By Angus Loten
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. 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. and Pinterest Inc. 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. But the rapid expansion of computing power and
cloud storage in recent years has enabled sellers to gather and
crunch data on a massive scale.
Shoppers generate data on retail websites every time they place
an item in a virtual cart, hover over product pages, click on
product recommendations and ultimately make a purchase. Stores
create more-robust customer profiles by adding their shoppers' ages
and genders, where they live, the local weather or seasonal events
and holidays -- and in some cases data drawn from all over the
internet by third-party services.
Two greatly improved tools that most online retailers use to
turn that data into sales are computer vision and natural-language
processing, says Bob Hetu, a vice president and analyst in tech
research firm Gartner Inc.'s retail industry services unit. The
former helps to index products in a website's virtual catalog using
visual cues, while the latter aggregates and learns from words that
shoppers use when describing products they are looking for. Both
rely on algorithms powered by machine learning, a subset of
artificial intelligence.
Where standard algorithms generate results based on instructions
for very specific input, AI algorithms go further by using the
results they produce to then fine-tune the instructions and "learn"
how to handle new input, repeating the cycle over and over
again.
Gartner predicts that within the next five years, the world's 10
largest retailers will be using AI models as the backbone of
product searches and recommendations -- and as a competitive
edge.
Companies keep their algorithms a closely guarded secret.
Amazon.com Inc.'s AI algorithms scour millions of listings for
items matching a buyer's search query, weighing more than 100
variables, such as past purchases, age, gender, and a long list of
criteria known only to company insiders.
Computer vision
Online retailers use computer-vision models to automate the
process of assigning keywords, or tags, to identify individual
products. The systems are designed to "see" products and label them
with a list of attributes, such as chair, teak, Scandinavian, and
so on. A decade ago, Mr. Hetu says, online product photos were
manually tagged with a handful of descriptive terms.
The problem is two people manually tagging items can look at the
same chair, or garment, or bedding and pick out very different
attributes, Mr. Hetu says, "or just get it wrong."
Computer vision, also known as object recognition, recognizes
dozens of features from a product photo and compares them with
similar items in a store's database, quickly tagging any
overlapping attributes -- like a Venn diagram that has furniture as
the overall subject and black, leather and contemporary as three
circles that intersect at the center.
Mr. Miller, a former vice president of Amazon.com Inc.'s
supply-chain operations, says Wayfair trains its computer-vision
algorithm with a combination of supplier and customer photos, and
photos generated by three-dimensional imagery of a given piece of
furniture.
The algorithm captures design features, materials, styles,
color, vintage and a vast array of other elements. It then takes
that information, along with tags added by professional designers,
and applies it to similar items in the store's extensive line of
products. The richer the list of keywords tagged to a product, the
more likely an algorithm is to produce relevant search results.
The model improves over time by learning from its successes and
from its mistakes, such as when a mislabeled product is spotted and
corrected. "You're taking a very large catalog and parsing it and
segregating it into searchable terms, to match what you want," Mr.
Miller says. When a customer enters a store with millions of
products, they are going to need as much help as they can get to
find the right aisle, he says.
Pinterest Inc., the 10-year-old social-media platform, has
trained its object-recognition model on billions of images saved by
users from around the Web, says Jeremy King, the site's senior vice
president of engineering. Many of the objects in these photos,
known as Pins, are available for purchase.
"The more we know about a Pin and the objects and products
inside it and their attributes, we can return more relevant results
based on searches, " Mr. King says, adding that the site has
enlisted fashion industry pros who make sure clothing styles are
being correctly identified.
Mr. King says precise visual-search capabilities enable users to
ask "what material is this made from" or "what is the color scheme
of this dress," and get answers that also direct them to similar
items for sale.
Pinterest's visual-search capabilities can also identify
incidental objects within a photo, like a vase in the background of
a restaurant, and make the items searchable for users who want to
buy them, Mr. King says.
Natural-language processing
The other big AI tool that is revolutionizing e-commerce,
advanced natural-language processing, helps retailers hone search
results by interpreting buyers' intentions from the terms and
phrases they use.
"There's a paradox of choice that really matters," Mike Fisher,
Etsy's chief technology officer, says about the online
marketplace's 80 million-plus items for sale. "You don't want it to
be overwhelming," he says.
Five years ago, Mr. Fisher says, search was purely based on
keyword matching. This means it was easy to miss relevant
merchandise when customers typed in a search query.
Today, Etsy's search engines "learn" from a customer's past
queries as well as from other customers' past queries and
purchases. Billions of historical data points are consulted to make
links between the terms shoppers have used and the products they
eventually find.
This relieves shoppers of the "burden" of having to formulate
"the 'perfect' search query," Mr. Fisher says.
The tool also enables Etsy to map out synonyms within a
hierarchy of colors. So if a customer searches for a teal rug, say,
the algorithm can find products matching the query, but also
options in other hues of blue, which would otherwise be buried
deeper in the catalog.
Mr. Fisher says the next step will be to train algorithms to
combine product and search data over time to determine a shopper's
overall taste. That requires associating an array of elements that
aren't linked in an obvious way, like flannel pajamas and
midcentury décor. "The tricky part is going across chairs and
shirts or jewelry to detect a similar style," he says.
Taken together, natural-language processing, computer vision and
vast stores of customer data enable online retailers to better
interpret a search query, identify a more precise set of relevant
products, and pull out a smaller assortment of personalized choices
unique to each shopper -- all just a few clicks.
Many traditional stores with a heavy online presence are also
looking to AI to ratchet up search.
Walmart Inc. takes advantage of data from its physical stores
and its website to build out a robust profile of customers, says
Ravi Jariwala, a company spokesman.
"Because we have a massive data set between our online and
offline purchases, the algorithms are incredibly well informed," he
says. According to Mr. Jariwala, most Walmart customers continue to
shop in the physical stores, but a growing number are paying online
for delivery, or ordering online for in-store pickup. "This is how
people are shopping now and we don't see that changing any time
soon," Mr. Jariwala says.
"So much of our shopping has drifted online and there's a lack
of tolerance for companies that aren't getting it right," says Jon
Duke, a vice president for retail insights at tech research firm
International Data Corp.
"These tools are proving their worth during the crisis," he
says.
Mr. Loten is a reporter for The Wall Street Journal in New York.
He can be reached at angus.loten@wsj.com.
(END) Dow Jones Newswires
November 02, 2020 10:33 ET (15:33 GMT)
Copyright (c) 2020 Dow Jones & Company, Inc.
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