Advertisement

Big Consumer Behavior Data and their Analytics: Some Challenges and Solutions

  • Mihai CalciuEmail author
  • Jean-Louis Moulins
  • Francis Salerno
Conference paper
Part of the Developments in Marketing Science: Proceedings of the Academy of Marketing Science book series (DMSPAMS)

Abstract

This chapter contributes to the still very reduced marketing literature that deals with big consumer behavior data using cloud analytics by summarizing some of the main extant academic researches and by introducing new applications, datasets, and technologies in order to complete the picture. Both internal “purchase history” and external Web-based customer reviews and social media data are discussed, organized, and analyzed. They cover volume and variety aspects that define big data and uncover analytic complexities that need to be dealt with.

Keywords

Big data MapReduce Text mining Sentiment analysis Social mining 

References

  1. Albescu, F., & Pugna, I. B. (2014). Marketing intelligence—The last frontier of business information technologies. Romanian Journal of Marketing, 3, 55–68.Google Scholar
  2. Bello-Orgaza, G., Jungb, J. J., & Camachoa, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59.CrossRefGoogle Scholar
  3. Benson, A. R., Gleich D. F. & Demmel J. (2013). Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures, 2013 IEEE International Conference on Big Data, October 6–9, TBD Silicon Valley.Google Scholar
  4. Beyer, M. A., & Laney, D. (2012). The importance of ‘big data’: A definition. Stamford, CT: Gartner.Google Scholar
  5. Bradley, J. (2016). Apache® Spark™ MLlib: From Quick Start to Scikit-Learn. Retrieved October, 2017, from http://go.databricks.com/spark-mllib-from-quick-start-to-scikit-learn.
  6. Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social networks. Marketing Science, 35(3), 343–362.CrossRefGoogle Scholar
  7. Davenport, T., & Patil, D. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70–76.Google Scholar
  8. Dean, J. & Ghemawat, S. (2004, December). MapReduce: Simplified data processing on large clusters, OSDI'04: Sixth symposium on operating system design and implementation, San Francisco, CA.Google Scholar
  9. Forrester, (2011). Expand your digital horizon with big data. Forrester. Retrieved May 27 from http://www.asterdata.com/newsletter-images/30-04-2012/resources/Forrester_Expand_Your_Digital_Horiz.pdf Accessed July 7, 2017.
  10. Goes, P. (2014). Big data and IS research. MIS Quarterly, 38(3), III–VIII.Google Scholar
  11. Halko, N. P. (2012). Randomized methods for computing low-rank approximations of matrices. Unpublished doctoral dissertation, University of Colorado, Boulder.Google Scholar
  12. IBM. (2011) From stretched to strengthened—Insights from a global CMO study. Retrieved September 17, 2015, from http://www.ibm.com/services/us/cmo/cmostudy2011/downloads.html.
  13. Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety, technical report. Retrieved October, 2017, from https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.
  14. Lilien, G. L., & Rangaswamy, A. (2000). Modeled to bits: Decision models for the digital, networked economy. International Journal of Research in Marketing, 17, 227–235.CrossRefGoogle Scholar
  15. Liu, X., Singh, P. V., & Srinivasan, K. (2016). A structured analysis of unstructured big data by leveraging cloud computing. Marketing Science, 35(3), 363–388.CrossRefGoogle Scholar
  16. Martin, L. & Pu, P. (2014). Prediction of helpful reviews using emotions extraction. AAAI Publications.Google Scholar
  17. McAuley, J., Pandey, R. & Leskovec J. (2015). Inferring networks of substitutable and complementary products, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.Google Scholar
  18. Odersky, M., Spoon L., Venners B. (2011), Programming in Scala. In A comprehensive step-by-step guide (2nd ed) (January 4, 2011), Artima Inc.Google Scholar
  19. Rust, R. T., & Huang, M. H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206–221.Google Scholar
  20. Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26–48.CrossRefGoogle Scholar
  21. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.CrossRefGoogle Scholar
  22. Wilkinson, D. (2013). Scala as a platform for statistical computing and data science. Retrieved October, 2017, from https://darrenjw.wordpress.com/2013/12/23/scala-as-a-platform-for-statistical-computing-and-data-science/
  23. Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562–1566.CrossRefGoogle Scholar
  24. Zaharia, M. (2014). An architecture for fast and general data processing on large clusters, University of California at Berkeley, Technical Report No. UCB/EECS-2014-12.Google Scholar
  25. Zaharia, M., Chowdhury M., Das T., Dave A., Ma J., McCauley M., Franklin M. J., Shenker S., Stoica I. (2012, April). Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing, NSDI 2012.Google Scholar

Copyright information

© Academy of Marketing Science 2019

Authors and Affiliations

  • Mihai Calciu
    • 1
    Email author
  • Jean-Louis Moulins
    • 2
  • Francis Salerno
    • 3
  1. 1.Université de Lille RIME-LabLilleFrance
  2. 2.Aix Marseille Université Cret-LogMarseilleFrance
  3. 3.Université de Lille LEMLilleFrance

Personalised recommendations