As Zalando continues taking steps towards becoming a fully-fledged platform, we want to move fast, validate the ways that our big strategic moves pay off, and capture the full value of our products by continuous optimization. To this end, we wanted to ensure that we’re bringing data-informed decision making to the forefront of our processes by establishing a true data and experimentation culture that could ultimately become a competitive advantage in today’s fast-changing world.

Zalando has always been a data-driven company and analytics has been one of our key success factors. We believe that much of the success (or failure) of a product rides on data, and on how it is used. This brought about the following question: How can we elevate Zalando to the next level of data-informed decision making? This is how the Product Analytics department came to life.

Purpose

The purpose of the Product Analytics team is to embed a true data and experimentation culture at Zalando to empower smart decision making.

What do we mean by true data and experimentation culture?

• Our Business Units are aligned around key metrics that are rooted in our most important business priorities. Success is defined by a set of well-proven metrics which individual teams own and contribute to.
• Every team can access the data they need from various data sources and with high data quality. Setting up tracking is easy as well as assessing the data quality. Understanding user behavior based on A/B tests is quick and teams are always running multiple experiments at the same time.
• Every team can draw the right insights from their data. Teams have the ability and skills to learn from and make decisions informed by data. Advanced analytics helps them discover problems and opportunities, plus focus on the right developments.
• Decision making is not influenced by compromises, personal biases or egos, but only insights.

How can we get there?

To make data-informed decision making an easy and effective routine, and establish a data and experimentation culture, we focus on 1.) building a self-service infrastructure for experimentation, tracking, analytics 2.) ensuring common data governance, and 3.) enabling and educating all teams throughout Zalando.

• Self-service infrastructure for tracking, experimentation, and analytics: Data analysis and experimentation should be fast and easy. Only true self-service tools are truly scalable given the size of our organization today.
• Common data governance: With nearly 200 teams producing and consuming data events, there’s a growing need to ensure event tracking completeness and correctness and to allow for the easy compatibility of data.
• Enablement and education: As we want to move fast, all teams must be enabled and empowered in data informed product development; e.g. from building a rationale around new features up to iterative testing and optimization at the end of the product lifecycle. We expect a certain data and experimentation affinity from everybody and want to embed a data-driven culture everywhere. In order to get there, we want to guide teams and help them be more rigorous by embedding an expert analyst role into teams.

Department structure and competencies

The Product Analytics department was created as a hybrid organization of central teams and team-embedded product analysts. The central teams provide world-class tools and knowledge in the domains of Economics, Tracking, Experimentation, Journey Modelling, and Digital & Process Analytics. Product Analysts would also be embedded into teams varying from our Fashion Store, Data, and Logistics areas to focus on insight-driven product development. They play an instrumental part in all steps of the product lifecycle (“discover - define - design - deliver”) and can support insights-based decision making by performing the following tasks:

• Understand user and customer behavior: Develop in-depth analytical understanding for what drives growth for the product and how it can be improved, thus inspiring product work.
• Measure and monitor product progress: Analysts help to define target KPIs for the team and ensure that Product Specialists and and Product Owners develop ownership of them. At the same time, they facilitate access to the key target KPIs and other relevant data. They establish methods to monitor short-term progress and long-term product health. When KPIs change, embedded analysts explore the underlying reasons and are able to provide context for these changes.
• Prove if product ideas work: In the context of value creation, especially for new features, embedded analysts play an essential role by gathering and formulating analytical evidence that supports all phases in the product lifecycle, from discovery to rollout. Data must justify why we do what we do.
• Drive product optimization: From a value capturing point of view, embedded analysts drive optimization iterations for existing features until they reach a local maximum.
• Ensure data quality: Product Analysts create awareness about data quality within the teams where they are embedded. They have the responsibility of defining the specifications of the data to be generated by their teams, monitoring its quality and making sure the team addresses any quality-related issues they are responsible for.
• Improve data literacy: Analysts drive the data mindset in their teams, educate and guide in terms of analytical methodology – they are enablers for any data leading to product decisions.

What the future holds

Ultimately, we want the magic of data-informed product development to happen in every team, guided by team-embedded Product Analysts and empowered by central teams with best in class self-service tools and methodologies. By adopting processes that ensure data-informed decision making is taking place, our teams can build better products and iterate faster than ever.

Opinions are great to start a discussion, but we win on insights from user behavior. We prove strong hypotheses with relentless and granular attention to data and KPIs driving our decisions. We believe in high frequency experimentation and iterations to create the best possible experience for customers and all other players in the ecosystem.

It’s our vision that every product decision – be it the discovery or rollout of a new product; be it on the customer-facing, brand, core platform or intermediary side – is backed by analytical insights and rigorous impact testing. Thereby, we’re building a solid foundation for the next big learning curve in analytics: Artificial Intelligence and Machine Learning. We’ll be revealing more about our plans and learnings in upcoming articles.

Interested in Product Analytics possibilities at Zalando? We’re hiring.