costis aivalis web analytics software ifitt greece hilton athens sept 2011

Web Analytics Software to Predict the Behavior

of Website VisitorsConstantine J. Aivalis

Technological Education Institute of CreteUniversity of Peloponnese

costis.aivalis@gmail.com

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Content

IntroductionThe ProblemThe SolutionArchitectureFunctionalityDBMSResultsCustomer Behavioral Model GraphMeasurements Future WorkApplicationsConclusion

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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 are often unused.

User activity data must be converted to information.

Intelligent Customer classification allows better customized services.

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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 make 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 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.

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Architecture25/9/2011

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Functionality25/9/2011

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DBMS25/9/2011

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Results

Visitor Behavioral Analysis (including non registered visitors)

Dynamical generation of various statisticsGraph generationTendency ForecastsData Mining PossibilitiesException ReportsMeasurements and e-shop performance

comparisonTime Period performance analysis

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Customer Behavioral Model Graph 25/9/2011 10

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Measurements25/9/2011 Web Analytics Software to Predict the Behavior of Website Visitors

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Measurements

Order Values/Numbers Visits Time spent per product or serviceAccesses per product or serviceOrders per Product or serviceBots visitedVisitorsUncompleted ordering sessionsProfitable customer groupsProfitable products or servicesOverall profitsPromotion impact

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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

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Thank YouConstantine Aivalis

costis.aivalis@gmail.com

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