zalando case study pdf

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Zalando: Building one of Europe’s leading data-driven companies

About Zalando

Founded in 2008, Zalando is a leading European online fashion platform, connecting more than 24 million active customers with 2,000 leading brands.

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Leading online fashion retailer zalando combines data from multiple sources using bigquery as the bedrock of its data-driven business., google cloud results.

• Combines metrics from diverse sources for advanced reporting and insights
• Generates same-day results for A/B testing to speed development

Democratizes data access with Looker Studio dashboards

Zalando is one of most remarkable success stories in European online retail. Founded in 2008, the company today connects more than 24 million active customers with clothing, shoes, and accessories from 2,000 brands. Following a phase of international expansion, Zalando is now active in 17 countries, employing more than 15,000 people. In 2017, not yet a decade old, the company posted annual revenues of €4.5 billion.

In 2016, as the scope, scale, and complexity of Zalando’s multinational operations increased, the company looked to anchor its activities in comprehensive data analytics, as Jorge Ramos, Team Lead Digital Analytics at Zalando, explains: “There were still some areas in the company that had trouble accessing all the data that they required. There's a famous saying, ‘If it takes two weeks to get an answer, in the end you stop asking questions.’ We were concerned that could happen at Zalando. We had over-complex tooling that meant data didn't flow as fast as we needed, and some teams were starting to make decisions that were not really based on data, or at least, not on all of the data that they would like to have.”

“We believe that Zalando has been one of the most data-driven organizations in Europe in the last decade. In a company of our size, quantitative data is essential to make decisions, and BigQuery was the best and, in some senses, the only option to work with data at our scale.”

To supply the framework necessary for a truly data-driven organization, Zalando supplemented Google Analytics 360 Suite with a solution based on BigQuery . Leveraging exported Google Analytics 360 data to BigQuery, Jorge and his team then made it available throughout the company with dashboards on Looker Studio .

“We believe that Zalando has been one of the most data-driven organizations in Europe in the last decade,” says Jorge. “In a company of our size, quantitative data is essential to make decisions, and BigQuery was the best and, in some senses, the only option to work with data at our scale.”

Combining data from across the organization in BigQuery

Online retail operations rely on accurate, rapid analytics to optimize customer experiences and meet KPIs. With more than 200 million monthly visits on mobile browsing for upwards of 300,000 products, Zalando used Google Analytics 360 to stream details ranging from customer lifetime value, to best- and worst-selling products, and pricing estimation models. In a single month, Zalando’s global activity could generate as many as 30 billion Google Analytics hits. To process that kind of volume, Zalando looked for a way to handle exports at scale.

“We were spending a lot of time taking care of those exports,” says Jorge. “We had to make sure that they happened at the right time, with no data loss, and at speed. The tools we were using proved to be quite complicated for end users who were not as experienced as our main analysts. Those were the main reasons why we switched to Google Analytics 360 as our primary analytics tool. We wanted to bring this data to every corner of the organization, with a very powerful tool that initial users can actually use.”

For Jorge, BigQuery was an obvious next step. “BigQuery is the best solution available for working with raw data at the level of granularity and accuracy we need. The Google Analytics 360 interface is good, but was not able to answer all of the questions that people may have, and that’s where BigQuery kicks in, delivering much more advanced analysis that we then plug into Looker Studio for dashboards that make big datasets digestible for anyone at Zalando.”

Zalando uses BigQuery to combine Google Analytics 360 data with information from other data sources, too, such as social media APIs or measurements of page performance from Zalando’s bespoke infrastructure, which show website loading speed for every single page view with absolute granularity. By bringing those datasets together, Zalando teams can understand the impact of a slow or fast website on commercial KPIs, conversions, and other metrics. “That’s only possible because of BigQuery,” adds Jorge. “Not only because of how easy it is to stream data into it, but also because of how easy it is to combine it once it’s in there, and then make fast calculations with huge volumes of data. Crucially, we can do that without sampling, which could otherwise mean losing 50% of our data – something we cannot afford to do.”

“We generate tens of billions of Google Analytics hits per month at Zalando. That amount of data just isn’t manageable with a more traditional database system. It needs something very advanced, like BigQuery, and thanks to the seamless integration between BigQuery and Google Analytics 360, it was easy to set up.”

Because BigQuery is a managed service, it requires minimal maintenance, beyond its basic configuration, meaning Jorge and his team can spend time on adding value elsewhere. “Now, for example, we are working to import certain fields from our own databases into BigQuery to make it more efficient,” says Jorge. “We are looking to combine business events, such as parcel deliveries or order acknowledgements, with the user behavior that we track on our websites with Google Analytics 360.” Combining those two sources in BigQuery means Zalando can create highly complex reports at speed, delivering sophisticated insights that add value and make the company more competitive.

“We generate tens of billions of Google Analytics hits per month at Zalando,” says Jorge. “That amount of data just isn’t manageable with a more traditional database system. It needs something very advanced, like BigQuery, and thanks to the seamless integration between BigQuery and Google Analytics 360, it was easy to set up.”

Different Google products for different levels of expertise

Zalando employees use Google Workspace for communications, and for the first year using BigQuery, the main reporting from the analytics solution was to Sheets , running scripts with BigQuery API. “Something we really enjoyed in the early days of using BigQuery was the easy integration with Sheets,” says Jorge. “We could call the BigQuery API from Sheets and easily build dashboards in a very short amount of time.”

At the time, Looker Studio was in an earlier stage of development, and as it became more advanced, Zalando switched to using it for dashboards. Now Looker Studio dashboards are updated daily from BigQuery for reference across the company.

“Our central idea is to have different Google products for different user profiles, to democratize data and bring it to every corner of the organization” says Jorge. “For hardcore analysts used to working with big volumes of data, BigQuery is just the perfect solution. The Google Analytics 360 interface is great for marketing, middle management, and other functions, and then on top of that we have Looker Studio connected to BigQuery. Those Looker Studio dashboards are consumed by the whole company, especially C-Suite executives and senior management. They provide an up-to-date, consolidated, single point of truth for our core KPIs.”

“We are really happy with Google Cloud support. As soon as we raise concerns or needs, Google addresses them. Google acts less like a vendor and more like a partner. Instead of selling products, they're invested in finding solutions together, and that's something we really appreciate at Zalando.”

Exploring machine learning to leverage analytics

Using BigQuery for analytics means Zalando can generate insights much more rapidly than in the past. “One example of that is in our A/B testing,” says Jorge. “We do hundreds of A/B tests, and the analysis used to be really time consuming. With BigQuery we can do it in a couple of hours, and as soon as a test is over, we can have results the same day and decide how to take things further. That’s been a change in the way we work.”

Now Jorge and the Zalando team are exploring the use of Google AI and machine learning tools to leverage analytics data, looking to create predictive models for conversions, churn, and other key metrics.

“We are really happy with Google Cloud support,” says Jorge. “As soon as we raise concerns or needs, Google addresses them. Google acts less like a vendor and more like a partner. Instead of selling products, they're invested in finding solutions together, and that's something we really appreciate at Zalando.”

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Zalando: Becoming the Starting Point for Fashion

By: Antonio Moreno, Leela Nageswaran, Emilie Billaud, Federica Gabrieli

Born in 2008 as a small startup selling flip flops, by mid-2021 Zalando had turned into an online fashion company with an assortment of more than 4,500 international brands, 45 million active…

• Length: 34 page(s)
• Publication Date: Jun 21, 2022
• Discipline: Operations Management
• Product #: 622070-PDF-ENG

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Born in 2008 as a small startup selling flip flops, by mid-2021 Zalando had turned into an online fashion company with an assortment of more than 4,500 international brands, 45 million active customers, and a presence in 23 European markets. An essential component in the company's ambition to become Europe's "starting point for fashion" was the ongoing transition from an online retailer to a platform business. The management team grappled with numerous strategic decisions. How could Zalando accelerate its transformation? Which new markets and novel customer propositions should the company invest into? And how should it balance the needs of consumers and partners?

Learning Objectives

The case is designed to: i) Discuss the central role of logistics as a source of competitive advantage; ii) Illustrate the value of centralization of inventory in outbound logistics in achieving pooling benefits; iii) Present the challenges of reverse logistics in an industry with high return rates, and show how decentralized return processing centers near customers can save costs by consolidating shipments (in contrast to pooling); iv) Discuss the differences between a wholesale and a platform model, and the inventory implications of different distribution models; v) Discuss the value proposition to customers and partners and the tradeoffs between B2C and B2B offerings.

Jun 21, 2022

Discipline:

Operations Management

Geographies:

Industries:

Apparel accessories, Beauty and personal care, Distribution and logistics industry, Information technology industry, Retail trade, Shipping industry, Transportation and distribution

Harvard Business School

622070-PDF-ENG

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