A click path (clickstream) is the sequence of hyperlinks one or more website visitors follows on a given site, presented in the order viewed. A visitor's click path may start within the website or at a separate 3rd party website, often a search engine results page, and it continues as a sequence of successive webpages visited by the user. Click paths take call data and can match it to ad sources, keywords, and/or referring domains, in order to capture data.
While navigating the World Wide Web, a "user agent" (web browser) makes requests to another computer, known as a web server, every time the user selects a hyperlink. Most web servers store information about the sequence of links that a user "clicks through" while visiting the websites that they host in log files for the site operator's benefit. The information of interest can vary and may include: information downloaded, webpage visited previously, webpage visited afterwards, duration of time spent on page, etc. The information is most useful when the client/user is identified, which can be done through website registration or record matching through the client's Internet service provider (ISP).
As the world of online shopping grows, it is becoming easier for the privacy of individuals to become exploited. There have many cases of email addresses, phone numbers, and other personal information that have been stolen illegally from shoppers, clients, and many more to be used by third parties. These third parties can range from advertisers to hackers. There are consumers who actually benefit from this by gaining more targeted advertising and deals, but most are harmed by the lack of privacy. As the world of technology grows, consumers are more and more in risk of losing privacy.
The growing e-commerce industry has made it necessary to tailor to the needs and preferences of consumers. Click path data can be used to personalize product offerings. By using previous click path data, websites can predict what products the user is likely to purchase. Click path data can contain information about the user's goals, interests, and knowledge and therefore can be used to predict their future actions and decisions. By using statistical models, websites can potentially increase their operating profits by streamlining results based on what the user is most likely to purchase.
Researchers who stand behind click path analysis note that "the path analytical method estimates a system of equations that specify all the possible causal linkages among a set of variables". Further click paths enable researchers to break down correlations among variables into direct or indirect and spurious components. Finally click path analysis "helps researchers disentangle the complex interrelationships among variables and identify the most significant pathways involved in predicting an outcome". But even with these advantages the technique is still critiqued by many due to its large margin of error. The model must assume that each variable is "an exact manifestation of the theoretical concepts underlying them and reasonably free of measurable error". Another "casualty in the hypothesized model is that the path must flow in one direction (no feedback loops or bidirectional causality), otherwise the model cannot be solved with ordinary least squares regression techniques". Lastly, because models are based on correlations, path analysis "cannot demonstrate causality or the direction of causal effects". Due to these limitations, many believe that click path analysis tends to be a waste of time, money, and resources.
Most websites store data about visitors to the site through click path. The information is typically used to improve the website and deliver personalized and more relevant content. In addition, the data results can not only be used by a designer to review, improve or redesign their website, but can also be used to model a user's browsing behaviour. In the online world of e-commerce, information collected through click path allows advertisers to construct personal profiles and use them to individually target consumers much more effectively than ever before; as a result, advertisers create more relevant advertising and efficiently spend advertising dollars. Meanwhile, in the wrong hands click path data poses a serious threat to personal privacy.
The number of paths a user can potentially take greatly increases depending on the number of pages on that particular website. Many tools to determine path analysis are too linear and do not account for the complexity of internet usage. In most cases, less than 5% of users follow the most common path. However, even if all users used the same path, there is still no way to tell which page is the most influential in determining behavior. Even in more linear forms of path analysis, where they can see where most customers drop off the website, the "why?" factor is still missed. The main challenge of path analysis lies in the fact that it tries to regulate and force users to follow a certain path, when in reality users are very diverse and have specific preference and opinions.
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