Shayla: AI-Powered Shopping Assistant
Shayla is an AI shopping assistant designed for Shein shoppers, providing personalized outfit recommendations, including accessories and shoes, based on their style preferences and shopping habits.
Product Space
Problem Statement
How might we create an AI shopping assistant for Shein shoppers that delivers personalized outfit recommendations—including accessories and shoes—tailored to their unique style preferences and shopping behaviors?
Problem Background
Personalization in online shopping has rapidly evolved with AI advancements, transforming traditional retail experiences. However, as AI integration increases, new customer pain points emerge. According to Goldberg (2024), AI agents provide unprecedented precision and personalization in targeting consumers. Major retail players, including Amazon with its AI assistant, Rufus, have already implemented AI-driven shopping experiences.
Despite this progress, 83% of Shein shoppers struggle with styling complete outfits. Shein’s current ‘You May Also Like’ recommendation system is limited—it primarily suggests similar items rather than cohesive outfit ideas. This leaves shoppers overwhelmed with choices and forces them to manually piece together looks. Additionally, 75% want full outfit recommendations, 58% seek matching shoes and accessories, and 33% desire regional style suggestions.
This project aimed to explore Shein shoppers’ attitudes toward AI-powered personalized shopping assistants. By analyzing browsing and purchase habits, the AI assistant would provide complete outfit recommendations, highlight popular outfit combinations, and make shopping more efficient and enjoyable. Gathering insights from at least 10 user experiences was critical in validating the initial concept and ensuring unbiased research.
Research Insights & User Pain Points
The research focused on Shein shoppers' purchasing frequency, their comfort level with AI, styling challenges, and the impact of regional factors on shopping habits. The goal was to determine if an AI-powered assistant would enhance their shopping experience and what features would drive adoption. Understanding existing solutions by competitors was also important as it helped identify gaps in the market and differentiation opportunities for Shayla. By analyzing what Amazon, Stitch Fix, and Nordstrom offer, we can position Shayla as a superior, AI-powered shopping assistant tailored to Shein’s target audience. Amazon’s “Complete the Look” suggests outfits but lacks deep personalization, meaning recommendations are often generic rather than user-specific. Stitch Fix offers curated styling but requires a subscription-based model, which may deter casual shoppers. Nordstrom’s AI styling provides recommendations but lacks real-time adaptability to changing user preferences. Several studies reveal that AI-driven fashion recommendation systems can often frustrate users. For instance, users may experience decision fatigue from too many generic suggestions, particularly if AI fails to consider individual preferences, price sensitivity, or climate considerations (International Journal of Innovative Research, 2023). Research on AI-driven user interactions also shows that users often become frustrated with AI tools when they feel the recommendations are not aligned with their style or budget preferences (PromptLayer, 2023). This highlights a key opportunity for Shayla to provide more personalized, user-specific, adaptable recommendations in real time, reducing user frustration and increasing satisfaction.
The key findings showed that:
- 75% of respondents are interested in AI-powered outfit recommendations.
- 83% struggle with styling outfits, indicating strong demand for AI assistance.
- Most valued AI features:
- Full outfit recommendations (75%)
- Matching shoes and accessories (58%)
- Regional style preferences (33%)
- Comfort with AI tracking shopping behavior is moderate, with some users expressing privacy concerns.
- Users are motivated by:
- Ability to save style preferences (58%)
- Discounts for using AI (50%)
- Gamified rewards (50%)
- The biggest shopping challenges include:
- Finding items that match personal style (50%)
- Not knowing how to pair items together (33%)
- Climate and regional trends influence purchases, with 50% of respondents adjusting shopping habits based on weather and seasonal needs.
For full survey response - see here
See Research Synthesis here
Landing on the Solution
The research confirmed a clear need for an AI shopping assistant that provides complete outfit recommendations. 83% of participants identified styling outfits as a challenge, emphasizing the necessity for a tool like Shayla to simplify the process.
Explanation of the Solution
Developing Shayla was a straightforward decision based on survey insights, but implementing the solution posed challenges. With no technical background, I visualized the user experience and created a prototype outlining Shayla’s functionality.
Style Assistant– A dedicated space within Shein’s platform where users can:
- Take style quizzes
- Save favorite outfit combinations
- Receive AI-curated looks based on shopping behavior
User Flows & Mockups
User Flow - DTTP - User Flow
Mockup - Here
See Shayla Wireframe homepage - Here
See Shayla Wireframe for Cart prompt - Here
Future Steps
To refine Shayla, the next steps include conducting qualitative interviews and AI prototype testing, while scaling from women’s fashion to men’s, accessories, and regional styles; success will be measured by user adoption, conversion rates, engagement time, satisfaction scores, and repeat usage, with at least 70% of users reporting increased confidence in styling, reduced shopping frustration, and high personalization in recommendations through focus groups and feedback.
References
- Goldberg, J. (2024, November 25). AI shopping agents are here: They will reshape retail and advertising. Forbes. Retrieved from https://www.forbes.com/sites/jasongoldberg/2024/11/22/ai-shopping-agents-are-here-they-will-reshape-retail-and-advertising/
- International Journal of Innovative Research in Management & Finance. (2023). Customer satisfaction with AI-driven fashion recommendations. Retrieved from https://www.ijirmf.com/wp-content/uploads/IJIRMF202402005-min.pdf
- PromptLayer. (2023). Can AI sense your frustration? Retrieved from https://www.promptlayer.com/research-papers/can-ai-sense-your-frustration
Learnings
Product Manager Learnings:
Toritse Ikomi
This sprint has been both challenging and rewarding. The biggest takeaway for me has been discipline—committing weekly to learning new concepts and applying them to a real-world problem. Through this process, I have gained fundamental product management skills and learned how to approach AI product development from a non-technical background.
One key insight is that AI product management is less about technical expertise and more about process improvement. I learned to focus on leveraging AI to enhance existing experiences rather than getting overwhelmed by the technical aspects like deep learning or natural language processing.
As someone interested in entrepreneurship, I am also thinking about how to apply these learnings to building a physical product in the future. Creating a prototype of Shayla has been an exciting step in this journey.
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.