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Web metrics, success, failure & insights

Web metrics are based on quantitative data captured by tracking web site usage, at its most basic refers to the measuring of page requests (referred to as page views) and server side functions such as Search, Downloads or form submissions. This data can provide valuable quantitative data to help us measure the sites success, and understand how it is being used by the user including the technologies they are using to visit the site.

With regular reporting of this data we can track and measure performance over time. For long term projects the data captured can be extremely valuable when coupled with site usability reviews and user interviews, to provide valuable design insights for improving sites user experience across; content, functionality, navigation, content structures, processes and even the underlying technology infrastructure (i.e. disk space, traffic volumes, bandwidth requirements).

I will cover the subject of usability reviews in a separate article, but making sure this data is available can provide significant insights in areas such as product finding, self service, process abandonment i.e. registration, product ordering etc.

The analysis and interpretation of data requires us to remember what exactly is measured, a page visit – does not mean it was read, just that it was visited, dwell time doesn’t mean how long the user looked at a page, it is how long between page requests. Reporting should always be done with care, as figures that appear like black and white can often mislead, as illustrated by these quotes.

“Then there was the man who drowned crossing a stream with an average depth of six inches.”
W. I. E. Gates

“Get your facts first, and then you can distort them as much as you please: facts are stubborn, but statistics are more pliable”
Mark Twain

“There are three kinds of lies: lies, damn lies, and statistics.”
Benjamin Disraeli

If there is no data

If you are looking at a web site where no usage data exists, it is worth looking at freely available web statistics. These are generalised statistics but can still provide a valuable starting point for many design decisions.

“Errors using inadequate data are much less than those using no data at all.”
Charles Babbage

Guidelines for Web metrics

These guidelines outline the statistical data that should be captured as part of a web metrics solution. Statistical data has been split into two parts, the first is focused on what content and functionality is being used on the site. The second part is focused towards the technologies used to browse the site, and can help inform future design decisions. Each site may require different metrics to measure performance correctly.

Site Statistics

All statistical data should be recorded with timestamps to enable review by anytime periods: daily, weekly, monthly and annually. And if working on international sites consider recording locality and regional time for targeting specific market segments.

Total number of visitors
Number of unique visitors
Total number of pages
Total disk space used
Total bandwidth usage

Incoming Search Engine Terms & Term Frequency
Referred from (External link from other sites and Search Engines)

Total number of visitors by [section, page, category]
Number of unique visitors by [section, page, category

If you have forms (could be contact or survey type applications) or download pages you may also want to consider;

Total number of visitors by [form, download]
Number of unique visitors by [form, download]
Total number of visitors by [Submitted, Downloaded]
Number of unique visitors by [Submitted, Downloaded]

If you have external links and social bookmarking functions you may also want to consider;

Total number of exits by [external link]
Total number of bookmarks by [social bookmarking link]

Site Visitor Statistics

User distribution by Browser Agent
User distribution by OS (Browser agent and OS can help identify traffic from other devices)
User distribution by Screen Resolution
User distribution by Browser view pane size (not everybody browses full screen)
User distribution by Screen Colour Depth
User distribution by Location
User distribution by OS language
User distribution by Plug-in Version (Flash, Quicktime, Real, Windows Media Player or other relevant plug-in)
Load time (This can help with determining user connection speeds, although they may also performing other internet activities)

Remember when looking at the statistics you may want to remove your own and other extraneous data such development teams usage data during the analysis or possible through configuration. Typically by listing IP addresses to ignore within the server configuration or statistical application. If this isn’t possible be aware of this impact this may have when analysing the results.

A thought for intranets

If you are working on an intranet project then understanding the organisations hardware can provide accurate information about the target machines specification i.e. screen size, browsers to support, plug ins, etc.

Useful web metric terms

Hit – A request for a file from the web server. Available only in log analysis. The number of hits received by a website is frequently cited to assert its popularity, but this number is extremely misleading and dramatically over-estimates popularity. A single web-page typically consists of multiple (often dozens) of discrete files, each of which is counted as a hit as the page is downloaded, so the number of hits is really an arbitrary number more reflective of the complexity of individual pages on the website than the website’s actual popularity. The total number of visitors or page views provides a more realistic and accurate assessment of popularity.
Page View – A request for a file whose type is defined as a page in log analysis. An occurrence of the script being run in page tagging. In log analysis, a single page view may generate multiple hits as all the resources required to view the page (images, .js and .css files) are also requested from the web server.
Visit / Session – A series of requests from the same uniquely identified client with a set timeout. A visit is expected to contain multiple hits (in log analysis) and page views.
Visitor / Unique Visitor – The uniquely identified client generating requests on the web server (log analysis) or viewing pages (page tagging). A visitor can make multiple visits.
Repeat Visitor – A visitor that has made at least one previous visit. The period between the last and current visit is called visitor recency and is measured in days.
New Visitor – A visitor that has not made any previous visits.
Impression – An impression is each time an advertisement loads on a users screen. Anytime you see a banner that is an impression.

(definitions from Wikipedia)

Methods used for tracking

There are two typical methods for capturing statistical data from your web site, referred to as tracking, which are used by systems like Google analytics, Ominture, Sophus to name a few.

Server log files – browser requests are recorded to track page impressions and server side functions.
Page tagging – embedded javascript passes interaction, page and browser data back to the server (Flash application can use Actionscript in the same way.

There are unique benefits to both methods, server logs will record data whether or not the user has javascript enabled. Page tagging allows page level interactions to be tracked.

More information

Wikipedia has comprehensive entry on the subject of web analytics. Or visit some of these web analytic blogs;
Eric T. Peterson is a veteran and author of several web analytic books.
Jim Novo highly respected
Web Analytics Demystified site
Web Analytics community

I hope this article has proved interesting and thought provoking, if you would like to comment or share your views please leave a response.

Richard Marsh
User Experience Consultant

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