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Web analytics is the study of online behaviour in order to improve it. There are two categories; off-site and on-site web analytics.

Off-site web analytics refers to web measurement and analysisirrespective of whether you own or maintain a website. It includes themeasurement 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 measure a visitor's journey once on your website. This includes its drivers and conversions; for example, which landing pages encourage people to make a purchase. On-site web analytics measures theperformance of your website in a commercial context. This data istypically compared against key performance indicators for performance, and used to improve a web site or marketing campaign's audience response.

Historically, web analytics has referred to on-site visitormeasurement. However in recent years this has blurred, mainly becausevendors are producing tools that span both categories.

On-site web analytics technologies

Many different vendors provide on-site web analytics software and services. There are two main technological approaches to collecting the 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. Both collect data that can be processed to produce web traffic reports.

In addition other data sources may also be added to augment thedata. For example; 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.

Web server logfile analysis

Web servers have always recorded all their transactions in alogfile. It was soon realised that these logfiles could be read by aprogram 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 (or hits)made to the web server. This was a reasonable method initially, sinceeach web site often consisted of a single HTML file. However, with theintroduction of images in HTML, and web sites that spanned multipleHTML files, this count became less useful. The first true commercialLog Analyzer was released by IPRO in 1994.

Two units of measure were introduced in the mid 1990s to gauge moreaccurately 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 visitwas defined as a sequence of requests from a uniquely identified clientthat expired after a certain amount of inactivity, usually 30 minutes.The page views and visits are still commonly displayed metrics, but arenow 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 apage, the second request will often be retrieved from the browser'scache, and so no request will be received by the web server. This meansthat the person's path through the site is lost. Caching can bedefeated by configuring the web server, but this can result in degradedperformance for the visitor to the website.

Page tagging

Concerns about the accuracy of logfile analysis in the presence ofcaching, and the desire to be able to perform web analytics as anoutsourced service, led to the second data collection method, pagetagging or 'Web bugs'.

In the mid 1990s, Web counters were commonly seen — these were images included in a web page thatshowed the number of times the image had been requested, which was anestimate of the number of visits to that page. In the late 1990s thisconcept evolved to include a small invisible image instead of a visibleone, and, by using JavaScript, to pass along with the image requestcertain information about the page and the visitor. This informationcan then be processed remotely by a web analytics company, andextensive statistics generated.

The web analytics service also manages the process of assigning acookie to the user, which can uniquely identify them during their visitand in subsequent visits.

With the increasing popularity of Ajax-basedsolutions, an alternative to the use of an invisible image, is toimplement a call back to the server from the rendered page. In thiscase, when the page is rendered on the web browser, a piece of Ajaxcode would call back to the server and pass information about theclient that can then be aggregated by a web analytics company. This isin some ways flawed by browser restrictions on the servers which can becontacted with XmlHttpRequest objects.

Logfile analysis vs page tagging

Both logfile analysis programs and page tagging solutions arereadily available to companies that wish to perform web analytics. Insome cases, the same web analytics company will offer both approaches.The question then arises of which method a company should choose. Thereare advantages and disadvantages to each approach.

Advantages of logfile analysis

The main advantages of logfile analysis over page tagging are as follows.

• The web server normally already produces logfiles, so the raw data is already available. To collect data via page tagging requires changes to the website.
• The web server reliably records every transaction it makes. Page tagging relies on the visitors' browsers co-operating, which a certain proportion may not do (for example, if JavaScript is disabled).
• The data is on the company's own servers, and is in a standard, rather than a proprietary, format. This makes it easy for a company to switch programs later, use several different programs, and analyze historical data with a new program. Page tagging solutions involve vendor lock-in.
• Logfiles contain information on visits from search engine spiders. Although these should not be reported as part of the human activity, it is useful information for search engine optimization.

Advantages of page tagging

The main advantages of page tagging over logfile analysis are as follows.

• The JavaScript is automatically run every time the page is loaded. Thus there are fewer worries about caching.
• It is easier to add additional information to the JavaScript, which can then be collected by the remote server. For example, information about the visitors' screen sizes, or the price of the goods they purchased, can be added in this way. With logfile analysis, information not normally collected by the web server can only be recorded by modifying the URL.
• Page tagging can report on events which do not involve a request to the web server, such as interactions within Flash movies, partial form completion, mouse events such as onClick, onMouseOver, onFocus, onBlur etc.
• The page tagging service manages the process of assigning cookies to visitors; with logfile analysis, the server has to be configured to do this.
• Page tagging is available to companies who do not have access to their own web servers.

Economic factors

Logfile analysis is almost always performed in-house. Page taggingcan be performed in-house, but it is more often provided as athird-party service. The economic difference between these two modelscan also be a consideration for a company deciding which to purchase.

• Logfile analysis typically involves a one-off software purchase; however, some vendors are introducing maximum annual page views with additional costs to process additional information. In addition to commercial offerings, several open-source logfile analysis tools are available free of charge.
• For Logfile analysis you have to store and archive your own data, which often grows very large and quickly. Although the cost of hardware to do this is minimal, the overhead for an IT department can be considerable. For example, if you run out of disk space your database will start over writing and this can often be irreparable.
• For Logfile analysis you need to maintain the software, including updates and security patches.
• Complex page tagging vendors charge a monthly fee based on volume i.e. number of pageviews per month collected. However, some vendors now offer page tagging solutions for free. This includes Google Analytics (2005), Microsoft Analytics (beta 2008) and Yahoo IndexTools (2008).

Which solution is cheaper to implement depends on the amount oftechnical expertise within the company, the vendor chosen, the amountof activity seen on the web sites, the depth and type of informationsought, and the number of distinct web sites needing statistics.

Regardless of the vendor solution or data collection methodemployed, the cost of web visitor analysis and interpretation shouldalso be included. That is, the cost of turning raw data into actionableinformation. This can be from the use of third party consultants, thehiring of an experienced web analyst, or the training of a suitablein-house person. A cost-benefit analysis can then be performed. For example, what revenue increase or cost savings can be gained by analysing the web visitor data?

Key definitions

There are no globally agreed definitions within web analytics as theindustry bodies have been trying to agree definitions that are usefuland definitive for some time. The main bodies who have had input inthis 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 followinglist from being a useful guide, suffering only slightly from ambiguity.Both the WAA and the ABCe provide more definitive lists for those whoare declaring their statistics using the metrics defined by either.

• 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.

• First Visit / First Session - A visit from a visitor who has not made any previous visits.

• Visitor / Unique Visitor / Unique User - The uniquely identified client generating requests on the web server (log analysis) or viewing pages (page tagging) within a defined time period (i.e. day, week or month). A Unique Visitor counts once within the timescale. A visitor can make multiple visits. The Unique User is now the only mandatory metric for an ABCE audit.

• 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. This definition creates a certain amount of confusion (see common confusions below), and is sometimes substituted with analysis of first visits.

• Impression - An impression is each time an advertisement loads on a user's screen. Anytime you see a banner, that is an impression.

• Singletons - The number of visits where only a single page is viewed. While not a useful metric in and of itself the number of singletons is indicative of various forms of "Click Fraud" as well as being used to calculate bounce rate and in some cases to identify automatons ("bots").

• Bounce Rate - The percentage of visits where the visitor enters and exits at the same page without visiting any other pages on the site in between.

• % Exit - The percentage of users who exit from a page.

• Visibility time - The time a single page (or a blog, Ad Banner...) is viewed.

• Session Duration - Average amount of time that visitors spend on the site each time they visit. This metric can be complicated by the fact that analytics programs can not measure the length of the final page view. Also, if a visit comes back to the site within a short period of time, that can be measured as a continuation of the first session.

• Page View Duration - Average amount of time that visitors spend on each page of the site. As with Session Duration, this metric is complicated by the fact that analytics programs can not measure the length of the final page view.

• Depth / Page Views per Session - Depth is the average number of page views a visitor consumes before ending their session. It is calculated by dividing total number of page views by total number of sessions and is also called Page Views per Session or PV/Session.

• Frequency / Session per Unique - Frequency measures how often visitors come to a website. It is calculated by dividing the total number of sessions (or visits) by the total number of unique visitors. Sometimes it is used to measure the loyalty of your audience.

Common confusions in web analytics

The hotel problem

The hotel problem is generally the first problem encountered by auser of web analytics. The term was first coined by Rufus Evisonexplaining the problem at one of the Emetrics Summits and has now gained popularity as a simple expression of the problem and its resolution.

The problem is that the unique visitors for each day in a month donot add up to the same total as the unique visitors for that month.This appears to an inexperienced user to be a problem in whateveranalytics software they are using. In fact it is a simple property ofthe metric definitions.

The way to picture the situation is by imagining a hotel. The hotel has two rooms (Room A and Room B).

As the table shows, the hotel has two unique users each day over threedays. The sum of the totals with respect to the days is therefore six.

During the period each room has had two unique users. The sum of the totals with respect to the rooms is therefore four.

Actually only three visitors have been in the hotel over thisperiod. The problem is that a person who stays in a room for two nightswill get counted twice if you count them once on each day, but is onlycounted once if you are looking at the total for the period. Anysoftware for web analytics will sum these correctly for whatever timeperiod, thus leading to the problem when a user tries to compare thetotals.

New visitors + repeat visitors unequal to total visitors

Another common misconception in web analytics is that the sum of thenew visitors and the repeat visitors ought to be the total number ofvisitors. Again this becomes clear if the visitors are viewed asindividuals on a small scale, but still causes a large number ofcomplaints that analytics software cannot be working because of afailure to understand the metrics.

Here the culprit is the metric of a new visitor. There is really nosuch thing as a new visitor when you are considering a web site from anongoing perspective. If a visitor makes their first visit on a givenday and then returns to the web site on the same day they are both anew visitor and a repeat visitor for that day. So if we look at them asan individual which are they? The answer has to be both, so thedefinition of the metric is at fault.

A new visitor is not an individual; it is a fact of the webmeasurement. For this reason it is easiest to conceptualise the samefacet as a first visit (or first session). This resolves the conflictand so removes the confusion. Nobody expects the number of first visitsto add to the number of repeat visitors to give the total number ofvisitors. The metric will have the same number as the new visitors, butit is clearer that it will not add in this fashion.

On the day in question there was a first visit made by our chosenindividual. There was also a repeat visit made by the same individual.The number of first visits and the number of repeat visits will add upto the total number of visits for that day.