Encyclopedia of Big Data

Living Edition
| Editors: Laurie A. Schintler, Connie L. McNeely

Behavioral Analytics

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32001-4_18-1

Behavioral analytics can be conceptualized as a process involving the analysis of large datasets comprised of behavioral data in order to extract behavioral insights. This definition encompasses three goals of behavioral analytics intended to generate behavioral insights for the purposes of improving organizational performance and decision-making as well as increasing understanding of users. Coinciding with the rise of big data and the development of data mining techniques, a variety of fields stand to benefit from the emergence of behavioral analytics and its implications. Although there exists some controversy regarding the use of behavioral analytics, it has much to offer organizations and businesses that are willing to explore its integration into their models.

Definition

The concept of behavioral analytics has been defined by Montibeller and Durbach as an analytical process of extracting behavioral insights from datasets containing behavioral data. This definition is derived from...

Keywords

Game Development Behavioral Analytic Data Mining Technique Business Analytic Game Industry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Further Readings

  1. Bollen, J., Mao, H., & Pepe, A. (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. Proceedings of the Fifth International Association for Advancement of Artificial Intelligence Conference on Weblogs and Social Media.Google Scholar
  2. Brown, G. M. (2007). Use of kohonen self-organizing maps and behavioral analytics to identify cross-border smuggling activity. Proceedings of the World Congress on Engineering and Computer Science.Google Scholar
  3. Carneiro, H. A., & Mylonakis, E. (2009). Google trends: A web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49(10).Google Scholar
  4. Davenport, T., & Harris, J. (2007). Competing on analytics: The new science of winning. Boston: Harvard Business School Press.Google Scholar
  5. Drachen, A., Sifa, R., Bauckhage, C., & Thurau, C. (2012). Guns, swords and data: Clustering of player behavior in computer games in the wild. Proceedings of the IEEE Computational Intelligence and Games.Google Scholar
  6. El-Nasr, M. S., Drachen, A., & Canossa, A. (2013). Game analytics: Maximizing the value of player data. New York: Springer Publishers.Google Scholar
  7. Fields, T. (2011). Social game design: Monetization methods and mechanics. Boca Raton: Taylor & Francis.Google Scholar
  8. Fox, P., & Hendler, J. (2011). Changing the equation on scientific data visualization. Science, 331(6018).Google Scholar
  9. Géczy, P., Izumi, N., Shotaro, A., & Hasida, K. (2008). Toward user-centric management of organizational information systems. Proceedings of the Knowledge Management International Conference, Langkawi, Malaysia (pp. 282-286).Google Scholar
  10. Kohavi, R., Rothleder, N., & Simoudis, E. (2002). Emerging trends in business analytics. Communications of the ACM, 45(8).Google Scholar
  11. Mitchell, T. M. (2009). Computer science: Mining our reality. Science, 326(5960).Google Scholar
  12. Montibeller, G., & Durbach, I. (2013). Behavioral analytics: A framework for exploring judgments and choices in large data sets. Working Paper LSE OR13.137. ISSN 2041-4668.Google Scholar
  13. Negash, S., & Gray, P. (2008). Business intelligence. Berlin/Heidelberg: Springer.Google Scholar
  14. Sifa, R., Drachen, A., Bauckhage, C., Thurau, C., & Canossa, A. (2013). Behavior evolution in tomb raider underworld. Proceedings of the IEEE Computational Intelligence and Games.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of CommunicationSan Diego State UniversitySan DiegoUSA