So I’m walking down the street in SF today and someone steps out of a building in front of me. I’m always interested in what’s happening around me so naturally I ask if they work there. This person answers yes and says it’s a bunch of startups, including Le Tote.
“It’s like Netflix for clothes” I am told as a shopping bag is opened to reveal a box.
“We send you clothes we recommend you wear and…” I interrupt to finish the sentence “if you keep them you buy them! Am I right?” They nod yes and smile widely as if they were about to explain something hard and I saved a mountain of effort.
A pregnant moment arrives as I wait for even more congratulations; although I have really just described the age-old mail-order model as it currently exists. The irony of me predicting the end of a sentence, and pointing out a lack of innovation, is lost. They just seem relieved of the chore of explaining something aspiring to be innovative.
Maybe I shouldn’t attempt to find humor in analytics. I ask seriously if they have anything I should try.
“It’s only for women right now” I am told with a disapproving look.
I genuinely wonder out loud about their predictive algorithm: “Why do you assume already I do not want to wear women’s clothes? What if I am transgender? Would you still predict my fashion?”
I am looked at skeptically and offered no answers other than a soft and slow repeat of “if you want to wear women’s clothes…”.
Still curious and since this person is still standing there (presumably at the mercy of a service, a late driver) I press for more. “Nevermind gender fashion definitions, how does your prediction reflect regional differences. For example when someone in Colorado…” they interrupt me to say “we check the weather”.
Weather? Definitely not the end of sentence I had in mind.
I forgo jokes about the weather being perpetually wrong and instead restart my question so I can bring back my ending: “what do you do when someone in Colorado thinks sky-blue is the hot new color, while someone in SF wants orange and green? Can your algos anticipate fashion trends from social or other indicators, given your fashion angle”; tempted to add “not a good indicator of weather”.
Their face grows bright, they lean back, look to the street with an open gaze, suck in an ocean of air and exclaim “WOW WHAT A GREAT IDEA, I WILL SUGGEST THIS IDEA AT OUR NEXT MEETING”. Then they abruptly turn and excitedly run across the street waving a hand.
Now standing alone I yell “so what’s your name” towards the back of a head that nears an Uber parked in the bike lane. “Heather” is the response. Of course it is. And so I continue on my way.
Your air of smug superiority is palpable. I can’t help feel that you disregarded this woman’s potential for intelligence because she’s a woman.
Then obviously I have failed. My disregard comes from a lack of evidence that this model changes anything. Someone selects clothing, puts a catalog together. Then customers open the catalog, order clothes, receive them via mail, pay for what they keep. How is that not a description of the Sears catalog?
More than that I found it very funny that an attempt to be “analytic” led them to weather data (probably because the example code is so easy to find and copy). It is the worst possible, and most painfully dull/obvious, correlation already used (poorly) in clothing. There is literally nothing new in choice of clothing for general weather by season (doesn’t seem like they’re shipping overnight for next day’s storm, and even there I have done that already with existing mail-order for sailboat races).
An uncomfortable reality for retailers is the old saying “there is no bad weather, only bad clothing”. And the correlative new saying goes four simple layers is sufficient for any weather if you choose the right ones so you don’t need to order clothing more than a few times in your lifetime. Old answers are still solid. The new questions (ripe for new answers — innovation) are how to disrupt supply-chain, delivery and fashion.
For example I would say real innovation looks something like this: customer takes a shower and is simultaneously scanned for ideal fit for however long they will be awake. Then a 3D printer with containers of raw material downloads latest patterns/styles and offers selection to customer during shower based on current body scan. Customer selects, steps out of shower and picks up set of fresh clothing that has just been printed. End of wearing period the customer throws clothing into printer to be deconstructed and re-used in another period. Maybe what is printed is not entirely a new set of clothes and rather becomes clothing as a four-layer platform to be modified with new modules.