Sales Opportunities Lead Qualification in B2B Market

This thesis addresses the challenge of inefficient lead qualification in the business-to-business (B2B) market by applying machine learning techniques to predict the likelihood of winning a sales opportunity. Using a real-world dataset of over 78,000 records and 17 variables, the study aims to improve how sales teams identify and prioritize high-conversion leads. A thorough data preparation process was conducted, including handling of missing values, outlier detection, and under-sampling to resolve class imbalance between won and lost opportunities. After thorough data cleaning, preprocessing, and under-sampling to address class imbalance, five machine learning models were developed: Logistic Regression, Random Forest, Neural Network, Linear SVM, and XGBoost Tree. Each model was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. Among them, XGBoost outperformed all others, achieving an accuracy of 95.57%, precision of 98.3%, recall of 95.9%, and an AUC score of 0.993, indicating its strong ability to differentiate between won and lost opportunities. Feature importance analysis using the F-score method revealed that Sales Velocity, Qualification Board Score, and Opportunity Size were among the top predictors of opportunity success. Statistical tests including Chi-square, Mann-Whitney U, and Kruskal-Wallis supported these findings by highlighting significant relationships between key variables and opportunity status. This study demonstrates that machine learning can enhance B2B lead qualification by providing data-driven insights into what drives successful outcomes. By moving beyond manual scoring and intuition, organizations can improve lead targeting, increase sales efficiency, and better allocate their resources. The research offers a practical framework for integrating AI into the sales decision-making process and contributes to the growing exploration of machine learning in B2B environments.