User Journey Funnel Analysis and Drop-Off Prediction Using Machine Learning
User Journey Funnel Analysis and Drop-Off Prediction Using Machine Learning
Dr. Tabasum Guledgudd
Associate Professor
Department of Computer Science and Engineering
A.G.M Rural College of Engineering & Technology,
Varur, Hubballi
tabuagphd20@gmail.com
Mr. Siddartha Hugar
Assistant Professor
Department of Computer Science and Engineering
A.G.M Rural College of Engineering & Technology,
Varur, Hubballi
ktcansi143@gmail.com
Ms. Abhilasha Gangal
Student
Department of Computer Science and Engineering
A.G.M Rural College of Engineering & Technology,
Varur, Hubballi
abhilashagangal06@gmail.com
Ms. Nandini Desai
Student
Department of Computer Science and Engineering
A.G.M Rural College of Engineering & Technology,
Varur, Hubballi
desainandini610@gmail.com
Ms. Netravathi L M
Student
Department of Computer Science and Engineering
A.G.M Rural College of Engineering & Technology,
Varur, Hubballi
netravathilmnetravathilm@gmail.com
Ms. Keerthana
Student
Department of Computer Science and Engineering
A.G.M Rural College of Engineering & Technology,
Varur, Hubballi
keeruk315@gmail.com
ABSTRACT
The expansion of e-commerce platforms has resulted in a surge in customer interactions through browsing, product searching, clickstream navigation, and online transactions. However, customer drop-off during different stages of the purchasing process remains a major challenge in the e-commerce industry. This paper proposes a User Journey Funnel Analysis and Customer Drop-Off Prediction System using Machine Learning techniques to understand customer behaviour and predict churn probability. The proposed system captures user actions such as browsing history, session duration, add-to-cart activities, and checkout behaviour to identify potential customer drop-off points. Logistic Regression, Random Forest, and XGBoost algorithms are used for prediction, while SHAP and LIME techniques improve transparency and business understanding. The proposed framework also integrates dashboard visualization and recommendation systems to improve customer retention, conversion rates, and overall user experience in e-commerce platforms.
Keywords: The development of a user journey funnel, a customer churn prediction machine learning model, Clickstream analytics and recommendation system dashboard.