Abstract
Dynamic pricing models are extremely relevant in today's marketplaces where companies can switch their profitability based on real-time data as it shifts in the markets. The paper is an attempt to explain the theoretical frames of dynamic pricing as well as its technological and real applications in various marketplaces. A real-life investigation into how ML and AI create price optimization mechanisms, thereby impacting the profitability levels in marketplaces. Demand forecasting, market segmentation, and elasticity are the major drivers measured. The paper also took into consideration the ethical and regulatory concerns of dynamic pricing. The bottom line was that dynamic pricing would immensely improve profitability if managed in the proper sense but should always be kept under check and readjusted to avoid a consumer reaction and regulatory attention.
Published Version
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