Machine Learning Models to Manage Cloud Computing Revenue Opportunities

Authors: Pavan Nithin Mullapudi

DOI: https://doi.org/10.5281/zenodo.15206895

Short DOI: https://doi.org/g9fczz

Country: USA

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Abstract: The application of machine learning (ML) techniques to sales pipeline management represents a significant advancement in how cloud computing providers optimize their revenue generation processes. This paper presents a comprehensive framework for leveraging ML to predict and prioritize revenue opportunities in cloud computing environments. Our approach demonstrates how historical sales data, usage patterns, and firmographic information can be systematically analyzed to develop predictive models that significantly outperform traditional heuristic-based prioritization methods. Experimental results show that properly implemented ML models can achieve precision scores up to 75% compared to 45% for conventional approaches, enabling sales teams to focus on high-value opportunities and improve overall conversion rates. The methodology outlined emphasizes rigorous training set construction, feature engineering, model calibration, and evaluation practices applicable across the cloud computing industry.

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Paper Id: 232269

Published On: 2022-10-04

Published In: Volume 10, Issue 5, September-October 2022

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