Analyzing User Behaviour for Predicting Customer Conversion Rates and Enhancing Marketing Strategies

Abstract

"In today’s world, it is essential for businesses to understand how users behave on websites to improve marketing strategies and anticipate customer conversions. This study delves into the challenges that companies face when trying to predict customer behaviour and discusses how analyzing user interactions can help refine marketing approaches. The primary goal of this research is to investigate the potential of analyzing user behavior on websites to forecast customer conversion rates and enhance strategic marketing efforts. The research project aims to improve marketing strategies by analyzing website user behaviour and predicting customer conversion success rates. By employing tools and data analytic methodologies, the study examines user involvement, consumer behaviour patterns, and their influence on the sales and profits of firms in the digital environment. The study uses a dataset pertaining to user behavior on an electronic commerce platform, where the 'Revenue' column denotes the purchase completion status. This binary classification challenge serves as the foundation for developing eight machine learning models, each fine-tuned with hyperparameters and cross-validation methods. We assessed the models using metrics such as accuracy, precision, recall, and ROC curve analysis. Among them, the Gradient Boosting Classifier stood out as the model due to its high accuracy levels and consistent precision and recall rates across both categories. The models' receiver operating characteristic curve (ROC) of 0.98 demonstrates a harmonious equilibrium between sensitivity (positive rate) and specificity (false positive rate). Furthermore, the research provides recommendations for firms to improve their marketing methods. The techniques encompass enhancing user engagement, introducing exclusive deals on days with significant traffic, optimizing for widely used operating systems, tailoring marketing efforts to specific regions, promoting return visits, introducing specials during weekdays, and implementing a thorough product recommendation system. The objective of this research is to offer practical insights, evidence-based recommendations, and practical solutions for companies to improve customer conversions in the highly competitive online market. The findings of this study could serve as a valuable resource for firms seeking to improve their internet marketing strategies and boost their sales and profits."