Wayfair Inc. has 37,173 kinds of coffee mugs for sale. Factor in different colors, sizes or materials, and the range of options rises above 70,000. It's Jim Miller's job to help shoppers find the mug they want--along with a set of espresso cups, a waffle iron or other products they didn't know they wanted. In the past five years, the company's success rate has jumped 50%, measured by the number of clicks it takes for a customer to add an item to their carts and how often they buy those items, among other variables, according to Mr. Miller, the Boston-based online retailer's chief technology officer. He credits the gains to advances in smart software.

He credits the gains to advances in smart software. Rather than asking customers to browse through the entire catalog of mugs, he says, algorithms, artificial intelligence and troves of data "are doing the work behind the scenes." Since the coronavirus outbreak, online retailers like Wayfair, Etsy Inc. ETSY 3.99% and Pinterest Inc. PINS -0.97% are ratcheting up efforts to leverage data from a surge in e-commerce to get better at helping customers find what they are looking for--even when they don't know what that is. To do that, these Web-only stores are supercharging search-and-recommendation engines by feeding data into sophisticated algorithms, building predictive models with a level of accuracy unimaginable just a few years ago. Not all of the capabilities are new--algorithms have been around for decades.

Retailers are increasingly applying artificial intelligence (AI) to better personalize customer service initiatives, and momentum is increasing. Fifty five percent of retailers plan to leverage AI technology within three years, and applications will vary, according to the "2018 Customer Experience/Unified Commerce Survey," from Boston Retail Partners (BRP). AI's ability to learn from data, identify patterns and make decisions with minimal human intervention makes it a credible option to improve customer service. Some retailers use it to offer purchasing suggestions based on customer responses to a series of questions. Others use it to pinpoint the most convenient time of day to reach out to consumers with product suggestions, which are also based on past clicks and website visit data.