A Survey for Redefining Success Metrics: B2B Sales in the Machine Learning Era

Abstract

This study looks at the transformational impact of Machine Learning (ML) on sales forecasting, exposing the limitations of traditional methodologies in the face of a dynamic and competitive market. The study employs Linear Regression, Multiple Logistic Regression, Decision Trees, Random Forests, and XG Boost to extract deep patterns and insights from the data. Sales forecasting, a critical component of effective business management, requires exact estimates to guide resource allocation. This technology has the ability to transform firms' decision-making processes by providing actionable insights regarding product positioning, pricing tactics, and general market dynamics. The findings given in this research contribute to the wider conversation on using machine learning for exact sales projections in the context of product attributes and market dynamics.