Stetson University

Center for Business Intelligence and Analytics

Research

This site is a web archive of Stetson University's Center for Business Intelligence and Analytics (developed during 2013-2015). The site content is no longer updated.

Research Areas

Social Media Analytics

The use of social media is on the rise. Marketers leverage social media (online reviews, blogs, forums, etc.) to promote products and to appeal to customers. Customers who peruse these social media content are influenced in their purchase decisions due to new information obtained from other shoppers who posted their comments on these media. Similarly, financial institutions develop social media in the forms of forums, blogs, news releases, and financial documents to announce company movements and to create investor communities. Investor decisions are influenced heavily by the opinions expressed in news, company reports, and online forums. This stream of research develops new techniques and methods to harness the potential value of social media and to provide new insights to support decision making.

Consumer Analytics and Sentiment Extraction

New trends in consumer taste, fashion, product popularity, spending propensity, and overall sentiment can often be found in online media, news, and published documents. BI analysts would then be able to segment customers into various groups that reflect not only their demographic characteristics but also their sentiment and preferences. Understanding consumer sentiment from voluminous data presents tremendous opportunities for companies to move ahead of competitors.

Risk, Intelligence and Security Informatics

A vast amount of data have been collected and stored in companies in security and insurance businesses. Developing new predictive analytics methods and testing these methods in these companies could help identify new business opportunities and better serve their customers.

Curriculum and Pedagogical Developments

New skills, concepts, and techniques are increasingly demanded by employers who manage growing volumes of data. Consequently, new curricula and pedagogies are needed to educate students. An important research area is to develop suitable curricula and pedagogies to ensure effective education of next-generation data analytics workers. The NSF-funded Computing in Context Project is part of the effort to develop new curriculum and teaching materials in four computing-related disciplines.

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