Top Picks for You (TP4U) is the first feature on Zalando Lounge that is built solely on a recommendation engine. This feature has been developed utilizing design thinking, SwiftUI Prototyping Framework, and leveraging attitudinal & behavioral metrics throughout its journey.
Role: Principal Designer, Design Mentor, Product Design Strategist
Executive Summary:
From the early concept phase to the final rollout, I’ve been a key contributor in the iterative development of this feature. My role involved bridging gaps between cross-functional team members, such as Data Scientists, Product Analysts, Product Managers, and Engineers. By facilitating communication and collaboration, we were able to align everyone from team leaders to Directors and VPs, ensuring a successful deployment. The result was a feature that not only prioritizes user experience but also achieves commercial success.
This initiative demonstrated the potential of high-level interdepartmental collaborations. It underscores how departments, which are not traditionally aligned due to organizational structure, can build strong relationships and collectively develop features prioritizing user-centricity.
Situation: After Zalando Lounge integrated the personalization algorithms into their catalogs enabling the default personalized catalog sorting, the team started to look into opportunities utilizing the personalization model into creating a more personalized experience when users are visiting the Lounge app.
Task: As the Principal Product Designer and the co-driver of the Personal Relevance Squad, I was tasked with leading the Discovery, Definition, and the Solution Design phases.
Action: By bringing all the different teams’ perspectives on the same ground. I lead a top-level collaboration conducting discovery, co-writing the Press Release, driving the Solution Design process, utilizing the SwiftUI framework for usability studies and validation, then co-driving the solution design handover for delivery of the feature.
Results: TP4U has been a resounding success for the Lounge team, marking several noteworthy achievements. It is the first feature solely based on machine learning algorithms and personalization, demonstrating our commitment to innovation. The enhanced user experience has been validated through key UX metrics, such as reduced Time to Add to Cart, and utilization of the GetFeedback tool for receving users’ attitudunal feedback while increasing the Gross Merchandise Volume (GMV).
Please reach out to me for a deep dive on this project!
Kaan L Caglar 