Sephora AI-Driven Personalized Shopping Assistant
AI-powered personalized shopping for beauty enthusiasts.
Problem Space
Problem Statement
How might we improve product discovery, loyalty optimization, and personalized beauty shopping experiences using AI to increase customer satisfaction and business revenue?
Problem Background
Since the beginning of the pandemic in 2020, online beauty shopping has seen significant growth. However, many Sephora shoppers struggle with finding beauty products tailored to their specific skin type, preferences, and needs. The challenge lies in navigating through an extensive product catalogue, optimizing loyalty rewards, and managing repeat purchases efficiently.
Research Insights
User Pain Points
Through customer feedback and research:
- 85% of beauty shoppers expressed frustration with finding the right products.
- Sephora customers struggle to track, redeem, and maximize their loyalty points, often missing out on the best rewards. Many shoppers forget to restock essential beauty products, leading to gaps in their routines.
Supporting Data
- Increased demand for AI-driven personalization in e-commerce (+30% in customer interest).
- Loyalty program engagement improves by 25% with optimized reward recommendations.
- AI-powered recommendations boost conversion rates by 15%
Feedback
- User testing with loyalty program participants highlighted that AI-generated product suggestions significantly improved their shopping experience.
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Landing on the Solution
Based on our target users’ pain points, we focused on the following features:
- AI-powered beauty product recommendations based on skin type, preferences, and purchase history.
- Loyalty program optimization for maximizing reward utilization.
- Predictive shopping assistance to ensure timely repurchases.
- AI-driven discount recommendations at checkout.
- A seamless, intuitive shopping interface.
Explanation of Solution
After showcasing our prototype to users again, we gathered detailed insights that shaped our final solution. The AI-powered shopping assistant was refined to enhance user interactions and functionality based on real user needs.
- AI-Powered Beauty Product Recommendations
- Users receive tailored product suggestions based on skin type, past purchases, and preferences.
- AI dynamically updates recommendations based on browsing behavior and feedback.
- Loyalty Program Optimization
- The system proactively suggests ways to maximize loyalty points and exclusive deals.
- Users receive personalized reward reminders and suggestions for optimal redemption.
- Predictive Shopping Assistance
- The assistant predicts when users will run out of essential products and suggests timely repurchases.
- Smart notifications prevent gaps in beauty routines by offering auto-replenishment options.
- AI-Driven Discount Recommendations
- At checkout, the AI suggests applicable discounts and bundled deals to maximize savings.
- Users receive personalized offers based on shopping behavior and seasonal promotions.
- Seamless Shopping Interface
- The UI was enhanced for intuitive navigation, ensuring a smooth shopping experience.
- Users can quickly add recommended products to their cart with minimal steps.
Future Steps
- Improve AI personalization with more detailed user preferences.
- Expand predictive shopping to include seasonal beauty trends.
- Conduct further A/B testing to optimize the checkout experience.
- Develop a virtual try-on feature for enhanced engagement.
Images
Learnings
Product Manager Learnings:
Nina Pan
This project reinforced the value of user research and AI-driven personalization in e-commerce. Iterating based on real customer pain points led to a more engaging and seamless beauty shopping experience.
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.