Interactive Tool to Find Focal Spots in Human Computer Interfaces in eCommerce

eCommerce Consumer Analytics Tool (eCCAT)
  • VenkataSwamy Martha
  • Zhenrui Wang
  • Angela JiangEmail author
  • Sam Varghese
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 529)


eCommerce is one of the popular electronic services available in the vast Internet world. eCommerce endpoints, also called eCommerce websites, in general, are a composition of several web pages. Within eCommerce endpoints, there exist specific web page types that are abnormal in their consumption of information and user behavior called focal spots. Finding a focal spot is key for understanding and improving the human interaction interface on eCommerce endpoints. In order to make business decisions concerning these focal spots, decision analytics teams are employed to identify focal spots with abnormal consumer perception and to address areas in which to expand business. We propose a methodology for transforming user activity data into useful business analytics to find focal spots if any. In this work, we developed a prototype of a one-stop solution for non-technical users to understand customer response analysis on a given eCommerce endpoint. The proposed system, ‘eCommerce Consumer Analytics Tool (eCCAT)’, consists of a data extraction and automated analysis component and a visualization component. The interactive tool further provides a way to find a page in the eCommerce endpoint with an extreme key performance indicator.


  1. 1.
    Joines, J.L., Scherer, C.W., Scheufele, D.A.: Exploring motivations for consumer web use and their implications for e-commerce. J. Consum. Mark. 20(2), 90–108 (2003)CrossRefGoogle Scholar
  2. 2.
    Waldegg, P.B., Scrivener, S.A.R.: Designing interfaces for culturally diverse users. In: Proceedings of Sixth Australian Conference on Computer-Human Interaction 1996, pp. 316–317, 24–27 Nov 1996Google Scholar
  3. 3.
    Purchase, H.C.: The effects of graph layout. In: Proceedings 1998 Australasian Computer Human Interaction Conference, pp. 80–86, 30 Nov–4 Dec 1998Google Scholar
  4. 4.
    Kinley, K., Tjondronegoro, D., Partridge, H.: Web searching interaction model based on user cognitive styles. In: Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction, pp. 340–343. ACM, New York (2010)Google Scholar
  5. 5.
    Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A framework for the evaluation of session reconstruction heuristics in web-usage analysis. INFORMS J. Comput. 15, 171–190 (2003)CrossRefGoogle Scholar
  6. 6.
    Chen, C.: Searching for intellectual turning points: progressive knowledge domain visualization. Proc. Natl. Acad. Sci. USA 101(suppl.), 5303–5310 (2004)Google Scholar
  7. 7.
    Keim, D.A., Mansmann, F., Thomas, J.: Visual analytics: how much visualization and how much analytics? ACM SIGKDD Explor. Newslett. 11(2), 5–8 (2010)CrossRefGoogle Scholar
  8. 8.
    Munzner, T., Johnson, C., Moorhead, R., Pfister, H., Rheingans, P., Yoo, T.S.: NIH-NSF visualization research challenges report summary. IEEE Comput. Graph. Appl. 26(2), 20–24 (2006). doi: 10.1109/MCG.2006.44 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • VenkataSwamy Martha
    • 1
  • Zhenrui Wang
    • 1
  • Angela Jiang
    • 1
    Email author
  • Sam Varghese
    • 1
  1. 1.@WalmartLabsSunnyvaleUSA

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