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Pricing Optimization

Maximizing Profits with AI


With retail assortments growing, increasing turnover and with many cases with decreasing store footprints, retailers need new ways to generate profits. Pricing for retailers has typically been driven from the corporate level by established pricing guidelines and competition. Markdowns for many retailers are driven by tried and true techniques with x% at 6 weeks, y% at 8 weeks etc. These traditional methods are insufficient to compete with new online or omni-channel competitors who are better positioned to capture profits through careful price management.


AI is ideal for situations where a retailer needs to optimize across a wide assortment of items based on a variety of factors. AI models can be used to determine the best price for each item using data on seasonality and price elasticity along with real-time inputs on inventory levels and competitive products and prices. The result is more careful markdowns on specific colors or versions to a very specific price to increase demand and maximize profits. Marginal price increases are also possible on some items to capture demand from trends. AI can also be used to provide reasons for pricing suggestions that indicate the key factors when making the pricing suggestion. This is helpful to retailers who want to know why particular items are being suggested for markdowns.


The mission at is to democratize AI for all so that more people across industries can use the power of AI to solve business and social challenges. Leading retail brands like Macy’s, Walgreens, eBay and HEB and more use technology to forecast product demand, create personalized customer experiences, and drive advanced inventory planning. H2O Driverless AI is an award-winning platform for automatic machine learning that empowers data science teams to scale machine learning efforts by dramatically increasing the speed to develop highly accurate predictive models. Driverless AI includes innovative features of particular interest to retail brands including machine learning interpretability (MLI), reason codes for individual predictions, and automatic time series modeling.

Related Case Studies

Daqing Zhao
Director, Advanced Analytics, Macy's

"With H2O we are able to build models quickly so we can find patterns that we can use right away"

Satya Satyamoorthy
Director of Software Development, Nielsen Catalina Solutions

"H2O allowed us to interface directly with our existing application and it scales for our massive data set. There is nothing else like it."