Estée Lauder Partners with Profound to Track Brands Across Generative AI

The Estée Lauder Companies has partnered with AI marketing platform Profound to monitor and optimize how its beauty portfolio appears across generative artificial intelligence platforms.

Earlier this summer, industry leaders highlighted a new visibility battle for beauty brands as consumers increasingly consult 2 leading AI assistants, ChatGPT and Gemini, for product comparisons and personalized beauty advice.

Through the collaboration, Estée Lauder gains a consolidated view of how its brands are represented across major AI engines worldwide, deploying Profound’s tools to ensure conversational recommendations align with product positioning.

Tracking Generative Engine Optimization

Retail marketing is shifting rapidly from search engine optimization to generative engine optimization. Traditional search engines return ranked links based on keywords, indexing and paid placements. Conversational models work differently. They synthesize answers from training data and live retrieval, producing direct recommendations without standard search results.

Profound co-founder and chief executive James Cadwallader noted that brand owners must actively monitor how algorithmic systems describe their products to shoppers. The software scans model outputs across regions to flag inaccurate claims, missing product lines and unapproved comparisons.

“The way people discover beauty is being rewritten in real time, and we intend to shape that shift rather than reacting to it,” said Aude Gandon, global chief digital and marketing officer at The Estée Lauder Companies.

Shifting Digital Spend From Keyword Search

Beauty conglomerates face higher customer acquisition costs across standard digital channels as retail media networks fragment and search traffic slows. Deploying optimization software lets brand managers identify which datasets feed recommendation models.

That transition creates operational friction for teams accustomed to bidding on static keywords. Generative models update retrieval patterns frequently. As a result, visibility is harder to predict and measure across regional variations in product nomenclature.

Dynvibe chief executive Anne-Cécile Guillemot characterized the rise of conversational discovery tools as a new visibility battle for consumer brands. She noted that companies must rebuild how they structure product data and publish ingredient documentation.

Monitoring Brand Accuracy Across Platforms

Estée Lauder will direct its portfolio teams to standardize product attributes, clinical claims and application guidance across databases that large language models crawl. The initiative covers the group’s global stable of skincare, makeup, fragrance and hair care labels.

Consumer adoption of generative search interfaces has spread across North America, Europe and Asia over the past two years. Beauty remains one of the most queried retail categories on conversational platforms. Shoppers frequently seek advice on complex ingredient combinations and personalized skin routines.

Looking ahead, the two companies will integrate regional language queries into the monitoring platform as AI engines expand local conversational capabilities.