Conversion Rate Optimization in E-commerce: Implementing ML algorithms to identify clickstream patterns

Khan, Tooba (2024) Conversion Rate Optimization in E-commerce: Implementing ML algorithms to identify clickstream patterns. Masters thesis, Dublin, National College of Ireland.

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Abstract

Today’s digital revolutionization in the world has immensely changed the landscape of businesses, specifically the retail segment with a 360-paradigm shift. The digital evolution in the E-commerce has essentially redesigned the commercial operations, disregarding conventional brick-and-mortar operations with digital platforms. It has facilitated continuous exchange of any form of information (transactional or informative) between buyers and sellers via online channels. Analyzing online behavior of users on websites has been a key aspect for idealizing of business performance metrics and growth. The major gap addressed in this research, is the behavioral patterns identification of website users, while using clickstream dataset from E-commerce fashion store to optimize conversion rate. Since, existing techniques from industry have not been enough to identify the factors relating link between clickstream pattern and user purchase predictions. This model intends to help e-commerce businesses to analyze and evaluate their conversion rate strategies. Relevant machine learning methodologies have been employed on clickstream data from an e-commerce (fashion store) to analyze the clickstream pattern from online sessions and their purchase intent.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jameel Syed, Muslim UNSPECIFIED
Uncontrolled Keywords:	Clickstream; Intent identification; Behavioral analysis; Conversion rate; Machine Learning Algorithms; Imbalance class handling
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	02 Jul 2025 16:19
Last Modified:	02 Jul 2025 16:19
URI:	https://norma.ncirl.ie/id/eprint/7991

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