Zara Virtual Fitting Tool
Zara AI-powered virtual fitting tool that enhances online fashion shopping by providing precise size recommendations and outfit visualizations.
Problem Statement
“How might we improve the online shopping experience by ensuring accurate clothing fit and enabling customers to visualize how garments will look on them before purchase?”
Problem Background
Online fashion shoppers frequently struggle with sizing inconsistencies, leading to frustration, high return rates, and lost revenue for retailers. Traditional size charts are unreliable, and customers lack a way to see how garments will fit their body shape before purchasing. With 80% of online shoppers returning items due to sizing issues, Zara identified the need for a more precise, AI-powered solution.
Research Insights
User Pain Points
- Inconsistent sizing across different items and brands creates confusion.
- Customers lack a way to visualize how clothing fits before purchasing.
- High return rates due to poor fit impact both shoppers and retailers.
- The trial-and-error process wastes time and reduces confidence in online shopping.
Supporting Data
- 30-40% return rates in the online apparel industry, with sizing issues being a major cause.
- 75%+ customer interest in an AI-powered fitting solution, based on internal research.
- Potential for 20-30% reduction in return rates and 35-45% increase in engagement with the tool.
Feedback
Our preliminary user research to validate this problem with online fashion shoppers found that over 70% of participants expressed frustration with inconsistent sizing across different brands. Additionally, many users mentioned that they often hesitate before completing a purchase due to uncertainty about fit, leading to abandoned carts and high return rates. Respondents also highlighted that a virtual try-on feature would significantly improve their confidence in purchasing clothing online.
Landing on the Solution
Based on our target users’ pain points, we knew we wanted to work on the following features:
- AI-Driven Size Recommendations: A machine-learning model that adapts to body measurements and purchase history.
- Virtual Try-On: Realistic 3D clothing visualization based on a user’s body shape.
- Fabric and Fit Insights: AI-generated details on fabric stretch, comfort, and fit accuracy.
- Personalized Shopping Experience: Outfit recommendations and size adjustments based on browsing behavior.
- GDPR-Compliant Privacy Measures: Secure handling of user data with opt-in controls.
Explanation of Solution
After we showcased our prototype to the users again, we learned that…”
- Users preferred photo-based AI recommendations over manual input for body measurements and less restrictions in using the AI feature.
- Customers wanted an option to refine AI suggestions manually, making size selection more customizable.
- Privacy-conscious shoppers appreciated Zara’s commitment to secure, non-invasive AI processing.
User Flows / Mockups
You can find below a link to the user flow and sketches in Lucid Spark: LucidSpark Page
Mockups
Future Steps
Firstly, enabling cross-platform functionality will allow the virtual fitting tool to be utilized across multiple e-commerce sites, creating a seamless and universal AI-driven fitting experience that enhances convenience for online shoppers.
Additionally, exploring AI-powered style recommendations will create a more personalized shopping experience by suggesting outfits based on individual preferences and past selections.
To maintain user trust, it is crucial to enhance GDPR-compliant data security measures, ensuring that personal data remains protected and used responsibly.
Learnings
Product Manager Learnings:
Chiamaka Obia
Co.Lab was a great experience for me, especially as someone new to product management. Working with a mentor provided invaluable guidance, helping me navigate key product decisions and refine my approach to problem-solving. I gained hands-on experience in defining product requirements, prioritizing features, and iterating based on user feedback. Additionally, I learned how to use various AI tools to enhance product development, deepening my understanding of how AI can be leveraged to solve real user problems. This experience has been instrumental in building my confidence and skills as a product manager.
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.