L'oreal Brandstorm
Summary: This article explores how L’Oréal can implement AI Beauty Assistants to create a more personalized experience.
The Concept
The project aims to develop an AI Beauty Assistant application to revolutionize the beauty, hair, and wellness experience. This will provide users with a personalized, AI-driven platform. The application will have agentic virtual assistants to answer beauty and health. Users can ask beauty questions, schedule appointments, purchase products, and more. The algorithm will be trained to understand user preferences, skin/hair types, and personal goals.
Ideation & Research
L’Oréal’s Mission
“create beauty that inspires and moves the world”
Current Consumers & Market
L’Oréal’s current consumer base is diverse and global. They capture about 41% of the skincare market, 22% of haircare, and 16% of makeup. They also have a strong presence in luxury fragrances, which continue to grow. L’Oréal has also made investments in dermocosmetics to meet consumer demands for wellness.
Market Research & Validation
L’Oréal’s core users seek convenience and personalization through tech. Several users feel overwhelmed by the number of brands and options and heavily rely on recommendations. Currently, a lot of it is driven by marketing, but agentic assistants can help make decisions based on what’s useful to an individual over simply going for the most popular item.
Current Competitors
L’Oréal’s main competitors are other global beauty giants that operate across various sectors such as skincare, haircare, makeup, and luxury products.
Estée Lauder is a leading competitor in the skincare and makeup segments, as it owns brands like Clinique, MAC, and Bobbi Brown. They also have a presence in the luxury fragrance market, like L’Oréal’s Lancôme and Giorgio Armani lines.
Procter & Gamble (P&G) competes with L’Oréal mainly through haircare brands like Pantene and Head & Shoulders. They also are growing in the skincare market with Olay, with their main focus being innovation and sustainability.
Shiseido is a competitor in the skincare market with advanced formulas and a strong presence in Asia. L’Oréal competes with their luxury skincare market.
Unilever is known for its product line in the beauty and personal care industries. It competes with L’Oréal’s brands like Dove, Tresemmé, and Axe. They are heavily focused on sustainable practices.
Coty owns brands like Sally Hansen and CoverGirl and is a major competitor in the mass-market cosmetics and fragrance industry.
LVMH (Moët Hennessy Louis Vuitton) is dominant in the luxury beauty segment through brands like Dior and Givenchy.
Key Features Ideas
AI Beauty Consultant: Users can interact with an AI agent by asking questions about beauty, skincare, health, etc. The responses will be personalized based on user preferences, the latest trends, and past interactions.
Smart Scheduling: Integrated with salons, dermatologists, etc, users can schedule appointments through the app which recommends professionals based on locations, reviews, etc.
Product Recommendations: The AI agent can recommend beauty and health products from the L’Oréal range based on the user’s profile, like skin type, hair type, goal, preferences, and more.
Progress Tracking & Personalized Care: Users can track their beauty and health progress, by logging skin or hair changes, routine changes, etc.
Content & Tutorials: AI agents can generate personalized tutorials, how-to guides, and articles based on user needs.
Seamless Shopping Experience: Users can buy products based on AI recommendations. This will be integrated with L’Oréal’s e-commerce platform.
Community & Social Integrations: This allows users to join communities, share their journeys, follow influencers, etc. AI agents can recommend groups, and community events based on user needs.
Use Cases
Here is a list of a few key use cases but not limited to:
Personalized Beauty Routines: Beginner users want to improve their skincare routines.
Virtual Hair Consultations: A user is considering a hair color change but is unsure about the color choice and integrity of their hair.
AI-Driven Product Recommendations: A user is shopping for makeup but is overwhelmed by the options.
Health & Wellness: A user wants to improve their skin and hair health.
Virtual Try-Ons: A user is curious about how the new lipstick shades from L’Oréal will look on their skin tone.
Smart Shopping Assistant: A user needs help finding the right products while browsing a store.
Beauty Community and Social Sharing: A user wants to find a community of curly hair girls and guys, to share and learn from others.
Appointment Scheduling: A user wants to book a salon appointment for a treatment.
Data-Driven Insights for Personalized Campaigns: L’Oréal can offer personalized product deals and promotions.
Planning and Design (PRD)
Objectives
The goal is to create an AI-driven mobile application that enhances users’ beauty experience through personalized beauty routines, product recommendations, virtual try-ons, and access to expert advice. The app will allow users to book beauty appointments and purchase L’Oréal products directly from the platform. This will increase user engagement and drive product sales.
User Personas & Stories
As a skincare enthusiast, I can use the app for daily routine guidance, and product recommendations, and track my progress on my skin health.
As a mom or working professional, I want to use the app to schedule salon appointments, shop for beauty products, and get virtual consultations more efficiently.
As a consumer, I want to find sustainable, eco-friendly products and learn more about product sourcing and packaging.
Features Scope
Personalized Beauty Routines: The agent will analyze the user’s profile and generate tailored skincare, haircare, and other requested routines.
Virtual Consultations: Users utilize augmented reality to understand best-suited hairstyles, hair colors, products, and more.
Smart Shopping Assistant: The app can provide real-time product recommendations based on the user’s shopping patterns.
Appointment Scheduling: Users will have the option to book appointments at any L’Oréal partner salon.
Health and Wellness Integration: Users can integrate health data from wearables that can be used to curate a well-rounded beauty routine.
Sustainability Options: Users can find eco-friendly products by adding filters such as biodegradable, refillable, or cruelty-free options.
Community Engagement: Allows users to participate in community threads where they can share stories, routines, and tips and receive feedback.
Functional Requirements
User Profiles: Users can create profiles to input beauty preferences and health data.
AI Beauty Diagnostics: Uses user data and image analysis to determine skin/hair types and conditions and recommend more personalized routines. ML models are used for image recognition and NLP.
Virtual Try-On: Utilization of augmented reality software to develop an AR feature that allows users to try makeup or hairstyles in real-time.
E-Commerce Integration: API integrated with L’Oréal’s e-commerce platform so users can purchase recommended products directly from the app.
Push Notifications & Reminders: Users receive reminders to follow their beauty routine, use products, and attend appointments.
Non-Functional Requirements
Security & Privacy: All user data must be securely stored in compliance with GDPR and CCPA regulations.
Performance: The app should load product recommendations, beauty analyses, and AR experiences within seconds.
Scalability: The system should be scalable to handle thousands of concurrent users and must support multi-language functionality considering L’Oréal’s global market.
Technical Considerations
Tech Stack
Frontend: App built on React Native for cross-platform development to ensure consistency in UI.
Backend: Tools such as Node.js & GraphQL will be utilized to streamline data querying.
AI & ML: NLP Models and CNN models, fine-tuned towards wellness and beauty, will provide personable responses for users.
Databases: Database solutions such as MongoDB for storing data and PostgreSQL for transactional data can support complex queries
Integrations & APIs
AR/VR: Tools such as ARKit / ARCore can be utilized for AR/VR components.
Health Data Integrations: Options to integrate Apple Health or Google Fit to collect health metrics.
E-comm & Payments: Develop custom APIs to enable in-app purchases. Option to utilize Stripe for secure transactions.
Notifications: Utilize FCM for push notifications, and reminders.
Data Security and Compliance
Data Encryption: Encrypt sensitive at-rest & in-transit data.
GDPR & CCPA Compliance: Store personal data in regional data centers and be transparent about data collection with users.
Access Control: Implement RBAC policies to manage access to sensitive user data.
Scalability & Reliability
Server Load Balancing: Utilize AWS Elastic Load Balancing to distribute incoming traffic across several servers for consistent performance.
Microservices: Utilize microservices to separate functionality so it’s easier for future scalability and maintainability.
Success Metrics
User engagement rate, conversion rates, and customer satisfaction, all measure the features’ success. Users interacting with the search results and leaving impressions showcase users' engagement rates. Users who find the results useful to them or those who make purchases based on shopping suggestions help refine the overall relevancy score.
Risks and Mitigations
Security of Personal Data: Encrypting sensitive data, implementing user consent for data collection, and conducting regular security audits will limit potential security threats.
Accuracy of AI: Ensuring a diverse training data set can prevent inaccurate or irrelevant results.
Dependency on AR/VR: AR/VR components require significant processing power, so implementing optimized AR/VR features that adjust based on device capabilities may minimize device strain.
Market Competition: Continuous development of features, accurate models, and L’Orèal exclusive content can differentiate the app from other competitors.
User Retention: Offering rewards, recommending personal community threads, or showcasing exclusive content can increase user retention.
Stakeholders
Product Management: Define features and develop go-to-market strategy.
Engineering: Responsible for determining architecture, development testing, and release.
UX/UI Design: Design the user interface and experience for the application.
Sales & Marketing: Develop creative and strategic campaigns to promote this new product.
Development and Testing
Agile Development
Break down the development process into sprints to focus on building the core functionalities. Determine which project-tracking software works best for the team. Hold standups and sprint retrospectives to track progress and address any issues.
Here is an example of a sprint breakdown:
Sample Sprint Overview:
Goal: The goal is to develop a smart shopping assistant
Duration: 2 weeks
Members: Product Manager, Frontend Engineer, Backend Engineer, AI & ML Engineer, UX/UI Designer
Epic 1: Learn about a Product
Assignees: Frontend Devleopers & Backend Devleopers
User Story: As a user, I want to learn about the ingredient list of a product
Task 1: UI component for selecting a product
Task 2: Backend development to handle the query
Iterative Testing
Focus on user experience, performance, and accessibility for internal and external testing. Gather feedback from usability testing from several users and make continuous improvements.
Quality Assurance
Conduct QA testing, including functional testing, security testing, and stress testing to ensure the feature can handle multiple users.
Beta Launch & Feedback
Select a target user group to launch the feature and get early feedback. Continuously measure performance against KPIs such as user satisfaction, engagement rates, and search relevance. Document these insights and make necessary improvements to the functionality, experience, and performance of the product. Refine AI models using data from feedback loops and user behavior.
Full Launch & Marketing
As part of the PRD, plan a full rollout to all L’Oréal users. Ensure the platform is fully functional and all bugs from the beta tests are resolved. Work with the marketing team to develop a marketing campaign highlighting the value of visual search and focusing on trend-based communities. Work on a GTM (go-to-market) strategy where the company could collaborate with popular creators to showcase the feature through personalized content such as tutorials or challenges, etc.
Post-Launch Monitoring & Maintenance
Product managers can use a couple of frameworks to monitor feature metrics. Two popular ones are HEART and AARRR, which track growth, engagement, retention, user satisfaction, and revenue. Using data analytics to gain insights on engagement and retention can help identify high-value improvements, expand feature capabilities, scale partnerships with brands, and continuously provide relevant search results. Collecting user feedback helps improve the feature or introduce new enhancements to increase user satisfaction. Finally, based on revenue and user satisfaction, decide if planning new enhancements for the product will be a good future investment (the final stage of the product lifecycle).