This study presents a framework for dynamic assortment selection and pricing using a censored multinomial logit choice model, where sellers can optimize product offerings and prices based on buyer preferences and valuations. By employing a Lower Confidence Bound pricing strategy alongside Upper Confidence Bound or Thompson Sampling approaches, the proposed algorithms achieve significant regret bounds, which are validated through simulations.

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The article outlines five innovative pricing experiments that businesses can implement to optimize their pricing strategies. These experiments are designed to help companies better understand customer behavior and maximize revenue through strategic pricing adjustments. Each experiment emphasizes the importance of testing and iterating based on customer feedback and market responses.