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Web analytics is the study of the behaviour of website visitors. In a commercial context, web analytics especially refers to the use of data collected from a web site to determine which aspects of the website work towards the business objectives; for example, which landing pages encourage people to make a purchase.
Data collected almost always includes web traffic reports. It may also include e-mail response rates, direct mail campaign data, sales and lead information, user performance data such as click heat mapping, or other custom metrics as needed. This data is typically compared against key performance indicators for performance, and used to improve a web site or marketing campaign's audience response.
Many different vendors provide web analytics software and services.
There are two main technological approaches to collecting web analytics data. The first method, logfile analysis, reads the logfiles in which the web server records all its transactions. The second method, page tagging, uses JavaScript on each page to notify a third-party server when a page is rendered by a web browser.
Web servers have always recorded all their transactions in a logfile. It was soon realised that these logfiles could be read by a program to provide data on the popularity of the website. Thus arose web log analysis software.
In the early 1990s, web site statistics consisted primarily of counting the number of client requests made to the web server. This was a reasonable method initially, since each web site often consisted of a single HTML file. However, with the introduction of images in HTML, and web sites that spanned multiple HTML files, this count became less useful. The first true commercial Log Analyzer was released by IPRO in 1994[1].
Two units of measure were introduced in the mid 1990s to gauge more accurately the amount of human activity on web servers. These were page views and visits (or sessions). A page view was defined as a request made to the web server for a page, as opposed to a graphic, while a visit was defined as a sequence of requests from a uniquely identified client that expired after a certain amount of inactivity, usually 30 minutes. The page views and visits are still commonly displayed metrics, but are now considered rather unsophisticated measurements.
The emergence of search engine spiders and robots in the late 1990s, along with web proxies and dynamically assigned IP addresses for large companies and ISPs, made it more difficult to identify unique human visitors to a website. Log analyzers responded by tracking visits by cookies, and by ignoring requests from known spiders.
The extensive use of web caches also presented a problem for logfile analysis. If a person revisits a page, the second request will often be retrieved from the browser's cache, and so no request will be received by the web server. This means that the person's path through the site is lost. Caching can be defeated by configuring the web server, but this can result in degraded performance for the visitor to the website.
Concerns about the accuracy of logfile analysis in the presence of caching, and the desire to be able to perform web analytics as an outsourced service, led to the second data collection method, page tagging or 'Web bugs'.
In the mid 1990s, Web counters were commonly seen — these were images included in a web page that showed the number of times the image had been requested, which was an estimate of the number of visits to that page. In the late 1990s this concept evolved to include a small invisible image instead of a visible one, and, by using JavaScript, to pass along with the image request certain information about the page and the visitor. This information can then be processed by a web analytics company, and extensive statistics generated. This can be done remotely, by the web analytics company.
The web analytics service also manages the process of assigning a cookie to the user, which can uniquely identify them during their visit and in subsequent visits.
With the increasingly popularity of Ajax-based solutions, an alternative to the use of an invisible image, is to implement a call back to the server from the rendered page. In this case, when the page is rendered on the web browser, a piece of Ajax code would call back to the server and pass information about the client that can then be aggregated by a web analytics company.
Both logfile analysis programs and page tagging solutions are readily available to companies which wish to perform web analytics. In many cases, the same web analytics company will offer both approaches. The question then arises which method a company should choose. There are advantages and disadvantages to each approach.
The main advantages of logfile analysis over page tagging are as follows.
The main advantages of page tagging over logfile analysis are as follows.
Logfile analysis is almost always performed in-house. Page tagging can be performed in-house, but it is more often provided as a third-party service. The economic difference between these two models can also be a consideration for a company deciding which to purchase.
Which solution is cheaper often depends on the amount of technical expertise within the company, the vendor chosen, the amount of activity seen on the web sites, the depth and type of information sought, and the number of distinct web sites needing statistics.
Some companies are now producing programs which collect data through both logfiles and page tagging. By using a hybrid method, they aim to produce more accurate statistics than either method on its own. The first Hybrid solution was produced in 1998 by Rufus Evison who then spun the product out to create a company based upon the increased accuracy of hybrid methods [2].
Other methods of data collection have been used, but are not currently widely deployed. These include integrating the web analytics program into the web server, and collecting data by sniffing the network traffic passing between the web server and the outside world.
There is also another method of the page tagging analysis. Instead of getting the information from the user side, when he opens the page, it’s also possible to let the script work on the server side. Right before a page is sent to a user it then sends the data.
There are no globally agreed definitions within web analytics as the industry bodies have been trying to agree definitions that are useful and definitive for some time. The main bodies who have had input in this area have been Jicwebs(Industry Committee for Web Standards)/ABCe (Auditing Bureau of Circulations electronic, UK and Europe), The WAA (Web Analytics Association, US) and to a lesser extent the IAB (Interactive Advertising Bureau). This does not prevent the following list from being a useful guide, suffering only slightly from ambiguity. Both the WAA and the ABCe provide more definitive lists for those who are declaring their statistics using the metrics defined by either.
The hotel problem is generally the first problem encountered by a user of web analytics. The term was first coined by Rufus Evison explaining the problem at one of the emetrics summits and has now gained popularity as a simple expression of the problem and it's resolution.
The problem is that the unique visitors for each day in a month do not add up to the same total as the unique visitors for that month. This appears to an inexperienced user to be a problem in whatever analytics software they are using. In fact it is a simple property of the metric definitions.
The way to picture the situation is by imagining a hotel. The hotel has two rooms (Room A and Room B).
<table border="1" cellpadding="4" cellspacing="0"> <tr> <td> </td><td>Day 1</td><td>Day 2</td><td>Day 3</td><td>Total</td> </tr> <tr> <td>Room A</td><td>John</td><td>John</td><td>Jane</td><td>2 Unique Users</td> </tr> <tr> <td>Room B</td><td>Jean</td><td>Jane</td><td>Jean</td><td>2 Unique Users</td> </tr> <tr> <td>Total</td><td>2</td><td>2</td><td>2</td><td> ? </td> </tr>
</table>
As the table Shows the hotel has 2 unique users each day over three days. The sum of the totals w.r.t. the days is therefore 6.
During the period each room has had 2 unique users. The sum of the totals w.r.t the rooms is therefore 4.
In actual fact only three visitors have been in the hotel over this period. The problem is that a person who stays in a room for two nights will get counted twice if you count them once on each day, but is only counted once if you are looking at the total for the period. Any software for web analytics will sum these correctly for whatever time period, thus leading to the problem when a user tries to compare the totals.
Another common misconception in web analytics is that the sum of the new visitors and the repeat visitors ought to be the total number of visitors. Again this becomes clear if the visitors are viewed as individuals on a small scale, but still causes a large number of complaints that analytics software cannot be working because of a failure to understand the metrics.
Here the culprit is the metric of a new visitor. There is really no such thing as a new visitor when you are considering a web site from an ongoing perspective. If a visitor makes their first visit on a given day and then returns to the web site on the same day they are both a new visitor and a repeat visitor for that day. So if we look at them as an individual which are they? The answer has to be both, so the definition of the metric is at fault.
A new visitor is not an individual it is a fact of the web measurement. For this reason it is easiest to conceptualise the same facet as a first visit (or first session). This resolves the conflict and so removes the confusion. Nobody expects the number of first visits to add to the number of repeat visitors to give the total number of visitors. The metric will have the same number as the new visitors, but it is clearer that it will not add in this fashion.
On the day in question there was a first visit made by our chosen individual. There was also a repeat visit made by the same individual. The number of first visits and the number of repeat visits will add up to the total number of visits for that day.
Historically, vendors of page-tagging analytics solutions have used third-party cookies, that is cookies sent from the vendor's domain instead of the domain of the website being browsed. Third-party cookies can handle visitors who cross multiple unrelated domains within the company's site, since the cookie is always handled by the vendor's servers.
However, third-party cookies in principle allow tracking an individual user across the sites of different companies, allowing the analytics vendor to collate the user's activity on sites where he provided personal information with his activity on other sites where he thought he was anonymous. Although web analytics companies deny doing this, other companies such as companies supplying banner ads have done so. Privacy concerns about cookies have therefore led a noticeable minority of users to block or delete third-party cookies. In 2005, some reports showed that about 28% of Internet users blocked third-party cookies and 22% deleted them at least once a month [3].
Most vendors of page tagging solutions have now moved to provide at least the option of using first-party cookies (cookies assigned from the client subdomain).
Another problem is cookie deletion. When web analytics depend on cookies to identify unique visitors, the statistics are dependent on a persistent cookie to hold a unique visitor ID. When users delete cookies, they usually delete both first- and third-party cookies. If this is done between interactions with the site, the user will appear as a first-time visitor at their next interaction point. Without a persistent and unique visitor id, conversions, click-stream analysis, and other metrics dependent on the activities of a unique visitor over time, cannot be accurate.
Cookies are used because IP addresses are not always unique to users and may be shared by large groups or proxies. Other methods of uniquely identifying a user are technically challenging and would limit the trackable audience or would be considered suspicious. Cookies are the selected option because they reach the lowest common denominator without using technologies regarded as spyware.
Tracking the amount of activity generated through advertising relationships with external web sites through the referrals reports available in most web analytics packages is significantly less accurate than using unique landing pages.
Referring URLs are an unreliable source of information for the following reasons:
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