Zalando Co-CEO on Bringing Data Science to Fashion Retail

This article first appeared in The State of Fashion: Technology, a detailed report co-edited by BoF and McKinsey & Company.

Zalando is Europe’s largest online-only fashion retailer, but it often describes itself differently: Europe’s hippest tech company. Technology has been fundamental to the operation of the company since it was founded in 2008 in Berlin. Today you use data to optimize everything from how you buy products from brand partners to how you deliver items to customers. It also leverages technologies, including AI, to give shoppers a more personalized experience on your site and app. The approach has worked: in its fiscal year 2021, total merchandise volume on its platform increased 34% year-on-year to €14.3bn ($15.7bn), generating revenue of €10.4bn.

Robert Gentz, co-founder and co-CEO, is helping guide Zalando to its next goal: by 2025, it expects annual merchandise sales to top €30 billion as it aims to capture more than 10 percent of the European market. of fashion It’s a lofty ambition and far from guaranteed as online competition grows. If Zalando wants to achieve this, it must continue to differentiate itself, and technology will be vital to the effort.

BoF: Personalization has been a main focus at Zalando for years and is a key part of the customer experience it offers. Why is it so important for the company?

Robert Gentz: At Zalando you have 1.4 million different items. It is a great selection. And then you have 48 million customers. Using technology and data to get the right customer to the merchandise, or the right merchandise to the customer, is important because, for these 1.4 million choices, how do you make sure you find an item? So we’re trying to use technology to personalize it for customers as much as we can. It all comes down to the problem of matching: how do you match merchandise with customers?

BoF: What technologies are you using for this task?

RG: it’s AI. There’s a program running, an algorithmic fashion partner, based on items he’s bought in the past. The algorithm combines tuning elements to [create] an outfit, which we have learned through how people combine [items]. When you look at click-through rates and purchase rates, the kits we’re producing are hitting the mark of what customers want. Therefore, it is the algorithms that continually improve with the feedback loops of customer data, as well as the human feedback we produce internally.

BoF: What are some of the ways a customer’s experience on your site or app is tailored to them?

RG: First of all, by joining, you already have the opportunity to express the brands you like, their sizes. That already personalizes the site for you. As for the product and merchandise in the teasers you see, it’s personalized, so the Zalando store looks different for each customer once they interact with us.

BoF: What metrics does Zalando look at to determine if these efforts are successful?

RG: Sometimes the short-term metrics aren’t always the ones that lead to the right answers in the long run. If you just want to optimize for click-through rates, then the articles that might be the most sophisticated have the highest click-through rates, but they’re probably not the ones that create the right offer, the right experience in the long run. What we are primarily optimizing is the long-term customer lifetime value, and the long-term customer lifetime value is generated through complex algorithms that [factor] how much time you spent on the site, how much you are browsing and what you are buying: these are different sets of [key performance indicators].

BoF: New product discovery is one type of value a retailer can offer to shoppers, but if shoppers receive personalized recommendations based on past behavior, does that limit their chances of discovering new items they may love but don’t? are they like what are they? have you bought in the past? Does Zalando take any action to account for this?

RG: Just looking at the past does not always answer the question about the future. What we really get a lot of inspiration from is how the music industry is trying to solve the problem. You can’t do it just with past machines and behaviors. You always have to mix new and modern fashion elements. This is where the fashion people help the tech people.

BoF: So there is still a traditional human curation in the process?

RG: Yes. In the end, everything is a matter of emotion. No one wants to just shop at a large automated warehouse. It is as much about art as it is about science.

BoF: Determining the correct size and fit for a product remains one of the biggest hurdles shoppers face when shopping online. Zalando has invested heavily to help solve this problem. It acquired a virtual dressing room company in 2020, has a full sizing and fitting department, and is setting up a tech hub in Zurich dedicated to the task. How is Zalando using technology to solve or at least reduce these problems and what solutions are you exploring?

RG: What we’re trying to achieve is that probably by 2030, you don’t really need the physical locker room. You have the same experience everywhere. What we’re doing at this stage is primarily based on the data we get from our customers to help them make better decisions. It’s largely based on returns (why you’re returning a certain item) and customer feedback.

We have many clients who ask for a very wide range of products and from all brands. One customer returns an item and another customer returns the exact same item for the same reason, but kept a similar item. You get a data graph, a fit graph, and based on that, we can make recommendations with existing customers that we have a deep relationship with as to whether the items fit or not. We have already been able to reduce size-related returns by 10 percent. The next iteration of this will be when we move further into full body measurements and experiment a lot more with 3D technology and body measurement technology.

BoF: Logistics is another complex area. How does Zalando use AI or other technologies to manage logistics?

RG: One of the biggest tech teams we have is working on convenience and logistics. An interesting problem is where do you assign an item with the [greatest] proximity to a customer across a network of warehouses, which is very important to drive sustainability and delivery times by avoiding single item shipments. Where you have size and brand and other items, it gets very granular. This is a very big algorithmic and data problem.

BoF: Are there features in Zalando’s organizational structure that allow it to better integrate technology and data? Even companies that want to make the best use of technology are not always ready for it. Departments can be siled, for example, so they don’t look to the same data to make decisions.

RG: One of the big things that we at least try to do is bring cross-functional teams together as much as we can. We have around 2,500 software engineers working at Zalando in various teams. When we have large-scale projects, we try to bring the different disciplines to the table and have them all look at this problem.

BoF: One of the big challenges businesses face is making sure all the data they rely on is clean, and then they need to be able to get valuable insights from it. How does Zalando address these challenges?

RG: I wouldn’t say we’re perfect at this, but we’re very focused on it. We establish ownership of specific amounts of data we produce in terms of who is responsible for it, and we have constant discussions about how we get better data. It is a culture of data cleansing.

BoF: AR and VR have gained more attention as everyone talks about the metaverse. Are there any emerging technologies or applications that Zalando believes will have a big impact in the future?

RG: Going back to real-life issues of sizing and fit, this augmented reality space could be a good catalyst for producing real breakthroughs in terms of solving the virtual try-on experience for customers and having definitive answers if an item fits or doesn’t fit. No. , before you physically have it in your hand. It’s something we’re pretty passionate about, that this part of the metaverse can help us solve big problems in the area of ​​size and fit and sustainability. When it comes to a purely virtual world and elements that only live virtually, we are still exploring.

BoF: Even though e-commerce has grown, stores are still where most sales take place. In 2018, Zalando launched its Connected Retail platform to offer inventory from physical stores. How is Connected Retail progressing and how is technology enabling that program?

RG: Throughout the pandemic, obviously, this escalated quite a bit, so there are now around 7,000 stores operating in Connected Retail. It’s a big part of the partner program. How technology can help [is that] we actually provide [partners] with an interface. It does not require any integration in a store. It requires matching the inventory a store has to a database so customers can place orders, and it requires some interface to the physical aspects of logistics. In the future, where it gets much more interesting is when we can combine this with our local delivery efforts. [to] enable customers who want to order inventory that is nearby.

BoF: Zalando says it wants to have a net positive impact, meaning run the company “in a way that gives back to society and the environment more than we take.” It’s a huge goal and something much of the fashion industry is thinking about. What role can technology play here?

RG: I think a lot of the challenges in fashion around sustainability, around sizing and fit, overproduction, resource allocation, customization, etc., are fundamentally a problem of data and collaboration. As fashion brands become more data-savvy in terms of their own supply chain (not necessarily more tech-savvy, but I think more data-savvy) and collaborative, we can all co-produce a fashion ecosystem that makes more sense. and consume fewer resources.

What we are trying ourselves is to work with brands very early in the design process to make sense of how data can help throughout the process. Fewer resources are consumed, at least for us in terms of shipping and returns. Create more profit pools for everyone, and this can be reinvested. But in general, what is very clear to me is that, in the end, it’s about data, it’s about collaboration, data sharing. Many of the problems that we are seeing in terms of overproduction, in terms of incorrect production or not designing for circularity, can be solved in the long term.

This interview has been edited and condensed.

Source: www.businessoffashion.com