2015•CiiT international journal of data mining and knowledge engineeringRequires access

Clickstream Analysis of User Profile

Mihir Patel, Shyam Deshmukh

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Abstract

Clickstream data-the trail of digital footprints left by users as they click their way through a website-is loaded with valuable customer information for businesses. It is typically captured in semi-structured website log files. By discovering key strengths and limitations of these data used for research in marketing and user behaviour. The behaviour of users can be receive in log files store in web servers. By learning this user behaviour and making their profile and target them for different marketing techniques and promotions. Analysis of clickstream data and other user data gives a granular look at how individual customer segments are using the website. As a result bring in actionable insights to help personalize the user experience and convince more web visitors from browsers to buyers. In this research, we will analysis these logs and generate the user profiles. Website administrator use these user behaviour profile for internet marketing and online advertising promotions on website. This paper includes the solution proposal on to improve conversion of user by various web analytics matrices with discriminant algorithm.

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What this paper is about

Clickstream data-the trail of digital footprints left by users as they click their way through a website-is loaded with valuable customer information for businesses. It is typically captured in semi-structured website log files. By discovering key strengths and limitations of these data used for research in marketing and user behaviour. The behaviour of users can be receive in log files store in web servers. By learning this user behaviour and making their profile and target them for different marketing techniques and promotions. Analysis of clickstream data and other user data gives a granular look at how individual customer segments are using the website. As a result bring in actionable insights to help personalize the user experience and convince more web visitors from browsers to buyers. In this research, we will analysis these logs and generate the user profiles. Website administrator use these user behaviour profile for internet marketing and online advertising promotions on website. This paper includes the solution proposal on to improve conversion of user by various web analytics matrices with discriminant algorithm.

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Available abstract

Clickstream data-the trail of digital footprints left by users as they click their way through a website-is loaded with valuable customer information for businesses. It is typically captured in semi-structured website log files. By discovering key strengths and limitations of these data used for research in marketing and user behaviour. The behaviour of users can be receive in log files store in web servers. By learning this user behaviour and making their profile and target them for different marketing techniques and promotions. Analysis of clickstream data and other user data gives a granular look at how individual customer segments are using the website. As a result bring in actionable insights to help personalize the user experience and convince more web visitors from browsers to buyers. In this research, we will analysis these logs and generate the user profiles. Website administrator use these user behaviour profile for internet marketing and online advertising promotions on website. This paper includes the solution proposal on to improve conversion of user by various web analytics matrices with discriminant algorithm.

Key concepts: Clickstream, Computer science, Web analytics, World Wide Web, Online advertising, Web mining, The Internet, Digital marketing

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