An attempt to modeling rule base real time web funnel structure

Sasadhar Bera; Prasun Das

Article Details

Research Article Volume 05 Issue 2

An attempt to modeling rule base real time web funnel structure

Sasadhar Bera • Prasun Das

Published: 24 Feb 2012 Issue:Volume 05 Issue 2 Apr 2011 Author details below

Sasadhar Bera

Shailesh J. Mehta School of Management, Indian Institute of Technology,

Prasun Das

SQC & OR Division, Indian Statistical Institute

E-Commerce Chat Conversion Funnel Hot Lead AOV Cart Load Agent

Download PDF Reading View How to Cite BibTeX / RIS XML Metadata JSON Metadata View Issue

Article Metrics Report

Views, downloads, citations, engagement

Cited by

Current citation count

Abstract

Research summary

Every retail web site is actively seeking out new innovations and approaches that create competitive advantage and increase the profitability. In general, retailers constantly monitor the behaviour of the real shoppers on the website and any changes in the market requirements. This paper presents a chat invitation web funnel structure, profiling web visitors and selection of hot leads for retail business processes through scoring method using geographic region, product page and other factors. Choosing the right hot prospects through rule base real time chat invitation method based on product type, time on page, cart load, search behavior, cookie information etc. and providing chat to those hot prospects is a special merit to this work. Active rules selection process is done using rule effectiveness indicator and chat load contribution which ensures sales revenue, chat volume and profit margin. An indirect increase in customer delight for interacting with representatives is also expected.

Editorial Process

Article History

Published 24 Feb 2012

How to Cite

Bera, S. & Das, P.. (2012). An attempt to modeling rule base real time web funnel structure. Journal of Business and Retail Management Research, Volume 05 Issue 2.

Citation Context

Archive cited by No internal citing article yet

Reference depth 32 sources listed

DOI record DOI not listed

Citation signal Citation exports and metadata ready

APA

Bera, S. & Das, P.. (2012). An attempt to modeling rule base real time web funnel structure. Journal of Business and Retail Management Research, Volume 05 Issue 2.

MLA

Bera, Sasadhar, and Prasun Das. "An attempt to modeling rule base real time web funnel structure." Journal of Business and Retail Management Research, Volume 05 Issue 2, 2012.

Chicago

Sasadhar Bera and Prasun Das. "An attempt to modeling rule base real time web funnel structure." Journal of Business and Retail Management Research Volume 05 Issue 2 (24 Feb 2012).

Harvard

Bera, S. & Das, P. (2012) An attempt to modeling rule base real time web funnel structure. Journal of Business and Retail Management Research, Volume 05 Issue 2

References

Agresti, A. (2002), Categorical Data Analysis, 2nd edition, John Wiley, New York.
Breslow, N. (1982), Covariance adjustment of relative-risk estimates in matched studies,
Biometrics, Vol. 38, pp. 661-672.
Constantinides E. (2004), ‘Influencing the online consumer’s behaviour: the web experience’,
Internet Research, Vol. 14 No. 2, pp. 111-126.
Dellmann, F., Kfm, D., Wulff, H., Betriebsw, D. Schmitz, S. and Wirtschaft, F. (2003),
‘Statistical analysis of web log files of a German Automobile Producer findings from practical project concerning web usage mining’, in: Xindong Wu, Alex Tuzhilin, Jude
Shavlik (Eds.): Third IEEE International Conference on Data Mining, Melbourne, Florida,
IEEE Press, pp. 715-718.
E-Metrics - Business Metrics for the New Economy, available at:
http://www.netgen.com/emetrics/ (accessed July 2009).
Goldfarb, A. and Qiang, L. (2006), ‘Household-Specific Regression Using Clickstream Data’,
Statistical Science, Vol. 21 No. 2, pp. 217-255.
Griffin, N. L. and Lewis F. D. (2010), ‘A Rule-Based Inference Engine which is optimal and
VLSI implementable’, available at: http://www.cs.uky.edu/~lewis/papers/inf
engine.pdf(accessed July 16, 2010)
Hastie, T., Tibshrani, R. and Friedman, J. (2001), Elements of Statistical Learning, Springer-
Verlag, New York.Jarvenppa, S. L. and Todd, P. A. (1996), ‘Consumer reactions to electronic
shopping on the World Wide Web’, International Journal of Electronic Commerce, Vol. 1 No. 2, pp. 59-88.
King, B. Andrew. (2008), Website Optimization, O’Reilly Media Inc.
Kotler, P. and Armstrong, G. (2001), Principles of Marketing, Prentice-Hall, Englewood Cliffs, NJ.
Kucukyilmaz, T., Cambazoglu B. B., Aykanat, C. And Can, F. (2008), ‘Chat mining: Predicting user and message attributes in computer-mediated communication’, Information Processing and Management, Vol. 44, pp. 1448-1466.
Merrilees, Bill and Fry, Marie-Louise (2003), ‘E-trust: the influence of perceived interactivity
on e-retailing users’, Marketing Intelligence & Planning, Vol. 21 No 2, pp. 123-128.
Montgomery, A., Shibo, L., Kannan, S. and John, C. L. (2004), ‘Modelling online browsing
and path analysis using Clickstream data’, working paper, Graduate school of industrial
administration, Carnegie Mellon University, (accessed July 2009).
O’Cass, A. and Fenech, T. (2003), ‘Web retailing adoption: exploring the nature of internet
users Web retailing behaviour’, Journal of Retailing and Consumer Services, Vol. 10 No. 2, pp. 81-94.
Pallis, G., Angelis, L. and Vakali, A. (2007), ‘Validation and interpretation of Web users’ sessions clusters’,Information Processing and Management, Vol. 43, pp. 1348–1367.
Ranganathan, C. and Ganapathy, S. (2002), ‘Key dimensions of business-to-consumer web
sites’, Information and Management, Vol. 39, pp. 457-465.