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Smart Pricing for Biotech: Leveraging CPQ and AI to Maximize Value
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 4 |
| Year of Publication: 2025 |
| Authors: Sivasai Nadella |
10.5120/ijca2025924904
|
Sivasai Nadella . Smart Pricing for Biotech: Leveraging CPQ and AI to Maximize Value. International Journal of Computer Applications. 187, 4 ( May 2025), 27-34. DOI=10.5120/ijca2025924904
Abstract
Another very important phenomenon in the current highly competitive world is the implementation of dynamic pricing since it is characterized by the ability to change the price in real time depending on the market demand, situation, and competitors' strategies. In the case of biotechnology products, where the price is largely determined by the costs that are likely to have been incurred on research and development, regulatory issues, market factors, and competitor strategies, dynamic pricing strategies go a long way in improving the profits and satisfaction of the valued customers. It is a descriptive research paper on the biotech industry that incorporates Machine Learning (ML) into Configure, Price, Quote (CPQ) models for applying dynamic pricing strategies in price management. The modules of CPQ involve simplification of different configurations of products, besides automating the pricing and quoting process. The graphical model in the paper employs both supervised and unsupervised learning to analyze the formulation of past prices, customer purchasing patterns, and other market trends. This paper outlines the effects of utilizing ML for dynamic pricing methods on each objective of maximization of revenues, optimization of operations, and competitive advantage. From the real-world biotech firm datasets, the analyses prove that the ML-driven CPQ model can increase the accuracy of prices for new offers and increase customers’ satisfaction levels, thereby driving growth for the firm.
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