Stitch Fix is leading the way as the future of personalisation in the fashion industry. The company is focussed on asking its customers for their insights and feedback for individual items – information that is then combined with their size and colour preferences, in order to create a service that streamlines the online shopping experience in the fashion sector. For its members, Stitch Fix can eliminate the need to go out to shop for clothes – and has created nearly $1 billion in revenue in 2017 alone.
So they’re doing pretty well.
The technology behind Stitch Fix is a combination of machine learning, AI and natural language processing, as well as human stylists, which use the highly detailed customer profiles to select items that suit the nuances of the individual customer. Stitch Fix is a rare case study – a company that has created a customer feedback loop using data science, that results in a highly personalised experience.
In a recent interview, the CEO of Stitch Fix, Katrina Lake, was asked about the current retail landscape, and how other companies could become data-powered… Here are the main takeaways:
AI Won’t Take Over
AI will not take over human roles. Lake describes how Stitch Fix’s technology is a way to analyse large amounts of data in order to find gaps in their product line, and to find historical data about the most popular colours, shapes and details. Designers and stylists are still a vital part of the company’s structure, as Lake explains,
“The relationship we have with data science is not so much about client data as it is about the clients themselves … So to be able to get to know people one-to-one and personalise 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.”
Customer expectations are set to get higher and higher as more data is available on their preferences and online history.S so offering bad, wrong or silly recommendations, or inauthentic ‘personalised’ messages will cause a negative reaction. Lake notes that,
“Companies get away with inauthentic personalisation 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.”
Better Customer Experiences
Lake sees a future where customer data is used to genuinely improve the customer experience.
“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.
“We’ll see that gap begin to close. Expectations are very different around personalisation, but importantly, an authentic version of it. Not, ‘You abandoned your cart and we’re recognising that.’
“It will be genuinely recognising 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.”
Traditional retailers are facing problems in the current climate, and therefore it makes sense that they are not focussing on developments in technology and the importance of data science. However, Lake thinks that this is the wrong thinking,
“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 … 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 centre 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.”