Novel pricing strategies for revenue maximization and demand learning using an exploration-exploitation framework

Area of Science:

• Economics
• Machine Learning
• Optimization Algorithms

Background:

• The price demand relation is fundamental to business, impacting revenue.
• Accurate parameter estimation is crucial but challenging due to rapid market changes.
• Balancing revenue maximization and demand learning presents a learn/earn trade-off.

Purpose of the Study:

• To address the learn/earn trade-off in price demand function estimation.
• To develop an efficient method for parameter estimation using limited data.
• To improve revenue and demand learning simultaneously.

Main Methods:

• Designed a novel objective function combining revenue and parameter estimation error.
• Developed recursive algorithms to optimize the new objective function.
• Evaluated the proposed method against existing approaches.

Main Results:

• The new objective function effectively balances revenue maximization and demand learning.
• Recursive algorithms efficiently optimize the combined objective.
• The proposed method demonstrates superior performance compared to existing techniques.

Conclusions:

• The developed approach offers an effective solution to the learn/earn trade-off in price demand modeling.
• This method enables efficient and accurate price demand parameter estimation.
• The findings have significant implications for revenue optimization in dynamic markets.