For many of us, the toughest decision of the day doesn’t happen at work, school, or at the nearest Starbucks (although that one often comes close). It happens in front of an overstuffed closet in the form of the age-old question “What should I wear today?” Each selected outfit represents an equation based on a multitude of fluctuating variables (What’s on my work calendar? What’s on my personal calendar? What impression do I need to make? What is clean? What fits? Dress up or down? What do I feel good in? What do I feel like today?).
While users are enjoying the convenience of such choice-reducing platforms as Lyft, Blue Apron, and Hipmunk in other areas of their lives, the weight of fashion-related decisions still rests on their shoulders. Add to that the unparalleled external pressure from the fashion industry, which swears you will feel better if you buy another shirt, and we have an environment with just three alternatives: unbounded consumption, ceaseless frustration, or withdrawal into a uniform.
Meanwhile, a change is sweeping across the fashion industry itself as household brands that are drowning in debt close their doors and ecommerce fails to keep up with the fickle economy. Numbers don’t lie: For the last two years, British clothing retailer ASOS, for example, has doubled its revenue while its profits have stagnated.
Amazon is often referenced as the catalyst for these industry-wide changes (and cited as a threat to smaller businesses). In fact, the ecommerce giant became fashion market leader in the U.S. during the same two year-period that has been so hard on brands like ASOS. Unlike many struggling retailers, Amazon seems to really get data, which gives the online giant a major advantage — it knows what to sell and to whom.
But what if the rest of us were to use data with our customers’ interests in mind? What if we helped them figure out what purchases will bring them real value rather than the fading buzz of an impulse buy?
AI can help determine when a customer is ready to buy and can analyze past purchases, what people of a similar profile bought, and hundreds of other parameters involved in making a sale. And it can leverage that dataset to empower mindful consumption.
For the individual, it will mean simpler and more satisfying fashion choices. And it will help the fashion industry reduce waste, improve work conditions globally, and scale sustainability efforts.
My company analyzes a user’s wardrobe to unlock its full potential, and only after a thorough review does it recommend adding a few essentials that will provide a maximum number of looks. Instead of selling someone 10 shirts for each pair of pants, we encourage them to buy less.
The same approach converts to other areas. For example, imagine taking a picture of your fridge, and instead of automatically replacing everything you’re out of, you get recipes that work with what you have left. If AI can make consumption more mindful and sustainable by putting the customer front and center, could it give smaller companies a way to compete with giants like Amazon?
Both millennials and younger age groups value authenticity and sustainability and are searching for personal connection with the products they use.
In the new world they are building, which business model is more likely to succeed?
Anastasia Sartan is a seasoned fashion-tech entrepreneur, the founder of AI-based stylist bot Epytom and a finalist of “Entrepreneur of a year 2015” by Ernst&Young.
VentureBeatVentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:
- up-to-date information on the subjects of interest to you
- our newsletters
- gated thought-leader content and discounted access to our prized events, such as Transform
- networking features, and more