AI-Powered Shade Matching for Amazon Beauty
AI-driven shade matching for beauty products to reduce mismatched purchases and returns.
Product Experience
I led the research and product strategy for an AI-powered shade-matching tool aimed at reducing mismatched beauty purchases on Amazon. Through user surveys and data analysis, I identified key pain points, including an 80% return rate for incorrect shades. I designed a hybrid AI solution incorporating virtual try-on, shade comparison, expert consultations, and AI transparency features to enhance user confidence. This experience strengthened my skills in user research, product storytelling, and strategic decision-making, reinforcing my journey toward becoming a professional Product Manager.
Problem Space
Problem Statement
“How might we help online beauty shoppers find the right foundation shade with confidence, reducing mismatched purchases and returns?”
Problem Background
Since the rise of online shopping, beauty shoppers have struggled with selecting the correct foundation shade. Amazon’s beauty platform lacks an AI-powered shade-matching solution, leading to frequent mismatched purchases, high return rates, and customer dissatisfaction. Our research revealed an 80% return rate for mismatched beauty product purchases on Amazon, highlighting the need for a better solution.
Research Insights
User Pain Points
• Difficulty selecting the right shade due to lack of in-person testing.
• Inconsistent shade recommendations across different brands.
• Frustration with returning products that don’t match.
• Lack of transparency in how recommendations are generated.
Supporting Data
• 80% of survey participants returned beauty products due to incorrect shade matches.
• High demand for virtual try-on and AI-powered recommendations.
Feedback
• Users expressed interest in an AI tool that allows inputting existing foundation shades for accurate comparisons.
• Beauty brands are willing to support shade-matching efforts through better data integration.
Landing on the Solution
Based on user pain points, we designed a hybrid AI-powered shade-matching solution with:
1. Virtual Try-On: Users can test shades using AI for high accuracy.
2. Shade Comparison Tool: Allows users to input existing foundation shades for AI recommendations.
3. AI Transparency Features: Explains how recommendations are generated.
4. Beauty Specialist Chat: Connects users with experts for guidance.
5. Multiple Purchase Options: Direct purchase, subscription, or sample requests.
6. Enhanced Feedback Loop: Improves recommendations over time.
7. Product Education Features: “How to Use” guide, “Features & Benefits” section, and ingredient transparency.
User Flows/Mockups:
https://www.figma.com/design/Mnr8K5b1BzgaSZuDFXS2Mw/Untitled?node-id=0-1&p=f&t=5nBEjzZSaqs1d874-0
User flow: Mermaidchart
Future Steps
• Refine AI accuracy based on user feedback.
• Partner with beauty brands for data integration.
• Improve the virtual try-on experience.
• Expand to other beauty product categories beyond foundation, concealers, lipsticks and powders.
Images:
Learnings
PM’s Learnings
Leading this project has significantly strengthened my product management skills, particularly in user research, product strategy, and AI-driven innovation. Through this process, I have:
• Honed my research and problem identification skills by conducting user surveys, synthesizing insights, and defining a clear problem statement.
• Developed a data-driven approach to decision-making, using user feedback and supporting data to guide feature prioritization.
• Improved my product storytelling skills, crafting a compelling case for how AI-powered shade matching can solve real customer pain points on Amazon’s beauty platform.
• Gained experience in user-centered design, ensuring the solution aligns with customer needs by iterating based on feedback.
• Enhanced my ability to define product requirements and feature sets, breaking down complex AI capabilities into clear, actionable features.
• Strengthened my ability to communicate with stakeholders, including potential partners and business teams, as I explore how to present this solution to Amazon.
As I continue on my journey to becoming a professional Product Manager, I am actively:
• Building expertise in AI-driven product development, understanding how AI solutions can be applied effectively to enhance user experiences.
• Refining my wireframing and prototyping skills using tools like Figma to translate ideas into tangible user flows.
• Strengthening my strategic thinking by aligning product solutions with business goals, ensuring feasibility and scalability.
• Learning from industry professionals through my participation in Co.Lab AI Product Management and DTTP AI Product Manager programs to deepen my knowledge of agile methodologies, product development, and cross-functional collaboration.
This experience has reinforced my passion for product management, and I am excited to apply these skills in a professional role where I can drive impactful, user-centric innovations.
Learnings
Product Manager Learnings:
Imaobong Eyo
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.