Forget "haute couture", now it's "t-shirts hot off the presses, and right to your door".
DTC (direct-to-consumer) mattresses, shoes, you name it - are big with investors right now. And the same goes for fashion, where new brands are emerging to further displace brick-and-mortar businesses by eliminating the frontage cost and focusing on better product and better supply chain management.
Buck Mason ($PRIVATE:BUCKMASON), Marine Layer ($PRIVATE:MARINELAYER) and Everlane ($PRIVATE:EVERLANE) are three startups catering to men and women with fashion delivered right to their doors - but increasingly, also available in stores. It's still early innings for the DTC fashion playoffs - but there are already compelling results when we look at the alternative data for each.
Right now, there's one clear winner in terms of staffing growth, and it's getting more and more obvious. Everlane has seen staffing levels increase at a greater rate than its startup competitors in the space. Our LinkedIn Employee Headcount reflects Everlane has grown staff 46% over the course of this year (adding more than 100 people). Marine Layer grew from 166 staffers at the beginning of the year, to 205, an increase of 23%. And Buck Mason boosted headcount 18% - but only adding 8 staffers, growing to 51, at mos recent count earlier this month.
Equally impressive is how little each of the startups has raised - they haven't cobbled $10 million in investor backing between the three of them, which is probably more than some of their legacy competitors spend in a month servicing debt costs (we won't name names).
Right now, the companies have combined to create a scant number of physical locations to buy clothes - but if and when any of Everlane, Buck Mason or Marine Layer gathers another round of venture funding, expect this map to fill in - fast. You can track store growth over time, and using the box in the map at top-right, can focus on individual companies and filter others out.
About the Data:
Thinknum tracks companies using information they post online - jobs, social and web traffic, product sales and app ratings - and creates data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales.