CUSTOMER PURCHASE INTENT PREDICTION USING CLIKSTREAM AND BROWSING BEHAVIOR DATA

Abstract

In today’s competitive e-commerce landscape, accurately predicting customer
purchase intent is critical for increasing sales, improving user experience, and
optimizing marketing strategies. This project, Customer Purchase Intent Prediction
Using Clickstream and Browsing Behavior Data, focuses on leveraging granular user
interaction data—such as page views, click sequences, time spent on different pages,
product interactions, and navigation patterns—to anticipate whether a visitor is likely
to make a purchase. The dataset is collected from an e-commerce platform and
consists of clickstream logs combined with user metadata, such as session duration,
device type, location, and product categories viewed.
The methodology involves data preprocessing, including cleaning, handling missing
values, and encoding categorical features, followed by feature engineering to extract
meaningful indicators of purchase behavior. Machine learning algorithms, such as
Logistic Regression, Random Forest, and Gradient Boosting, are applied to classify
users into purchase-intent categories. Model performance is evaluated using metrics
like accuracy, precision, recall, F1-score, and ROC-AUC, ensuring robust and reliable
predictions.
The insights gained from this analysis can help e-commerce platforms deliver
personalized recommendations, optimize website layouts, target marketing campaigns,
and reduce cart abandonment rates. Furthermore, predicting purchase intent from
browsing behavior supports businesses in understanding consumer psychology,
ultimately enhancing conversion rates and customer satisfaction. This project
demonstrates how clickstream analytics combined with predictive modeling can
transform raw user activity into actionable business intelligence.