欧莱雅要在GhatGPT做GEO了？

This article discloses the latest progress of global beauty giant L'Oréal's end-to-end AI strategy, with key takeaways below:

1. Latest partnership update: L'Oréal has entered a strategic cooperation with OpenAI. Maybelline has integrated its virtual try-on feature into ChatGPT, and multiple brands including SkinCeuticals are participating in ChatGPT advertising pilots. The company is currently deploying generative engine optimization (GEO) on ChatGPT in the U.S. market to improve product discoverability, and has already included GEO in its China market development strategy for the next two years.

2. L'Oréal's full AI ecosystem: Beyond this new partnership, L'Oréal has built a comprehensive AI footprint over years. It early acquired AR/AI firm ModiFace to roll out virtual try-on across its entire brand portfolio; it partners with Unnamed Scentlight on AI-driven ingredient innovation, and collaborates with NVIDIA to develop an AI-powered virtual formula testing engine that cuts formulation development time by roughly 100x; it will also use OpenAI's life science models for skin microbiome research, and has built an in-house generative AI content platform to boost marketing output.

This article shares a complete end-to-end AI transformation case study from a global leading beauty brand, offering actionable insights for multi-functional operational upgrades for beauty brands. Key takeaways are as follows:

1. Insights for product R&D: Brands can integrate AI across their entire R&D pipeline. Partnering or investing in AI-powered biotech firms enables ingredient innovation, while collaborating with tech companies to build AI formulation tools can boost R&D efficiency by up to 100x. Large language models can also advance research in niche areas such as skin microbiome to fill gaps in in-house R&D capabilities.

2. Insights for marketing and distribution: Brands can proactively prepare for GEO, the new generative AI-era traffic entry point, and build an in-house generative AI content platform. This not only boosts marketing content output, but also enables localized customization aligned with brand identity. Mature features such as virtual try-on can also be integrated into large model conversational scenarios to optimize end-to-end user experience and improve conversion rates.

This article outlines the latest AI upgrade trend in the beauty industry, clarifying opportunity directions and risk warnings for all types of beauty sellers. Key takeaways are as follows:

1. Opportunity alerts: Generative AI has opened up new traffic opportunities, and GEO has already become a new priority traffic entry point for leading brands. Sellers can start early deployment to capture early-mover traffic advantages in this new scenario. AI tools also offer a new path to cost reduction and efficiency improvement for sellers: tools for marketing content generation and virtual try-on interactions can be deployed directly, cutting content production and experience upgrade costs for small and medium-sized sellers.

2. Recommended priority areas: Leading brands have already rolled out AI across the entire value chain from R&D to user experience. Sellers can adopt a phased approach aligned with their scale: small and medium-sized sellers can start with marketing content generation and user interaction tools before gradually expanding upgrades across the business.

3. Risk warnings: AI is gradually becoming a baseline capability in the beauty industry. Sellers that fail to keep up with AI deployment will gradually fall behind in operational efficiency and user experience, so brands need to start related deployments as early as possible.

This article discloses the end-to-end AI upgrade momentum of leading beauty brands, bringing multi-faceted insights and opportunities for beauty manufacturing factories. Key takeaways are as follows:

1. Shifting production demand: Leading brands have drastically accelerated R&D speed with AI, cutting formulation development time by 100x, which will noticeably speed up new product iteration. This places higher requirements on factories for rapid sampling and flexible production, so factories need to adjust their production capabilities to adapt to faster iteration cycles.

2. Insights for digital transformation: Leading brands are already advancing full end-to-end AI-enabled digital transformation. Factories also need to speed up digital and AI upgrades on the production side to align with brands' digital R&D and production connection requirements, so as to win more orders from leading brands and improve their own market competitiveness.

3. New business opportunities: AI has accelerated new product innovation in beauty, which will drive growth in small-batch, multi-category production orders. Factories specialized in flexible production will gain more business opportunities, and can also deeply align with brands' AI R&D needs to participate in the new product development process and capture higher value-added revenue.

This article highlights the clear AI upgrade trend in the beauty industry and discloses brands' actual needs, pointing out development directions for beauty industry service providers. Key takeaways are as follows:

1. Overall industry trend: AI upgrades in the beauty industry have moved beyond conceptual trials to full end-to-end deployment. Global leading brands have already completed AI rollout across multiple business functions, and mid-sized and small brands will soon follow suit, meaning the market for AI-related services will continue to expand.

2. Core customer pain points and needs: Brands' AI needs cover the entire value chain: on the R&D side, they need technical solutions for AI ingredient analysis and formulation development; on the marketing side, they need generative AI content customization solutions that preserve brand identity; on the traffic side, they need technical services for GEO deployment; on the user experience side, they need interactive solutions that connect virtual try-on features to large models.

3. Recommended business directions: Service providers can develop modular deployment solutions tailored to the needs of different links in the beauty industry, serving brands of all sizes from global leaders to small and mid-sized players, to capture the opportunities brought by industry-wide upgrade.

This article discloses the latest progress of beauty brands' deployment in new AI-powered scenarios, offering insights for the operation and development of beauty-related marketplaces. Key takeaways are as follows:

1. Shifting brand demand: Beauty brands have started to deploy in new traffic and operation scenarios such as GEO and AI interaction. L'Oréal has also explicitly proposed to co-develop GEO and smart beauty tech with e-commerce platforms. Platforms need to adapt to brands' new demands in a timely manner, open AI-related capability interfaces, and collaborate with brands to upgrade consumer experience.

2. Recommended operational development directions: Platforms can build dedicated recruitment and operation segments focused on AI-powered beauty to attract more beauty brands that prioritize AI upgrade, build differentiated competitive advantages for the platform, and capture first-mover advantage in the industry upgrade.

3. Risk mitigation: New generative AI scenarios are already diverting traffic from traditional search. Platforms need to speed up upgrades to their own AI search and AI interaction capabilities to avoid losing traffic to third-party scenarios, and should proactively develop regulations for AI-generated content and AI-powered experiences to mitigate the risk of user churn caused by non-compliant AI content.

This article documents the latest progress of global beauty leader L'Oréal's end-to-end AI deployment, providing a high-value research sample for beauty industry research. Key takeaways are as follows:

1. New industry trends: Large model technology has fully penetrated the entire beauty industry value chain. From upstream ingredient R&D, formulation development and dermatological research, to midstream marketing content generation, to downstream traffic deployment and user interaction experience, AI has completed full end-to-end deployment. It is no longer an industry concept, but a core competitive advantage for leading brands.

2. New research directions: As a new brand traffic deployment model in the generative AI era, GEO is emerging as a new channel for brands to reach consumers, and has spawned new brand operation and customer acquisition logics, making it a new industry direction worthy of in-depth research.

3. Transformation research sample: L'Oréal's transformation path—from early deployment of single-point technology such as virtual try-on to gradual full end-to-end AI coverage—provides a complete case study for AI digital transformation of traditional consumer goods giants, and offers important reference value for researching AI upgrade logic in traditional industries.