (2012) propose latent structure models that identify the unobserved categories that consumers perceive in a set of items, with the assumption that y ijk follows a Bernoulli distribution based on pairwise latent similarity judgments. Note that their procedures cannot accommodate the wide variety of sorting tasks that are not based on a latent pairwise similarity process (e.g., choice context, preferences).…”

“…Their model assumes that these pairwise counts are generated from a pairwise Poisson process with a gamma-distributed rate that is also a function of a latent pairwise similarity judgment between pairs of items. As is true in the research of DeSarbo, Jedidi, and Johnson (1991) and Blanchard et al (2012), the procedures involve conditional maximum likelihood estimates (MLEs) of the parameters through numerical optimization; here, the model's optimization (MLE) computation requirements are quite extensive, even for moderately sized marketing research applications (e.g., 100–150 participants, 20–50 items). Most importantly, these parametric models proposed are not appropriate unless one knows that the piles are formed by a cognitive process relying on pairwise similarity judgments.…”