Why Hugo Boss is building a €15 million data campus

This article on Hugo Boss is part of our Vogue Business Membership package. To enjoy unlimited access to Member-only reporting and insights, our NFT Tracker and TikTok Trend Tracker, weekly Technology and Sustainability Edits and exclusive event invitations, sign up for Membership here. When Hugo Boss CEO Daniel Grieder joined in 2021, he set out to turn the sleepy menswear brand into a “tech-driven fashion platform” that was among the world’s top 100 brands. The brand split into Boss (for millennials) and Hugo (for Gen Z) and set a goal of reaching €4 billion in sales by 2025, which it raised this June to €5 billion. To achieve that, the company needed to overhaul how it collects and uses data. This summer, Hugo Boss marks the latest milestone in that plan, with the opening of a large hub in Gondomar, Portugal, that will employ at least 250 people, including data scientists, data engineers, data visualisers and business intelligence specialists. In addition to expertise in data, they also specialise in e-commerce and technology more broadly. The Hugo Boss Digital Campus was made in partnership with Metyis, a company that specialises in helping retailers identify, collect and analyse data. Through a €15 million investment from Hugo Boss and Metyis, the two companies have formed a joint venture; by 2026 Hugo Boss plans to own the Digital Campus outright. (Hugo Boss now owns 30 per cent.) Forming a joint venture (JV) with Metyis, which works with eight of the premium 15 apparel companies, including a number of luxury European brands, was a faster way of onboarding this know-how than recruiting and training people in-house, Grieder says. The timing for a big data push might feel a bit late, especially as many other luxury brands have publicly shifted to more futuristic pursuits such as Web3 or generative AI. However, while the importance of data science has been prevalent in fashion for years, how to actually understand and act on the data collected is still elusive. Almost one-third of business leaders globally are overwhelmed by the amount of data, and 41 per cent cite a lack of understanding of data because it is too complex or not accessible enough, according to a February report from Salesforce. This is partly why fashion retailers that specialise in these capabilities, such as Stitch Fix, Farfetch and The Yes, saw so much momentum in the past decade, and why tech companies such as Google, Instagram and Pinterest have been positioning themselves as next-gen retail destinations. “Data and AI are the next steps and are going to be such an important part of our next chapter in life,” Grieder says. “There’s no question of if it comes; it’s just a question of how you implement it into the business. We believe the faster the better, and that’s why we want to be a tech-driven fashion platform.” Already, about 150 people are working in Hugo Boss’s new hub. Fashion brands haven’t typically hired many data scientists or others with this expertise, but data analytics can be one of the more practical technologies for increasing revenue in the industry, says Metyis CEO Yogen Singh, who began working with Grieder, via Metyis, when he was at Tommy. “If you look at cool things happening, like the NFTs and the metaverse, that is all very good. But our focus is on the balance sheet and P&L.” Hugo Boss’s growth strategy, called “Claim 5”, includes investing more than €150 million in digitising the company’s entire value chain. In addition to data analytics, this includes digital product development and digital showrooms, which all stand to increase revenue while decreasing waste — which has become a signature mission for Grieder. The data-informed decisions play a key role in preventing waste because it makes the business more efficient, Grieder says, and increases the likelihood that the products that are made will actually sell. While he was at PVH, he led efforts to use digital showrooms and later converted the company’s entire design processes into digital design. This experience has helped inform Grieder’s strategy. Metyis and Hugo Boss will broadly tackle the creation of what they call “data architecture infrastructure”, meaning identifying, organising and interpreting insights. In Singh’s experience, often, only about 5 per cent of data collected is unique and relevant enough for decision-making, he says. And, even if a company can identify clear guidance, sometimes decision-makers resist making changes if they personally disagree with the facts. “A lot of times, people are spending too much time disputing the facts. You need one true source of facts, and don’t discuss the data but the insight,” Singh says. This differentiates Hugo Boss’s all-in approach. “Daniel is very unique in his thinking,” Singh says. “You need leadership for this. You need to believe in this, not just talk about this in your annual report.” The data plan Grieder sees opportunity throughout the business, both in informing the products that are made and then following to details including when and where they are sold (and for how much) and how they are marketed, with an immediate goal of improving digital sales. He also wants to make more personalised product recommendations to customers and other improvements to e-commerce services. This isn’t that easy, which explains why brands still have room to improve. Shopping journeys now involve more disparate channels, and brands need a number of systems to manage those channels, says Rob Garf, VP and GM of retail at Salesforce. Historically, the data that is collected might be fragmented among teams, he adds. That’s why even though most customers want the types of personalisation that data science can provide, more than one-third of business leaders say they feel unable to turn their data into insights, intelligence and personalisation, Salesforce has found. These are problems that Hugo Boss’s digital campus hopes to solve. It has already started helping decision-makers interpret existing data so that they can act on it. In the past six months, the company has created a dashboard to support decisions related to details such as products, shopping behaviours, marketing spend and prices. For example, in the design process, it might tell designers which colours are likely to be a hit. “If you give a designer just a book of the data, they don’t know what to do with it,” Grieder has found. “But, if you tell them that the best results are black, white, camo, red, yellow and green, they can design with the analytics.” Especially in luxury fashion, there has been resistance to the idea that computers might assist in creativity, or that, more specifically, data-driven design will upend the fantasy of the human genius. To set that right, Singh clarifies that neither analytics, AI nor any other newly developed science is here to replace the artistic nature of humans. Rather, Metyis’s scientists can help Hugo Boss’s designers amplify existing successful designs. “If you look at any fashion company, 70 per cent of what sells is close to what actually was selling [in previous seasons] — with a little bit of alteration,” Singh says. Additionally, the most popular items, or “NOS” (“never out of stock”) items, are often already a variation on a theme. Thus, analytics can help Hugo Boss extend the profitability and longevity of its most successful products. Alternatively, if a brilliant design isn’t selling, perhaps analytics might reveal that the product is losing its colour or the collar doesn’t work. Grieder says that the most important element is the team’s mindset — a lesson he learned when he oversaw the process of converting PVH’s design teams to 100 per cent digital tools. “Nothing can happen without the open-mindedness of the people. If they refuse to accept it, you will not be able to implement it.” Beyond design, Hugo Boss can use its data capabilities to inform how products are brought to market, through details such as the ideal price point and marketing strategies. Already, it has started analysing marketing spend. It can identify which countries to invest in marketing, and in which countries certain items aren’t relevant. It can also identify the ideal price point, and, more broadly, if the brand is less competitive in certain product price ranges. The company can also use data to inform how it hires brand ambassadors, Singh says. If it wanted to, for example, launch a new women’s dress for summer, it might be able to narrow down from 430 potential candidates which people are best positioned to sell that product. “It’s all very incremental,” Singh says. “It’s not like a big designer designing a show and suddenly, things start working. It’s little pieces that add up to millions, and you have to have the rigour to go after each cent.” Why now? Since the arrival of the first generation of successful fashion-tech companies, a lot has changed to further incentivise fashion brands in their efforts to build data chests. The quality of algorithms and processing speeds have “improved tremendously” in the past five years, Singh says. Additionally, the shift toward direct-to-consumer e-commerce means brands now have access to crucial first-party and zero-party data, which comes directly from a brand’s own customers, Garf says. And then there’s generative artificial intelligence. Data science and machine learning capabilities are precursors to more sophisticated AI, such as generative AI. As executives are preparing to use generative AI, they first need useful, organised data. “The models are only as good as the available data,” Garf says. “The seemingly overnight rocket ship of generative AI has forced brands to take a new look at all the data that is sitting across their enterprise. There is a rallying cry for brands to get their data house in order. We talk a lot about AI, but there’s something to be said about access to the data before layering intelligence on top.” In recent years, luxury brands have started investing in their own capabilities. They often do so quietly, as data-driven decisions can seem, to the uninitiated, at odds with the creative, curated ethos associated with high-end fashion. In 2018, Kering developed algorithms to identify the customers who are most likely to purchase in the next three months. In 2021, LVMH partnered with Google Cloud to use its AI and machine learning (ML) technologies to improve demand forecasting, inventory optimisation and personalised experiences. However, neither tends to shout about how its luxury brands are making decisions based on data. That is starting to change, especially for more moderately priced fashion brands; 73 per cent of companies overall are planning to continue or increase spending on data skills development and training for employees to solve this, Salesforce has found. From dashboard to generative AI While brands’ data analytics capabilities are improving, so too are customers’ data privacy concerns and the tools to enable that. That is why first-party data, meaning information that the brand has collected passively as they engage with the customer, and zero-party data, which is information that customers have proactively shared with the brand, are crucial. To solve that, Grieder wants to turn customers into fans, who are eager for personalised recommendations. “If you are a fan of a brand, you have no problem giving your data. Consumers are quite open-minded as long as they like and understand what it is used for,” he says. During a recent Boss pop-up store during the Boss Open tennis tournament in Stuttgart, for example, the brand gathered 2,000 new customers in nine days, who eagerly shared their information. In the long term, Grieder is excited about the potential to layer on generative AI, which can generate text or images that are more humanlike. An associate or designer might be able to type in a question to get a recommendation on making a decision, rather than use a dashboard. There’s a lot more to AI than ChatGPT, Grieder says. “With AI in combination with data in the future, you can set trends much faster and you can even do it by country, or by region. It’s also structuring it better than when you just have the numbers and figures. You ask the question in AI and it answers it with a normal, understandable sentence. It makes it easier and faster, and in the end, more sustainable and efficient. It’s a dream. And a lot of people are afraid of that, but we think that it just makes the business better.” Clarification: Article was updated to reflect Hugo Boss's updated sales target of €5 billion, up from €4 billion. 5 July, 2023 To receive the Vogue Business newsletter, sign up here. Comments, questions or feedback? Email us at feedback@voguebusiness.com. More on this topic: Lessons from Levi’s data science bootcamp Analytics are reshaping fashion's old-school instincts AI, big data and fashion: Sell more, waste less

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