Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
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Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011

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    Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011 Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011 Presentation Transcript

    • Web Analytics Software to Predict the Behavior of Website Visitors
      Constantine J. Aivalis
      Technological Education Institute of Crete
      University of Peloponnese
      costis.aivalis@gmail.com
    • Content
      Introduction
      The Problem
      The Solution
      Architecture
      Functionality
      DBMS
      Results
      Customer Behavioral Model Graph
      Measurements
      Future Work
      Applications
      Conclusion
      25/9/2011
      2
      Web Analytics Software to Predict the Behavior of Website Visitors
    • Introduction
      WWW is today's common business platform.
      E-Commerce infrastructure must be reliable, robust and scalable.
      Web systems produce huge amounts of user activity data that are often unused.
      User activity data must be converted to information.
      Intelligent Customer classification allows better customized services.
      25/9/2011
      3
      Web Analytics Software to Predict the Behavior of Website Visitors
    • The Problem
      E-shops often operate in “blind folded” fashion.
      Only successful sales transactions are visible to the administration and management.
      Most e-Commerce systems have no built-in performance measuring mechanisms.
      Only registered-customer actions are taken into consideration. Visitor majority may not be customers yet. Their behavior has to be analyzed in order to make them.
      Access log files include all interaction data details.
      Manual access log file scrutinizing is too inconvenient to be performed on regular basis.
      25/9/2011
      4
      Web Analytics Software to Predict the Behavior of Website Visitors
    • The Solution
      Parsing and “cleaning” log files. Extraction and transfer into a DBMS. Information Generation.
      Cross correlation of log file and e-Commerce site data for seamless integration.
      Anonymous and registered visitor hits can be analyzed through their IP-addresses.
      Crawlers and Web-Bots can be recognized via IP-address and their behavioral patterns.
      Implementation of a software tool that directly measures the operational performance of the e-shop in nearly real time.
      25/9/2011
      5
      Web Analytics Software to Predict the Behavior of Website Visitors
    • Architecture
      25/9/2011
      6
      Web Analytics Software to Predict the Behavior of Website Visitors
    • Functionality
      25/9/2011
      7
      Web Analytics Software to Predict the Behavior of Website Visitors
    • DBMS
      25/9/2011
      8
      Web Analytics Software to Predict the Behavior of Website Visitors
    • Results
      Visitor Behavioral Analysis (including non registered visitors)
      Dynamical generation of various statistics
      Graph generation
      Tendency Forecasts
      Data Mining Possibilities
      Exception Reports
      Measurements and e-shop performance comparison
      Time Period performance analysis
      25/9/2011
      9
      Web Analytics Software to Predict the Behavior of Website Visitors
    • Customer Behavioral Model Graph
      10
      25/9/2011
      Web Analytics Software to Predict the Behavior of Website Visitors
    • Measurements
      25/9/2011
      11
      Web Analytics Software to Predict the Behavior of Website Visitors
    • Measurements
      Order Values/Numbers
      Visits
      Time spent per product or service
      Accesses per product or service
      Orders per Product or service
      Bots visited
      Visitors
      Uncompleted ordering sessions
      Profitable customer groups
      Profitable products or services
      Overall profits
      Promotion impact
      25/9/2011
      Web Analytics Software to Predict the Behavior of Website Visitors
      12
    • Future Research
      Analysis of bots and their search engine behavior concerning e-shops.
      Recognition of anonymous bots and spiders through their access patterns.
      Customer rating and evaluation application based on non purchase behavior.
      Agent implementation in order to automatically promote the rank of less sought for products.
      Methodology for RIAs
      25/9/2011
      Web Analytics Software to Predict the Behavior of Website Visitors
      13
    • Thank You
      Constantine Aivalis
      costis.aivalis@gmail.com
      25/9/2011
      Web Analytics Software to Predict the Behavior of Website Visitors
      14