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Product Recommendations

Increasing Cross-sell and Upsell with AI


McKinsey and Company reports that as much as 35% of Amazon’s onsite revenue is generated by its recommendation engine. Retailers know that recommended products can generate increased cross-sell and up-sell opportunities, but few have actually implemented truly personalized product recommendations on their sites, in emails or other channels. If they contain recommended products at all, many websites and emails still contain static recommendations where everyone who visits that page or receives the email sees the same products. While these recommendations are better than nothing, they are far from the individualized items that consumers now expect.


Every retailer wants to have high quality recommended products everywhere from emails to landing pages, their homepage to categories, product pages and cart to drive increased cross-sell and upsell. AI can be used to find the patterns in customer behavior from clickstream data, prior purchases, demographics and preferences that lead to the best product recommendations for each individual consumer. For example, when a customer visits the homepage of a website, the products can even categories displayed can be based on their known preferences and prior purchases such that the items displayed are highly relevant to them. For emails, product recommendations can be generated as an email is being opened so that the recommended products are relevant based on the customer’s most recent browsing behavior and purchases. With AI, highly relevant and personalized product recommendations, websites, email campaigns, call center agents, and mobile applications can provide personalized experiences for consumers that drive increased conversion rates, basket size and customer loyalty.


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."