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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
E-Commerce Analytics: Methodologies and
ApplicationsConstantine J. Aivalis
Lecturer at Technological Education Institute of Crete & University of Peloponnese
Email: [email protected]
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Contents of the Presentation
IntroductionWeb AnalyticsComparison of MethodologiesThe ProblemThe SolutionArchitectureFunctionalityResultsCustomer Behavioral Model GraphMeasurements Current WorkApplicationsConclusion
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
IntroductionWWW 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 often stay unused.User activity data must be converted to information.Intelligent Customer classification allows better
customized services and increases sales.
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Web AnalyticsAnalysis of log files
Log files contain very detailed information about each request. The data have to be carefully selected.
Page TaggingPage Tagging requires an extra web server, to whom the visitors browser is automatically sent.This server collects the log data generated by this visit and stores it to a specific data base for each site, based on an account number.
Network Data Collection DevicesSniffers, Black boxes that capture IP packages.
Hybrid MethodsCombine Analysis of log files and Tagging, in order to reduce the disadvantages of each method.
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Available Vendors• Google Analytics (Urchin)
• Microsoft adCenter Analytics (DeepMetrix)
• Yahoo Web Analytics (indexTools)
• Clickstream.com
• Adobe Web Analytics (Omniture)
• IBM Unica NetInsight
• ChartBeat Inc.
• Hitmatic
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Comparison of Methodologies
Source: Brian Clifton ”Web Traffic Data Sources & Vendor Comparison”
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
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 win them.
• Access log files include all interaction data details.• Manual access log file scrutinizing is too inconvenient to
be performed on regular basis.
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
The Solution• Parsing and “cleaning” log files. Extraction and transfer
into a DBMS. Information Generation.(Extract Transform Load ETL)
• 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.
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Rotating Access Log Files
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Access Log file Sample
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Architecture of the System
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Functionality of the System
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Real Time Support System
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
ResultsVisitor 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
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This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Metrics on Demand• 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
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
• Overall speed bytes per second• Number of active visitors• Requested items per visitor and overall• Orders completed• Customer behavioral graph• Number of logged in customers • Turnover or profit per hour, day • Bot counter
Real Time Metrics
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Customer Behavioral Model Graph
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Measurements
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Real Time Gauges
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Current Research• Methodologies for Web2.0 and RIA application
analysis. • Deeper bot behavior analysis concerning e-
commerce sites.• 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.
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The contents reflect the author's views. The Managing Authority is not liable for any use that may be made of the information contained therein
This project is co-financed by the ERDF and made possible by the INTERREG IVC programme
Thank You
Constantine J. AivalisLecturer at Technological Education Institute of Creteand University of Peloponnese
Email: [email protected]
