From virtual buying assistants, personalized product selections, and measuring user behavior — AI is transforming the online shopping experience for consumers and retailers.
Shoppers will be able to easily find the best price for an item, communicate with chatbots for quick customer service, while retailers will be able to better analyze consumer data to predict future buying patterns, create autonomous replenishing systems and also save money and time on customer service by utilizing chatbots.
Machine learning, a subfield of artificial intelligence, is the ability for computers to learn without explicit instructions. Consider Target’s online shopping experience; the retail discount giant has employed machine learning with algorithms that can identify whether a shopper is pregnant, and offer discounts or coupons that are likely to make that shopper more loyal its brand.
Target had employed statisticians and data scientists to use purchase behavior to identify shoppers who were pregnant and then market to them. Presumably, those statisticians and data scientists used data from Target’s baby registry system to identify pregnancy-driven buying patterns.
Target is, of course, is not alone. Netflix uses AI to provide personalized recommendations to subscribers based on their previous streaming habits. Under Armour, with the help of IBM’s Watson, uses AI to help its customers track their health and fitness activities.
Can’t find shoes to match that dress? A quick chat with a bot makes it possible understand the behavior of individual users (AB testing) and offer the best personalized solution possible, thereby maximizing conversion rates. This is as opposed to AB testing for groups of people and arriving at an average solution. This bot/user exchange will also allow retailers to better analyze consumer data to predict future buying patterns, create autonomous replenishing systems and also save money and time on customer service.
A German eCommerce merchant, Otto, uses AI to lower the number of products that customers return, which costs the firm millions of euros a year. Otto sells merchandise from other brands, but does not stock those goods itself. However, customers dislike multiple shipments and prefer to receive everything at once. What’s costing the company is shipping delays until all the orders are ready for fulfillment, or lots of boxes arriving at different times.
Partnering with Blue Yonder, a startup company specializing in machine learning, Otto developed a system to better forecast what customers are going to buy so that a few goods could be ordered ahead of time. The system has proved reliable—it predicts with 90% accuracy what will be sold within 30 days—that Otto allows it automatically to purchase around 200,000 items a month from third-party brands with no human intervention.