Deciding which prices to apply to products is one of the most important and complex decisions a company makes. Human analysis of pricing-related data is extremely time consuming and error prone, slowing time to market and jeopardizing profitability.

This paper examines how machine learning models can learn the relationships between complex variables like customer behavior, competition impact, seasonality, and how these interact with product pricing to affect demand, enabling organizations to increase profits and reduce labor.

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