E-Pulse – AI and Innovation in E-commerce
Montreal clothier tries AI suggestions to increase online sales
At first glance, Montreal-based Frank + Oak looks like just about any other successful clothier focused on the tastes of Millennials and wannabes. With a dozen stores in hip neighborhoods of Canadian cities and three more in the United States (Boston, Chicago and Washington, D.C.), they expressly focus on the experience of shopping as much as on their products.
Co-founder Ethan Song and Hicham Ratnani created the company in 2012 after experimenting with other retail concepts. In their early days they modeled and took their own pictures for Frank + Oak clothing. Today they’ve grown to 250 employees and more than 3 million customers. With a strong emphasis on personal service, Frank + Oak doesn’t just sell their products to anyone. Online customers need to sign up as “members” to buy, which is intended to create the sense of a unique club.
Their men’s and women’s line has a big emphasis on “lifestyle”, and tries to capture the work clothing tastes of young creative types.
Some of their larger stores feature barber shops and coffee bars. they’ve also been known to host events such as whiskey tastings and art shows. The effect is to broaden the definition of a retail experience in the same way that Disney Stores and Bass Pro Shops do for their market segments.
Personalization is a company mantra. Each store has stylists available to assist in clothing selections, and staffers include a handwritten thank-you notes before online orders are shipped (about 50 percent of the company’s sales are through its website).
It’s only a natural, then, that when Song was presented with an idea to increase personalization for its online customers, he said yes.
“From our research we know that customers often may not know what to buy, maybe they don’t shop for clothes that often,” Song says. “But they want to look good, and they appreciate good recommendations.”
Song’s interest dovetailed with the work of Eric Brassard, who formed Propulse Analytics in 2015. Brassard, a former marketing executive with Saks Fifth Avenue, saw the impact of personalized service when he started in retail.
“In the early 1990s I saw that some Saks salespeople were extremely efficient, earing nearly $200,000 per year in commissions”, Brassard says. “I spent some time watching how they worked, and noticed the had an ability to size up clients very quickly.”
As a customer looked at a few items in a department, the salesperson would gather some pieces from various racks and bring them over. Invariably, the customer would almost always choose something the salesperson brought to them.
“It was instinctual. Through experience the salesperson knew what to show the customer by what they appeared to be looking for”, Brassard says. “Over time that’s always stuck with me when it comes to online retail: How do you create that same experience?”
“Everyone talks about AI, but you get the sense in the industry that people are waiting to see how practical it is. In our case, we can say it’s working.”
– Ethan Song, Frank + Oak
Online product recommendations have been around for nearly as long as online retail, usually built on the same principles: taking data points of what people look at on a site and making product suggestions based on what others have bought. the data is based on what typical customers buy, combined with a product’s color, fabric, style and price point.
Brassard saw an opportunity to bring artificial intelligence into the mix.
“Big data looks at previous patterns in a large group of similar customers”, he says.
“Say you’ve bought from my site a handful of times in the past year. that’s still not enough data to tell me where the next dot will plot itself for you. So big data analytics typically averages in similar customer patterns to guess what you might like. You typically see this in the line, ‘Customers who bought this often purchased that…’ But maybe you don’t like what other people bought.”
Using the concepts of AI and machine learning, where algorithms are basically making rudimentary decisions based on data as it flows in, Brassard sought to create a system for Frank + Oak that started not with customer profiles, but with products themselves using image recognition software.
The retailer’s emphasis on personal service matched well with Propulse’s goals.
“For Frank + Oak, we dug into the pictures of all their products and looked at tens of thousands of aspects of each picture to try and determine what might be of interest to a shopper”, Brassard says.
“So it’s not based on customer’s history. usually a stylist in real life doesn’t know what the customer has bought before. We’re trying to mimic the stylist’s instincts for what a customer would like.”
The system is invisible and unobtrusive. Click on a woman’s trench coat and Frank + Oak will begin showing similar coats as well as complementary pants and tops. This differs from the company’s previous experiments with product recommendations.
“In the basic algorithms, if you looked at white shirts, you got more white shirts. With machine learning you can go deeper and get more texture recommendations”, says Brassard. “It picks up style and feel — it’s much more organic.”
The system interacts with supply chain software so it knows not o recommend out-of-stock items; when this happens it notifies the retailer that a possibly recommended product wasn’t shown because of an inventory problem. Once it learns the shopper’s size, it also won’t show unavailable sizes.
“Like anything else, it’s only as good as the information you give to make it work”, Sing says. “Everyone talks about AI, but you get the sense in the industry that people are waiting to see how practical it is. In our case, we can say it’s working.”
Like most cloud-based applications, implementation was fairly easy. After product pictures were uploaded and read to “teach” the system, a few lines of code were added into Frank + Oak’s online shopping program. The retailer began working with Propulse last fall and started to see a difference in customer shopping carts.
“The choices our customers make are often based on these recommendations”, Song says. “We’re seeing them select complementary items, because, I believe, there’s a more personal feel to the recommendation. It’s as if a stylist is online with you making these suggestions. Our conversion rates have risen, but more importantly this works well with our branding of creating a custom shopping experience.”
For the future, Brassard looks to AI technology improving to the point where older, big data analytics looks clunky. “Deep learning will continue to be refined to the point where it really will be like talking to a stylist. We’ll see more sophistication in the products chosen, and who knows, maybe AI will begin setting fashion trends for us.”
John Morell is a Los Angeles-based writer who has covered retail and business topics for a number of publications around the world.
The original article was posted on Stores.org (NRF’s MAGAZINE)
E-Pulse – AI and Innovation in E-commerce
Think Tank: With Personalization, Retailers Will Fix Big Data Misses
For retailers, big data represents the marketing renaissance that never arrived. Data science made remarkable strides over the past decade, with retailers eagerly investing millions in big data tools as a means to multiple ends, from improving omnichannel shopping experiences to integrating logistics merchandise, and payment tools. And indeed, some gains were achieved operations, supply chain and logistics. Yet, many department stores and malls are struggling. Chains like Sears Holdings Corp., Macy’s Inc. and J.C. Penney Co. Inc. are shuttering 100 or more stores alone. The Limited closed all 250 stores and cut thousands of jobs. Gap Inc. has also seen sales declines in 10 of the last 11 quarters. Abercrombie & Fitch closed 60 stores last year, and 200 in the last two years.
From a market competitiveness standpoint, big data was intended to forestall these closures, providing retailers with meaningful, actionable ways to understand customers’ personal styles and interests. Instead, it has given rise to a cheap form of “personalization” based primarily on redirected ads and past clicks, not a true understanding of the consumer.
The potential payoff from true “personalization” has yet to be realized. A recent survey by Time Trade Systems, a customer experience analytics company, found that consumers are willing to increase their spending by about 4.7 percent in exchange for better in-store experiences, including more personalized service. That extra spending adds up to $150 billion in unrealized revenue, the researchers found.
But improving the customer experience takes more than big data, it takes personalization technology and predictive taste recognition tools powered by next-generation technology.
“The potential payoff from true “personalization” has yet to be realized. […] That extra spending adds up to $150 billion in unrealized revenue”
Fortunately, retailers can now use artificial intelligence, and specifically deep learning tools that construct neural network maps, bridging the online and in-store activity of individual shoppers and generating unprecedented levels of personalized customer support. This is made possible through the use of a wider range of inputs — including images of products that shoppers browse inside stores and scan for more information — than legacy personalization tools.
And over time, as a deep learning platform accrues more data on individuals, its accuracy, in terms of prediction, increases.
Past Is Not Prologue
We’ve all walked into a department store and interacted with a salesperson who, by some unexplainable alchemy, seems to understand our tastes with uncanny speed and deftness. He or she suggests the perfect apparel pairings. It seems like the salesperson is a mind-reader.
But top department store sales associates are not clairvoyant, they’re well-trained, and they’ve honed their skills of perception over time. They are also a major, and scarce, asset for department stores.
Now, imagine you’re in that same department store with a salesperson suggesting apparel. The sales associate brings you an item you just tried on, something you already purchased, or worse, something that’s a complete mismatch with what you’re looking for. It’s likely you won’t return because it’s obvious that the salesperson doesn’t understand you or your style. Online, we’re experiencing a similar ineffective and frustrating approach.
That’s because the majority of e-tailers rely on a “If you bought this, you’ll also buy that” approach that is too focused on the past as a roadmap to future purchases. This is the conditional algorithmic approach mastered by Amazon and the 300 million sku’s and data points it collects, but it’s clear that this approach cannot account for nuances in taste. Our personal tastes evolve over time — they’re not just influenced by quantifiable data from marketing campaigns and ads.
A New Personalization
For retailers to succeed, leveraging big data toward a new kind of personalization — one that predicts your next purchase based on a broader range of inputs and nuances — is paramount. Shoppers are unpredictable, and the window of time during which retailers can turn interest in a given product into a transaction is also highly variable. But just as e-commerce has given consumers tremendous access to product, via comparison — and omnichannel-shopping, digitally-connected sales associates can better connect shoppers with products they’ll love.
Until now, it’s been possible to separate expectations between online and brick-and-mortar levels of personalization: traditionally, in-store would always be better. But shoppers want a seamless shopping experience: what they find online should match what they find in-store. Some retailers are already taking steps to bridge the gap.
Neiman Marcus is adding a human touch to its mountains of consumer data. The luxury retailer is looking to its sales associates, those closest to shoppers, to gather human data: observations about how customers shop, what they say and their preferences. Those insights will be translated to data, delivering a more human marketing experience for its shoppers. Others are also doing similar things.
Hudson’s Bay Company, for example, with its $250 million acquisition of Gilt Groupe, is focusing efforts on data-driven e-commerce and personalization. Another example is Nordstom with its acquisition of Trunk Club, an e-commerce site that is allowing users to sample looks at home. The North Face is also using artificial intelligence to drive e-commerce sales, using IBM’s Watson to engage with customers and serve personalized recommendations based on user preferences.
New tools for personalization are also emerging. Neural networks — like a computer system molded after the human brain — coupled with image-recognition technology have a supreme effect for retailers looking to capture the most actionable data possible because they analyze how people shop. These tools strive to understand taste by detecting thousands of elements of an image, using AI and machine-learning to adapt to customer preferences over time.
To succeed in e-commerce, retailers need to create more personalized experience. By offering a predictive nature to data analysis, retailers are provided with actionable insights that give value today — not in the coming months. This is how retailers, facing declines in brick-and-mortar stores, create community around brands, building loyalty by personalizing the shopping experience.
What’s your take on this? How do you see retailers delivering a personalized experience?
The original article was posted on WWD
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