by Hilary Milnes
Everyone talks about, and agonizes over, Amazon’s effect on the retail industry. And for good reason. But there’s a smaller, yet still mighty, transformation rippling through the industry that brands are bending to accommodate, and that’s the Stitch Fix effect.
CEO Katrina Lake is spearheading the future of personalization in fashion, a point of obsession for brands and retailers that know they can no longer offer up a generic user experience and still win. And while every apparel brand desperately wants to get to know the person buying and wearing its clothes on a more intimate level, Stitch Fix was modeled on the premise. It established a new type of retailer that asked customers for insights and feedback alongside their size and color preference for items, even the ones customers didn’t like or buy, in exchange for a clear value proposition. Stitch Fix, for its members, would eliminate the need to go out and shop for clothes.
The machinations of the Stitch Fix model rely on a combination of data science — machine learning, AI and natural language processing — and human stylists; on top of complex customer profiles built by data, stylists can layer the nuances of buying and wearing clothes. Striking that balance has led the company to its IPO. and to nearly $1 billion in revenue in 2017.
In response, other brands are scrambling to figure out how they can create a similar customer feedback loop using data science and promote a more personalized experience. We asked Lake to think about what it takes to be a data-powered retailer, what’s holding the industry back and where priorities should land.
Prediction: Human roles won’t vanish, even as AI dominates.
Stitch Fix’s Hybrid Design line of in-house apparel is created using a series of algorithms that identify white spaces in Stitch Fix’s inventory, then take successful trends, colors and patterns across clothing pieces and combine them into a series of new styles. For any designer working in retail, this could sound like a death knell.
But Lake said Stitch Fix’s ability to use people’s strengths alongside data science is the company’s secret sauce.
“The relationship we have with data science is not so much about client data as it is about the clients themselves,” said Lake. “So to be able to get to know people one-to-one and personalize Fixes to their needs, it requires that we really understand clients well and that we have a lot of information on them, which informs the inherent relationship around how Stitch Fix works. Because we have stylists and not just an algorithm, we get much higher quality data, and more involved and authentic data points.”
For instance, someone might buy a pair of jeans from their Stitch Fix delivery, and then tell their stylist that they only bought the jeans because they’re in the process of losing baby weight and want the jeans to fit. Right now, though, they don’t actually fit — so the stylist can then inform the algorithm not to take those jeans’ data and apply it to the customer’s profile.
“Our data is real, and effective. And people share this information because they know we’ll actually use it to deliver a good experience,” said Lake.
Prediction: Customer expectations are going to skyrocket.
Right now, we’re all used to seeing generic, “If you liked this, you might like this” photo dumps at the bottom of an e-commerce page, or getting an email in which a retailer boldly claims, “You’re going to love our new arrivals!” Arrivals that, of course, you very much do not love.
According to Lake, patience for such superficial excuses for personalization is wearing thin.
“Today, you laugh off a bad recommendation, but the bar is going to get much higher when it comes to what is expected from companies that are serving you recommendations based on your data, and what they actually understand about you,” said Lake. “Companies get away with inauthentic personalization and data that doesn’t make a lot of sense. In the future, all retailers should be able to anticipate better than they can today, whether that’s based on what you’ve liked on Instagram or your past purchases.”
Prediction: Experiences will actually evolve based on customer data.
As customer expectations increase, experience will actually catch up. There’s so much customer information that can be culled online that people willingly give up (whether it’s knowingly or not), that current retargeting efforts and recommendation bots are just the tip of what’s possible.
Eventually, that gap between customer information and experience will start to close.
“Historically, there’s been a gap between what you give to companies and how much the experience is improved. Big data is tracking you all over the web, and the most benefit you get from that right now is: If you clicked on a pair of shoes, you’ll see that pair of shoes again a week from now,” said Lake. “We’ll see that gap begin to close. Expectations are very different around personalization, but importantly, an authentic version of it. Not, ‘You abandoned your cart and we’re recognizing that.’ It will be genuinely recognizing who you are as a unique human. The only way to do this scalably is through embracing data science and what you can do through innovation.”
Prediction: ‘Client-centricity’ will become a pressing priority.
There are a lot of reasons why retail at large hasn’t caught up to the developments around what data science can do to serve a customer. Stores are closing, foot traffic is falling and when times aren’t good, experimenting with new technology takes a backseat.
It doesn’t help that these companies have legacy systems in place that would require a massive overhaul to change. But, according to Lake, time is running out for retailers to invest time and energy elsewhere.
“It’s a real challenge to invest in something new when you’re facing other challenges. People also don’t see how it will add value to their businesses. They can learn from what we’re seeing value in to improve the ecosystem overall, but it’s not super easy or straightforward,” said Lake. “But think about this ‘client-centricity’ of the industry. There are so many places where people can buy things today, that you have to think about everything from the lens of: ‘How is my customer at the center of everything?’ To be able to know, ‘This is the value we’re delivering to the client, and introducing this feature will be better for him or her,’ and to live and breathe that, is incredibly important.