Estimating Customer Potential in Cloud Computing Using Semi-Supervised Learning

Author(s)	Pavan Nithin Mullapudi
Country	United States
Abstract	Estimating customer potential is crucial for organizations, especially in the cloud computing domain. Traditional methods often rely on total addressable spend (TAS) estimates, which can be inaccurate or incomplete. This paper explores the use of semi-supervised learning techniques [1] to identify customers with untapped potential on the cloud. By leveraging external datasets and positive-unlabeled (PU) learning algorithms, we aim to improve the accuracy of customer potential estimation and provide a more effective approach for finance organizations in the cloud space. Our results demonstrate that PU learning models, particularly those utilizing technographic embeddings and bagging techniques, can significantly outperform traditional TAS-based methods.
Keywords	Machine Learning, Cloud Computing, Semi-Supervised Learning, Revenue Prediction
Field	Engineering
Published In	Volume 2, Issue 10, October 2021
Published On	2021-10-06
Cite This	Estimating Customer Potential in Cloud Computing Using Semi-Supervised Learning - Pavan Nithin Mullapudi - IJLRP Volume 2, Issue 10, October 2021. DOI 10.5281/zenodo.15051185
DOI	https://doi.org/10.5281/zenodo.15051185
Short DOI	https://doi.org/g88z2m

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