How Zalando Uses AI to Inspire, Engage, and Reduce Returns

Zalando, Europe’s leading online platform for fashion and lifestyle, has long been at the forefront of digital innovation in retail. Serving over 50 million active customers across more than 25 European markets, Zalando constantly seeks new ways to personalize the shopping experience, reduce friction, and inspire shoppers.

With a catalog spanning thousands of brands and millions of products, the platform faces the dual challenge of helping customers discover relevant items while addressing high return rates—often reported in the broader fashion e-commerce industry at around 25–30%.

To tackle these issues, Zalando has steadily invested in Artificial Intelligence (AI) to make product discovery and inspiration more intuitive. One of its latest milestones, the AI-Powered Fashion Assistant, leverages advanced language models to guide customers to the right products more easily.

Recent announcements detail the expansion of this tool across all of Zalando’s markets and the addition of four new cities to Zalando’s localized “Trend” coverage. This case study reviews Zalando’s journey, highlights the challenges it aimed to solve, and shows how AI now shapes the shopping experience in meaningful ways.

‍

The Challenge

• Personalizing at Scale: Shoppers can feel overwhelmed by the extensive catalog. They increasingly seek editorial guidance and “shop-the-look” features rather than searching products category by category.
• High Return Rates: Fashion e-commerce return rates can exceed 25–30%, primarily due to sizing or style mismatches.
• Localized Needs: Zalando serves diverse European markets, each with its own language, cultural style preferences, and shopping behaviors.

The AI-Powered Solution

‍Zalando’s response to these challenges involved developing a generative AI–driven fashion assistant that integrates seamlessly into its platform. The assistant allows customers to type free-form queries—such as “I need an outfit for a summer wedding in Paris”—and receive curated suggestions almost instantly. This level of natural language understanding is built on large language model technology, which continues to learn from user interactions, brand data, and trend patterns.

The solution provides dynamic styling recommendations, moving beyond single product suggestions toward entire outfits or complementary accessories. It also tracks city-based trends, a feature that was recently expanded to four additional urban hubs. Capturing local preferences enables more relevant advice, whether the customer is looking for minimalist aesthetics popular in Copenhagen or a bolder streetwear influence in Berlin.

Over time, the system becomes increasingly adept at personalization by processing data on brand affinities, previously returned items, and purchase histories to predict better fit, style, and size. Given Europe’s linguistic diversity, the AI is trained to handle multiple languages and dialects, offering localized recommendations and bridging cultural nuances that can otherwise complicate cross-border shopping.

Implementation and Roll-Out

• Pilot Launch: Zalando tested the assistant in select markets, tracking metrics like conversion rates and session times to gauge user satisfaction.
• Cross-Functional Collaboration: Data scientists, stylists, engineers, and marketers worked together to integrate the AI seamlessly into Zalando’s platform.
• City-Specific Trend Expansion: Coverage extended to four additional cities, adding more localized inspiration.
• Ongoing Model Updates: The AI is regularly retrained to keep pace with new products, fresh trends, and seasonal changes.

‍

Results and Impact

• Increased Engagement: Customers spend more time exploring curated collections, indicating deeper interaction with the platform.
• Reduced Returns: Pilot studies suggest up to a 7% reduction in return rates when customers use the assistant.
• Higher Conversion: Some Zalando reports attribute around 20% of incremental revenue growth in a recent quarter to improved personalization.
• Stronger Brand Perception: By moving beyond a purely transactional website, Zalando is increasingly seen as a style authority.

Challenges and Learnings‍

Data privacy and compliance considerations remain paramount in Europe. Zalando must observe strict GDPR guidelines, ensuring that customers have control over their personal data, can opt out of sharing, and understand how the AI utilizes their information. Balancing automation with human expertise continues to be an active area for refinement. Although machine learning excels at pattern recognition, trend editors and stylists still play a vital role in training the system and injecting creativity into curated looks.

Localization also remains a nuanced challenge. Handling multiple languages is technically feasible, but cultural variations in how people shop, what they value, and how they interpret trends are more subtle. The AI must continuously learn from usage data to avoid mismatched recommendations. Furthermore, establishing trust is crucial. Customers who understand how AI suggestions are generated tend to be more open to trying new products and styles, underscoring the importance of transparency in explaining why certain recommendations appear.

Future Outlook

• Visual Search: Integrating image recognition could allow shoppers to upload references and find similar items in Zalando’s catalog.
• Voice-Enabled Interaction: Conversational AI may enable users to verbally state their style needs, further simplifying discovery.
• Sustainability Insights: Highlighting eco-friendly or ethically produced items could appeal to an increasingly conscious consumer base.
• Omnichannel Experiences: Potential offline expansion might seamlessly merge in-store events with online style histories for a holistic shopping experience.

Zalando’s AI-Powered Fashion Assistant illustrates how a major online retailer can utilize advanced technologies to navigate the complexities of modern e-commerce. By personalizing the shopping journey, delivering localized trends, and guiding shoppers with inspiration-first interactions, the platform has improved conversion rates, cut returns, and strengthened brand perception. These achievements underscore the potential of AI to deliver tangible business results when paired with a clear strategic vision, robust data practices, and thoughtful user experience design.

Zalando’s approach holds valuable lessons for direct-to-consumer (DTC) founders seeking to integrate AI in their own digital platforms. Even smaller brands can start with basic data-driven recommendation engines, gradually layering in more sophisticated features such as natural language chat, city-level insights, or image-based product matching. The key lies in leveraging user data responsibly, refining algorithmic models with real-time feedback, and maintaining a human touch in creativity and curation. In doing so, brands can create a shopping journey that feels both personal and frictionless, ultimately setting new standards for customer satisfaction and loyalty in the era of AI-driven e-commerce.