website can learn about how to improve their site. 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. Analyzing the data of clients that visit a company website can be important in order to remain competitive. This analysis can be used to generate two findings for the company, the first being an analysis of a user's clickstream while using a website to reveal usage patterns, which in turn gives a heightened understanding of customer behaviour. This use of the analysis creates a user profile that aids in understanding the types of people that visit a company's website. As discussed in Van den Poel & Buckinx (2005), clickstream analysis can be used to predict whether a customer is likely to purchase from an e-commerce website. Clickstream analysis can also be used to improve customer satisfaction with the website and with the company itself. This can generate a business advantage, and be used to assess the effectiveness of advertising on a web page or site. Opinions 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. Implications 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. Unauthorized clickstream data collection is considered to be spyware. However, authorized clickstream data collection comes from organizations that use opt-in panels to generate market research using panelists who agree to share their clickstream data with other companies by downloading and installing specialized clickstream collection agents. Challenges The number of paths