How a global prestige beauty company turned product content into cross-channel performance and AI-recommendation visibility

The challenge

Beauty shoppers do not search the way product catalogs are written. They ask for “a buildable cushion blush for sensitive skin,” not “face blush, paraben-free, peach.” That language gap costs visibility in Google Shopping auctions, on retailer sites, and increasingly in the AI-driven surfaces (ChatGPT, Gemini, AI Overviews) where shoppers now ask for recommendations directly.

The company needed to close that gap across paid media, organic discovery, and the emerging AI recommendation layer, without rebuilding catalog operations or changing brand voice.

The solution

Lily Max, the agentic product intelligence engine, enriched the portfolio's product feeds with the consumer-language attributes, structured detail, and intent signals that ranking algorithms and LLMs actually use to match products to shoppers. What Lily optimized:

• Product descriptions: attribute-dense, consumer-language copy that matches how shoppers actually search.
• Structured attributes: comprehensive fields (color, finish, formulation, skin type, occasion, ingredients, claims) using Lily's beauty taxonomy.
• Product highlights: fields surfacing key benefits and differentiators for ranking signals.
• Content-quality scores: systematic improvement across the entire catalog in Google Merchant Center.

Google Shopping: a controlled test

Difference-in-differences A/B tests measured Lily-enriched feeds against matched control sets in Google Merchant Center, with downstream revenue tracked in the brands' own analytics.

Across the portfolio, enriched content drove conversion-value and revenue lifts. Just as telling, enrichment improved traffic quality: in several cases the enriched feeds drew fewer but better-qualified clicks and produced stronger revenue per product. Richer content surfaces products to higher-intent shoppers, not just more of them.

Meta: a conversion lift study

A separate two-cell conversion lift study tested Lily-enriched product content against standard catalog content in always-on paid social. On the primary purchase objective, the enriched cell delivered 42% lower cost per purchase and 28% higher ROAS.

The cross-channel pattern was consistent: better content quality compounds wherever ranking and bidding algorithms read product data.

The AI visibility dividend

The same enriched content is what LLMs read when they generate product recommendations. Better content quality drives stronger performance and engagement signals, and those are the signals AI systems ingest. After enrichment, the portfolio's brands surfaced more often in AI-generated product recommendations in their categories.

Lily improves the readiness and quality of the data these systems read. It does not control final placement or citation, and it makes no such promise. What it does is make a brand the easiest, best-described option for the machine to choose.

The takeaway

Lily Max does not just optimize feeds. It shapes how algorithms and AI see your brand. For this beauty company, that meant revenue and efficiency lifts in paid channels and stronger visibility in the AI recommendation surfaces that increasingly mediate beauty discovery. Product content quality compounds across every surface where shoppers, and the algorithms guiding them, make decisions.