Click-stream data

Click-streams are routes of the user takes when navigating through a site and it shows the page the user arrived on, the order in which the page was reviewed, how long they stayed and timestamp when they left.

Click streams give companies an understanding of their users` behaviours and habits. When recording click-streams organisations can determine how to arrange/customize their site/page, also it helps to identify weaknesses of website. “Based on such metrics as page requests, visit duration, visit pathway, and visit frequency, users were clustered into browsers, searchers and purchasers” (Hofgesang & Kowalczyk, 2005; Moe, 2003). When we take an example in e-commerce, if it is noticed that user mostly leave the site from the shopping cart page, companies might want to consider improving that page by changing the text colours or arrangement to increase conversions. .

Click-streams are commonly used in e-commerce websites to analyse the pages of shop or commonly visits spends time run and what user puts in their cart among other things; however since its quantitative, it is hard to understand  why users behave in the way they did.

The challenges of click-stream data 

Shortcoming of Log Files: there is a possibility that individual people might not be identified by log file, because computer IP addresses are recorded as a log file, not the user itself. So this can result in wrong assumptions while defining user sessions.

Excessive number of pages: low number of page can help to marketers to forecast or undertand the number of path taken by visitor. But if the number of website pages are increasing like thaudans, it is getting really difficult to measure, in other words it may become infinity.

It should not be forgotten that user privacy is becoming a major concern for internet users. Should it be a necessary to ask for a permission for web providers in order to take or use users` behavioral data?

Reference:

Hofgesang, P. and Kowalczyk, W., 2005. Analysing clickstream data: From anomaly detection to visitor profiling. [online] Available at: <https://www.researchgate.net/publication/228722329_Analysing_clickstream_data_From_anomaly_detection_to_visitor_profiling> [Accessed 4 June 2020].

Moe, W., 2003. Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream. [online] 13(1-2), pp.29-39. Available at: <https://www.sciencedirect.com/science/article/abs/pii/S1057740803701740> [Accessed 4 June 2020].