Thread, the fashion startup that helps lazy guys buy clothes, has raised $22 million

Thread Team (Ben Phillips, Kieran O'Neill, Shaunie Brett)

Menswear startup Thread has raised $22 million (£16.7 million) to help men navigate clothes shopping.
Thread uses a mix of machine learning and real-life stylists to learn about its customers' fashion tastes, and recommend stylish clothing accordingly.
The company has well-known brands such as Hugo Boss and Barbour on board, and more than 1 million customers.
The company will use the money to cement itself as a household name and build up its technical capabilities.

Thread, the startup that helps men who hate shopping buy clothes, has raised $22 million (£16.7 million) in venture capital backing to expand its technical capabilities and to cement itself as a household name.

Thread has more than a million customers signed up to its service, which takes a few registration details about a user's fashion taste and budget, then uses that information to recommend new outfits.

As customers buy more clothing through the app, Thread's algorithm learns more about what they like and makes new recommendations accordingly. It also learns from clothes customers click on but don't buy, what they scroll past, and what the weather's like.

The idea is that men who hate rifling through racks of clothes in physical stores can still look smart and on-trend by buying through Thread. Well-known brands such as Barbour and Hugo Boss are available through the service.

The startup employs 10 full-time stylists who curate outfits and write personalised messages to customers. But much of the recommendation load is handled by Thread's homegrown machine learning algorithm, much like Spotify's algorithm recommends music.

A quarter of Thread's users buy all their clothes through the service

Chief executive and serial entrepreneur Kieran O'Niell said a quarter of Thread's users buy all their clothing through the service. It's currently only available to male shoppers in the UK, but O'Niell said the plan was to introduce womenswear to the app some time in the future.

It has, he said, taken almost four years to get the machine learning aspect right. "If you use off-the-shelf approaches, you get boring results," he said. "Like if you buy a black T-shirt, you would be recommended a grey T-shirt. So we had to create something that would understand what you like... and factor in individual preferences, plus what the weather is like, and stuff you already own."

One thing the service could do better, he said, was adapt to customers' changing tastes. "What we're not good at is someone who did want [certain] stuff, has changed, and now wants more adventurous stuff. There's more we can do on that side."

O'Neill added that most of Thread's customers were aged between 25 and 45. "I was surprised our customer base is as old as it is," he said. "I would have thought a younger guy would be more fashion-conscious, it turns out lots of guys between 25 and 45 want to dress well [and] don't like shopping."

Thread's latest round was led by new investors Balderton, Forward Partners, Beringea and H&M's investment arm. The round included a small amount of debt financing from Triplepoint Capital. High-profile previous investors include DeepMind cofounders Demis Hassabis and Mustafa Suleyman, and prestigious Silicon Valley bootcamp Y Combinator.

O'Neill also plans to follow a startup trend set by the likes of Monzo and BrewDog: letting customers buy in. The company will open up to crowd investors via Crowdcube in November. The plan is to raise £500,000 to £1 million, though O'Neill said Thread may cap the round.

"From the beginning we we always wanted to hold back part of it so customers can invest," he said. "The main reason we have succeeded is because of loyal customers, and it's just a way of offering them the chance to invest in the business. We're not doing it to raise lots of extra cash."

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