Challenges

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.

Opportunity

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.