Incorporating Machine Learning for Value-Based Pricing: An Innovative Framework Combining Quotation and Sales Analytics

This thesis explores a novel approach to pricing strategy, enriching the domain of value-based pricing within the context of B2B companies. By integrating advanced data science and machine learning techniques, the research harnesses the power of quotation data to provide a methodology incorporating both quote acceptance probabilities and price point simulations. This innovative approach promises to offer businesses in the B2B sector a deeper understanding of customer price sensitivity, enabling them to optimize pricing strategies, maximize revenue, and adapt proactively to market dynamics. In doing so, it enriches and advances the field of value-based pricing, offering a more precise and data-driven path to sustainable profitability, specifically tailored for the B2B landscape.