Business Analytics

Logos serve a fundamental role as the visual figureheads of brands. Yet, because of the difficulty of using unstructured image data, prior research on logo design has largely been limited to nonquantitative studies. In this work, we explore the interplay between logo design and brand identity creation from a data-driven perspective.

Smartphones have made it nearly effortless to share images of branded experiences. This research classifies social media brand imagery and studies user response. Aside from packshots (standalone product images), two types of brand-related selfie images appear online: consumer selfies (featuring brands and consumers’ faces) and an emerging phenomenon the authors term “brand selfies” (invisible consumers holding a branded product).

We develop a flexible content-based search model that links the content preferences of search engine users to query search volume and click-through rates, while allowing content preferences to vary systematically based on the context of a search. Content preferences are defined over latent topics that describe the content of search queries and search result descriptions.

Marketing is the functional area primarily responsible for driving the organic growth of a firm. In the age of digital marketing and big data, marketers are inundated with increasingly rich data from an ever-expanding array of sources. Such data may help marketers generate insights about customers and competitors. One fundamental question remains: How can marketers wrestle massive flows of existing and nascent data resources into coherent, effective growth strategies?

In this research, we investigate the prevalence, robustness and possible reasons underlying the polarity of online review distributions with the majority of the reviews at the positive end of the rating scale, a few reviews in the mid-range and some reviews at the negative end of the scale.

Submitting queries to search engines has become a major way for consumers to search for information and products. The massive amount of search query data available today has the potential to provide valuable information on consumer preferences. In order to unlock this potential, it is necessary to understand how consumers translate their preferences into search queries. Strategic consumers should attempt to maximize the information content of the search results, conditional on a set of beliefs on how the search engine operates.

The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant ability to predict whether borrowers will pay back the loan above and beyond the financial and demographic variables commonly used in models predicting default.

We investigate the increasingly common business setting in which companies face the possibility of both observed and unobserved customer attrition (i.e., "overt" and "silent" churn) in the same pool of customers. This is the case for many online-based services where customers have the choice to stop interacting with the firm either by formally terminating the relationship (e.g., canceling their account) or by simply ignoring all communications coming from the firm.

The rise of "Big Data" had a big impact on marketing research and practice. In this article, we first highlight sources of useful consumer information that are now available at large scale and very little or no cost. We subsequently discuss how this information — with the help of new analytical techniques — can be translated into valuable insights on consumers' psychological states and traits that can, in turn, be used to inform marketing strategy.