Web analytics

There are at least two categories of web analytics, off-site and on-site web analytics.

• Off-site web analytics refers to web measurement and analysis regardless of whether a person owns or maintains 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 more common of the two, measure a visitor's behavior once on a specific 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 a specific website in a commercial context. This data is typically compared against key performance indicators for performance and is used to improve a website or marketing campaign's audience response. Google Analytics and Adobe Analytics are the most widely used on-site web analytics service; although new tools are emerging that provide additional layers of information, including heat maps and session replay.

Historically, web analytics has been used to refer to on-site visitor measurement. However, this meaning has become blurred, mainly because vendors are producing tools that span both categories. Many different vendors provide on-site web analytics software and services. There are two main technical ways of collecting the data. The first and traditional method, server log file analysis, reads the logfiles in which the web server records file requests by browsers. The second method, page tagging, uses JavaScript embedded in the webpage to make image requests to a third-party analytics-dedicated server, whenever a webpage is rendered by a web browser or, if desired, when a mouse click occurs. Both collect data that can be processed to produce web traffic reports.

The fundamental goal of web analytics is to collect and analyze data related to web traffic and usage patterns. The data mainly comes from four sources:[3]

1. Direct HTTP request data: directly comes from HTTP request messages (HTTP request headers).
2. 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.
3. 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.
4. External data: can be combined with on-site data to help augment the website behavior data described above and interpret web usage. For example, IP addresses are usually associated with Geographic regions and internet service providers, e-mail open and click-through rates, direct mail campaign data, sales and lead history, or other data types as needed.

Web servers record some of their transactions in a log file. It was soon realized that these log files 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, website statistics consisted primarily of counting the number of client requests (or hits) made to the web server. This was a reasonable method initially, since each website often consisted of a single HTML file. However, with the introduction of images in HTML, and websites that spanned multiple HTML files, this count became less useful. The first true commercial Log Analyzer was released by IPRO in 1994.[4]

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 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 log file 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 and bigger load on the servers.[5]

Concerns about the accuracy of log file 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 remotely by a web analytics company, and extensive statistics generated.

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. Cookie acceptance rates vary significantly between websites and may affect the quality of data collected and reported.

Collecting website data using a third-party data collection server (or even an in-house data collection server) requires an additional DNS lookup by the user's computer to determine the IP address of the collection server. On occasion, delays in completing a successful or failed DNS lookups may result in data not being collected.

With the increasing 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. This is in some ways flawed by browser restrictions on the servers which can be contacted with XmlHttpRequest objects. Also, this method can lead to slightly lower reported traffic levels, since the visitor may stop the page from loading in mid-response before the Ajax call is made.

Both logfile analysis programs and page tagging solutions are readily available to companies that wish to perform web analytics. In some cases, the same web analytics company will offer both approaches. The question then arises of which method a company should choose. There are advantages and disadvantages to each approach.[6]

The main advantages of log file analysis over page tagging are as follows:

• The web server normally already produces log files, so the raw data is already available. No changes to the website are required.
• 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.
• Logfiles contain information on visits from search engine spiders, which generally are excluded from the analytics tools using JavaScript tagging. (Some search engines might not even execute JavaScript on a page.) Although these should not be reported as part of the human activity, it is useful information for search engine optimization.
• Logfiles require no additional DNS lookups or TCP slow starts. Thus there are no external server calls which can slow page load speeds, or result in uncounted page views.
• The web server reliably records every transaction it makes, e.g. serving PDF documents and content generated by scripts, and does not rely on the visitors' browsers cooperating.

The main advantages of page tagging over log file analysis are as follows:

• Counting is activated by opening the page (given that the web client runs the tag scripts), not requesting it from the server. If a page is cached, it will not be counted by server-based log analysis. Cached pages can account for up to one-third of all page views, which can negatively impact many site metrics.
• Data is gathered via a component ("tag") in the page, usually written in JavaScript, though Java or Flash can also be used. Ajax can also be used in conjunction with a server-side scripting language (such as PHP) to manipulate and (usually) store it in a database, basically enabling complete control over how the data is represented.
• The script may have access to additional information on the web client or on the user, not sent in the query, such as visitors' screen sizes and the price of the goods they purchased.
• 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 log file 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.
• Lately, page tagging has become a standard in web analytics.[7]

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.

• 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 data must be stored and archived, which often grows large quickly. Although the cost of hardware to do this is minimal, the overhead for an IT department can be considerable.
• For Logfile analysis software need to be maintained, including updates and security patches.
• Complex page tagging vendors charge a monthly fee based on volume i.e. number of page views per month collected.

Which solution is cheaper to implement depends on the amount of technical expertise within the company, the vendor chosen, the amount of activity seen on the websites, the depth and type of information sought, and the number of distinct websites needing statistics.

Regardless of the vendor solution or data collection method employed, the cost of web visitor analysis and interpretation should also be included. That is, the cost of turning raw data into actionable information. This can be from the use of third party consultants, the hiring of an experienced web analyst, or the training of a suitable in-house person. A cost-benefit analysis can then be performed. For example, what revenue increase or cost savings can be gained by analyzing the web visitor data?

Some companies produce solutions that collect data through both log-files and page tagging and can analyze both kinds. By using a hybrid method, they aim to produce more accurate statistics than either method on its own. An early hybrid solution was produced in 1998 by Rufus Evison.

With IP geolocation, it is possible to track visitors' locations. Using IP geolocation database or API, visitors can be geolocated to city, region or country level.[8]

IP Intelligence, or Internet Protocol (IP) Intelligence, is a technology that maps the Internet and categorizes IP addresses by parameters such as geographic location (country, region, state, city and postcode), connection type, Internet Service Provider (ISP), proxy information, and more. The first generation of IP Intelligence was referred to as geotargeting or geolocation technology. This information is used by businesses for online audience segmentation in applications such online advertising, behavioral targeting, content localization (or website localization), digital rights management, personalization, online fraud detection, localized search, enhanced analytics, global traffic management, and content distribution.

Clickpath Analysis with referring pages on the left and arrows and rectangles differing in thickness and expanse to symbolize movement quantity.

Click analytics is a special type of web analytics that gives special attention to clicks.

Commonly, click analytics focuses on on-site analytics. An editor of a website uses click analytics to determine the performance of his or her particular site, with regards to where the users of the site are clicking.

Also, click analytics may happen real-time or "unreal"-time, depending on the type of information sought. Typically, front-page editors on high-traffic news media sites will want to monitor their pages in real-time, to optimize the content. Editors, designers or other types of stakeholders may analyze clicks on a wider time frame to help them assess performance of writers, design elements or advertisements etc.

Data about clicks may be gathered in at least two ways. Ideally, a click is "logged" when it occurs, and this method requires some functionality that picks up relevant information when the event occurs. Alternatively, one may institute the assumption that a page view is a result of a click, and therefore log a simulated click that led to that page view.

Customer lifecycle analytics is a visitor-centric approach to measuring that falls under the umbrella of lifecycle marketing. Page views, clicks and other events (such as API calls, access to third-party services, etc.) are all tied to an individual visitor instead of being stored as separate data points. Customer lifecycle analytics attempts to connect all the data points into a marketing funnel that can offer insights into visitor behavior and website optimization.

Other methods of data collection are sometimes used. Packet sniffing collects data by sniffing the network traffic passing between the web server and the outside world. Packet sniffing involves no changes to the web pages or web servers. Integrating web analytics into the web server software itself is also possible.[9] Both these methods claim to provide better real-time data than other methods.

The hotel problem is generally the first problem encountered by a user of web analytics. 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).

As the table shows, the hotel has two unique users each day over three days. 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 this period. The problem is that a person who stays in a room for two nights will get counted twice if they are counted once on each day, but are only counted once if the total for the period is looked at. Any software for web analytics will sum these correctly for the chosen time period, thus leading to the problem when a user tries to compare the totals.

Historically, vendors of page-tagging analytics solutions have used third-party 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.[10] 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. In some cases, the IP address is combined with the user agent in order to more accurately identify a visitor if cookies are not available. However, this only partially solves the problem because often users behind a proxy server have the same user agent. Other methods of uniquely identifying a user are technically challenging and would limit the trackable audience or would be considered suspicious. Cookies reach the lowest common denominator without using technologies regarded as spyware.[11]