SLAM: Sales Lead AMplification Through GenAI and ML for e-Learning Platforms

In the competitive landscape of contemporary business, predictive analytics, particularly in sales lead prediction, has become instrumental for enhancing sales effectiveness and maximizing revenue generation. This study investigates sales lead prediction utilizing machine learning techniques. In the domain of sales, the accurate prediction of leads is deemed essential for optimizing resource allocation and maximizing conversion rates. This study investigates sales lead prediction utilizing machine learning techniques, with a particular focus on the ensemble method of stacking algorithms. The research objective is to improve the predictive accuracy of sales lead identification through the utilization of advanced machine learning methodologies. Through rigorous experimentation and analysis, singular models were first explored, followed by the integration into a stacking ensemble model, achieving an accuracy rate of 94%. Extensive pre-processing techniques have been applied to ensure data quality and feature relevance, facilitating robust model training. The experimental results demonstrate the efficacy of both singular models and the proposed ensemble approach in accurately predicting sales leads. The implications of these findings extend to various sectors reliant on efficient lead management, including marketing, sales, and customer relationship management. By leveraging advanced machine learning techniques such as stacking ensembles, organizations can enhance their lead identification processes, leading to improved conversion rates and overall business performance. This research contributes to the growing body of knowledge in predictive analytics and offers valuable insights for practitioners seeking to optimize their sales strategies through the integration of machine learning technologies.