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E-Commerce Analytics:
Methodologies and Applications
Constantine J. Aivalis
Lecturer at Technological Education Institute of Crete & University of Peloponnese
Email: costis.aivalis@gmail.com
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Contents of the Presentation
 Introduction
 Web Analytics
 Comparison of Methodologies
 The Problem
 The Solution
 Architecture
 Functionality
 Results
 Customer Behavioral Model Graph
 Measurements
 Current Work
 Applications
 Conclusion
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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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

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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 often stay unused.
User activity data must be converted to information.
Intelligent Customer classification allows better
customized services and increases sales.
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Web Analytics
Analysis of log files
Log files contain very detailed information about each request. The
data have to be carefully selected.
Page Tagging
Page 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 Devices
Sniffers, Black boxes that capture IP packages.
Hybrid Methods
Combine Analysis of log files and Tagging, in order to reduce the
disadvantages of each method.
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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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
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Comparison of Methodologies
Source: Brian Clifton ”Web Traffic Data Sources & Vendor Comparison”
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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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.
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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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.
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Rotating Access Log Files
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Access Log file Sample
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Architecture of the System
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Functionality of the System
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Real Time Support System
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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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
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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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
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Real Time Metrics
• 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
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Customer Behavioral Model Graph
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Measurements
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Real Time Gauges
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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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.
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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

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Thank You
Constantine J. Aivalis
Lecturer at Technological Education Institute of Crete
and University of Peloponnese
Email: costis.aivalis@gmail.com
This project is co-financed by the ERDF and made possible by the
INTERREG IVC programme
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