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