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Web Analytics
INTRODUCTION
• Web analytics is the measurement, collection, analysis and
reporting of web data for purposes of understanding and
optimizing web usage.
• Web analytics is not just a process for measuring web traffic but can
be used as a tool for business and market research, and to assess
and improve the effectiveness of a website.
• Web analytics applications can also help companies measure the
results of traditional print or broadcast advertising campaigns.
• Web analytics provides information about the number of visitors to
a website and the number of page views. It helps gauge traffic and
popularity trends which is useful for market research.
Basic Steps of Web Analytics Process
• Collection of data: This stage is the collection of the basic, elementary
data. Usually, this data is counts of things. The objective of this stage is to
gather the data.
• Processing of data into information: This stage usually take counts and
make them ratios, although there still may be some counts. The objective
of this stage is to take the data and conform it into information,
specifically metrics.
• Developing KPI: This stage focuses on using the ratios (and counts) and
infusing them with business strategies, referred to as Key Performance
Indicators (KPI). Many times, KPIs deal with conversion aspects, but not
always. It depends on the organization.
• Formulating online strategy: This stage is concerned with the online goals,
objectives, and standards for the organization or business. These
strategies are usually related to making money, saving money, or
increasing marketshare.
Web analytics technologies
• Off-site web analytics refers to web measurement and
analysis regardless of whether you own or maintain a website.
It includes the measurement of a website's potential audience
(opportunity), share of voice (visibility), and buzz (comments)
that is happening on the Internet as a whole.
• On-site web analytics, the most common, measure a visitor's
behavior once on your website. This includes its drivers and
conversions; for example, the degree to which
different landing pages are associated with online purchases.
On-site web analytics measures the performance of your
website in a commercial context.
Web Analytics Data Sources
• Direct HTTP request data: directly comes from HTTP request messages
(HTTP request headers).
• Network level and server generated data associated with HTTP
requests: not part of an HTTP request, but it is required for successful
request transmissions. For example, IP address of a requester.
• Application level data sent with HTTP requests: generated and
processed by application level programs (such as JavaScript, PHP, and
ASP.Net), including session and referrals. These are usually captured by
internal logs rather than public web analytics services.
• External data: can be combined with on-site data to help augment the
website behavior data described above and interpret web usage. For
example,, e-mail open and click-through rates, direct mail campaign
data, sales and lead history, or other data types as needed.
Thank you…………..

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Web analytics

  • 2. INTRODUCTION • Web analytics is the measurement, collection, analysis and reporting of web data for purposes of understanding and optimizing web usage. • Web analytics is not just a process for measuring web traffic but can be used as a tool for business and market research, and to assess and improve the effectiveness of a website. • Web analytics applications can also help companies measure the results of traditional print or broadcast advertising campaigns. • Web analytics provides information about the number of visitors to a website and the number of page views. It helps gauge traffic and popularity trends which is useful for market research.
  • 3. Basic Steps of Web Analytics Process • Collection of data: This stage is the collection of the basic, elementary data. Usually, this data is counts of things. The objective of this stage is to gather the data. • Processing of data into information: This stage usually take counts and make them ratios, although there still may be some counts. The objective of this stage is to take the data and conform it into information, specifically metrics. • Developing KPI: This stage focuses on using the ratios (and counts) and infusing them with business strategies, referred to as Key Performance Indicators (KPI). Many times, KPIs deal with conversion aspects, but not always. It depends on the organization. • Formulating online strategy: This stage is concerned with the online goals, objectives, and standards for the organization or business. These strategies are usually related to making money, saving money, or increasing marketshare.
  • 4. Web analytics technologies • Off-site web analytics refers to web measurement and analysis regardless of whether you own or maintain a website. It includes the measurement of a website's potential audience (opportunity), share of voice (visibility), and buzz (comments) that is happening on the Internet as a whole. • On-site web analytics, the most common, measure a visitor's behavior once on your website. This includes its drivers and conversions; for example, the degree to which different landing pages are associated with online purchases. On-site web analytics measures the performance of your website in a commercial context.
  • 5. Web Analytics Data Sources • Direct HTTP request data: directly comes from HTTP request messages (HTTP request headers). • Network level and server generated data associated with HTTP requests: not part of an HTTP request, but it is required for successful request transmissions. For example, IP address of a requester. • Application level data sent with HTTP requests: generated and processed by application level programs (such as JavaScript, PHP, and ASP.Net), including session and referrals. These are usually captured by internal logs rather than public web analytics services. • External data: can be combined with on-site data to help augment the website behavior data described above and interpret web usage. For example,, e-mail open and click-through rates, direct mail campaign data, sales and lead history, or other data types as needed.