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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
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
Costis Aivalis Web analytics Software IFITT Greece Hilton Athens Sept 2011
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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  • 1. Web Analytics Software to Predict the Behavior of Website Visitors<br />Constantine J. Aivalis<br />Technological Education Institute of Crete<br />University of Peloponnese <br />costis.aivalis@gmail.com<br />
  • 2. Content<br />Introduction<br />The Problem<br />The Solution<br />Architecture<br />Functionality<br />DBMS<br />Results<br />Customer Behavioral Model Graph<br />Measurements <br />Future Work<br />Applications<br />Conclusion<br />25/9/2011<br />2<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 3. Introduction<br />WWW is today&apos;s common business platform.<br />E-Commerce infrastructure must be reliable, robust and scalable.<br />Web systems produce huge amounts of user activity data that are often unused.<br />User activity data must be converted to information.<br />Intelligent Customer classification allows better customized services.<br />25/9/2011<br />3<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 4. The Problem<br />E-shops often operate in “blind folded” fashion.<br />Only successful sales transactions are visible to the administration and management.<br />Most e-Commerce systems have no built-in performance measuring mechanisms.<br />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.<br />Access log files include all interaction data details.<br />Manual access log file scrutinizing is too inconvenient to be performed on regular basis.<br />25/9/2011<br />4<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 5. The Solution<br />Parsing and “cleaning” log files. Extraction and transfer into a DBMS. Information Generation.<br />Cross correlation of log file and e-Commerce site data for seamless integration.<br />Anonymous and registered visitor hits can be analyzed through their IP-addresses.<br />Crawlers and Web-Bots can be recognized via IP-address and their behavioral patterns.<br />Implementation of a software tool that directly measures the operational performance of the e-shop in nearly real time.<br />25/9/2011<br />5<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 6. Architecture<br />25/9/2011<br />6<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 7. Functionality<br />25/9/2011<br />7<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 8. DBMS<br />25/9/2011<br />8<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 9. Results<br />Visitor Behavioral Analysis (including non registered visitors)<br />Dynamical generation of various statistics<br />Graph generation<br />Tendency Forecasts<br />Data Mining Possibilities<br />Exception Reports<br />Measurements and e-shop performance comparison<br />Time Period performance analysis<br />25/9/2011<br />9<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 10. Customer Behavioral Model Graph <br />10<br />25/9/2011<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 11. Measurements<br />25/9/2011<br />11<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />
  • 12. Measurements<br />Order Values/Numbers <br />Visits <br />Time spent per product or service<br />Accesses per product or service<br />Orders per Product or service<br />Bots visited<br />Visitors<br />Uncompleted ordering sessions<br />Profitable customer groups<br />Profitable products or services<br />Overall profits<br />Promotion impact<br />25/9/2011<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />12<br />
  • 13. Future Research<br />Analysis of bots and their search engine behavior concerning e-shops.<br />Recognition of anonymous bots and spiders through their access patterns.<br />Customer rating and evaluation application based on non purchase behavior.<br />Agent implementation in order to automatically promote the rank of less sought for products.<br />Methodology for RIAs <br />25/9/2011<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />13<br />
  • 14. Thank You<br />Constantine Aivalis<br />costis.aivalis@gmail.com<br />25/9/2011<br />Web Analytics Software to Predict the Behavior of Website Visitors<br />14<br />

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