Funnel report mining for the MSN network

  • Authors:

  • Teresa Mah;Hank Hoek;Ying Li

  • Affiliations:

  • Microsoft Corporation, Redmond, WA;Microsoft Corporation, Redmond, WA;Microsoft Corporation, Redmond, WA

  • Venue:
  • Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2001

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Abstract

Data mining research has long concentrated on the five main areas: clustering, association discovery, classification, forecasting and sequential patterns. Web data mining projects are concerned mainly with text mining, user segmentation, forecasting web usage and analyzing users' clickstream patterns. We present a new type of web usage mining called funnel analysis or funnel report mining. A funnel report is a study of the retention behavior among a series of pages or sites. For example, of all hits on the home page of www.msn.com, what percentages of those are followed by hits to moneycentral.msn.com? What percentage of www.msn.com hits are followed by moneycentral.msn.com, and then www.msnbc.com? What are the most interesting funnels starting with www.msn.com? Where does the greatest drop off rate occur after a user has hit MSNBC? Funnel reports are extremely useful in e-business because they give product planners an idea of how usable and well-structured their site is. From our experience performing web usage mining for the MSN network of sites, funnel reports are requested even more than user segmentation analyses, site affiliation studies and classification exercises. In this paper, we define a framework for funnel analysis and provide a tree-based solution we have been using successfully to extract all relevant funnels using only one scan of the data file.