Dynamic pricing strategies towards strategic consumers under demand learning

Area of Science:

• Operations Research
• Marketing Science
• Econometrics

Background:

• Dynamic pricing is crucial in uncertain markets with strategic consumers.
• Demand learning, using Bayesian updating, can improve pricing strategies.
• The impact of price guarantees on dynamic pricing with demand learning is not fully understood.

Purpose of the Study:

• To analyze the conditions under which demand learning is beneficial in a two-stage dynamic pricing framework.
• To compare dynamic pricing strategies with and without price guarantees when incorporating demand learning.
• To provide managerial insights for optimizing dynamic pricing strategies based on market conditions and product lifecycle.

Main Methods:

• A two-stage dynamic pricing model incorporating demand learning and strategic consumers.
• Bayesian updating for market size estimation and learning.
• Analysis of different scenarios based on initial market size estimates and uncertainty.

Main Results:

• Demand learning offers marginal benefits without price guarantees only when market size is accurately estimated or overestimated with low uncertainty.
• With price guarantees, demand learning is effective for inaccurate estimates and high market uncertainty, but can cause revenue loss if estimates are certain.
• The optimal strategy (with or without price guarantee) depends on the product's market entry stage, initial market size estimation accuracy, and inventory levels.

Conclusions:

• Dynamic pricing strategies must be tailored to market uncertainty and demand learning capabilities.
• Price guarantees can enhance demand learning effectiveness under specific conditions but may be detrimental otherwise.
• Managerial insights suggest that for new products with overestimated markets, dynamic pricing with price guarantees and demand learning is optimal.