The Lost Metrics

Web Analytics is defined as dealing with a massive amount of web data and producing actionable analyses and recommendations out of them. This is the critical role that a Web Analytics team plays in internet marketing as their analysis will help big time in different marketing campaigns and initiatives. And to gauge the performance of these campaigns and come up with actionable insights, key metrics have been defined based on the website’s business goals and success events.

When Web Analytics was still starting, the main data source of analysts which they use to analyze their sites with are from the website’s log files. They use a log file analyzer, an example of which is AWStats or Advanced Statistics, to which they derive metrics such as:

• Hits
• Page Views
• Visits
• Clicks
• Visits Duration
• Top Pages
• Bandwidth Usage
• File Types
• Visitors’ Browsers
• Errors

From these, they were able to measure a bit of the visitor’s activity but as this is a log file analyzer, web analysts benefit more in knowing website performance rather than visitor conversions. But pretty soon, more advanced Web Analytics tools surfaced to aid analysts seeking to get deeper into web data. The advent of these tools, such as Omniture SiteCatalyst and Google Analytics, brought in more defined and meaningful metrics. These newly-found metrics are of such high importance that web analysts started to disregard the metrics offered by log file analyzers such as those mentioned above.

They may have mattered before but web analysts knew that these only count numbers, the tip of the iceberg. These do not tell how much of these Hits and page Views were converted to buyers (in case of e-commerce sites) or were triggered to do a certain activity the sites aims for its visitors to accomplish. These conversions are some of the many metrics which advanced Web Analytics tools offered to produce and then web analysts have been empowered once more. They can now focus on what web analysts should be doing:

• Compute for different conversions
• Do Path Analysis
• Dive into A/B and Multi-Variate Testing
• Perform Keyword Analysis
• Compute for step-by-step purchase conversion
• Know the Average Order Value
• Check for site-wide conversion rate
• Understand visitor behavior and traffic trends
• Create other actionable reports

Here’s a perfect example of the process Web Analysts should take when doing their analysis, perfectly illustrated by none other than the Google Analytics Evangelist himself, Avinash Kaushik.

They can just then leave the log file analyzer to the development and programming teams who can really benefit from knowing bandwidth usage, visitors’ browsers, file types, and errors. This way, getting dirty with web data for the purpose of creating actionable analysis will really be worth the while of a web analyst.