Web Analytics

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Web Analytics User Behavior
An update on Ajaxalytics and CBMGSKelly
StormMarch 17th, 2008
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Web Analytics

• The study of the behavior of website users.
• Use of the data collected from a website to
  determine which aspects of the website are
  'working' or 'effective'.
• Data is currently derived from web server logfiles

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Web Server Logfile Analysis

• Web servers record all their transactions in a
  logfile
• ex. a single line from Toxodb's
  logfile58.61.39.237 - - 19/Dec/2007232601
  -0500 "GET /restricted/data/Genome/nuc/TgME49B7_G
  ENOMIC_2003.04.17_TIGR.fasta.gz HTTP/1.1" 404 347
  "http//www.toxodb.org/restricted/data/Genome/nuc/
  " "Mozilla/4.0 (compatible MSIE 6.0 Windows NT
  5.1)"
• These logfiles can be read by a program to
  provide data on the popularity / usefulness of
  the website.

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Web Server Logfile Analysis

• Initially, statistics consisted primarily of
  counting the number of client requests (or hits)
  made to the web server.
• Now we wish to gauge more accurately the amount
  of human activity on web servers by analyzing
  page views, visits (sessions), and user-agents

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Web Server Logfile Analysis

• Page views are different from a hits. A page view
  is when a user accesses a particular web page, a
  hit encomposses both page views, image loads,
  JavaScript includes, etc.
• Visits, or sessions, are sequences of requests
  from a uniquely identified client (host ip
  address) that expired after a certain amount of
  inactivity, usually 30 minutes.
• User-agents indicate what operating system /
  browser type and version the host is currently
  using.

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Web Server Logfile Analysis

• By analyzing page views and user-agents we're
  able to provide detailed reports showing
• Monthly, weekly, daily history
• Hourly Average
• Usage by location (country, state, city)?
• Page load activity
• Percentage of operating system and browser
• References
• Google Keywords

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Web Server Logfile Analysis

• By analyzing sessions, instead of counting hits,
  we can better understand user behavior
• By creating model graphs of user sessions
• By creating customer behavior model graphs
  (CBMG)?
• By grouping users together based upon their
  individual usage and separating them with a
  clustering algorithm

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Customer Behavior Model Graphs
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Customer Behavior Model Graphs
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Customer Behavior Model Graphs

• For instance, this shows that half the users came
  into the website and browsed, and half came into
  the website and searched

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Customer Behavior Model Graphs

• This can quickly spin out of control, however,
  when you have a website with thousands of pages,
  so we'd like to be able to group users according
  to
• user-agent information
• time
• As well as group pages according to keywords to
  limit the size of the CBMG and give us a clearer
  view of user behavior.

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Future work ( 03.17.08 )?

• Group by keyword to limit the size of the CBMG
• Allow more options to better understand the
  information provided by the CBMG
• Clustering algorithm to group users
• cluster users by sessions to group 'similar'
  users, then analyze the user-agent information
  particular to these groups