L’Oréal’s Mark Elkins on Building a Future-Proof Commerce Engine

Beauty as complexity across segments, shoppers, and formats

L’Oréal’s global commerce strategy isn’t anchored to a single product or playbook. With divisions spanning mass market, luxury, dermocosmetics, and professional channels, the company is effectively four businesses in one. Elkins, who oversees global e-commerce, outlined how this diversity forces L’Oréal to think modularly building platforms and practices that can flex across geographies, customer types, and retail partnerships.

The customer base isn’t monolithic either. From Gen Z TikTok shoppers to dermatology patients seeking clinical guidance, the needs vary widely. Each touchpoint, whether a WhatsApp consult or a product discovery video, requires its own tech and content structure. Internally, this means supporting each brand division with tools that are adaptable, scalable, and embedded across retail ecosystems.

From PDPs to platforms: how search and discovery are being rebuilt

One of the most significant shifts L’Oréal has observed is the fragmentation of product discovery. While once confined to retail shelves and brand websites, the modern shopper now navigates a maze of social feeds, influencer reviews, virtual try-ons, and algorithmic prompts.

This behavior is not just multi-channel, it’s intent-driven. Elkins pointed to the growing expectation that shopping journeys combine emotion, education, and ease. The company is investing in AI tools that personalize product recommendations, simulate application results, and even initiate live consults via retailer integrations.

What emerges is an experience where the path to purchase no longer starts with the brand, it starts with the consumer’s moment of need, wherever they happen to be. The job now is to be present at every one of those entry points.

Social commerce as infrastructure not campaign

L’Oréal was among the earliest Western brands to partner with TikTok Shop and Douyin. But Elkins was careful to distinguish this from traditional influencer marketing. The company doesn’t treat social platforms as promotional add-ons, they’re operational layers where real transactions happen and full journeys unfold.

Success in this model requires not just creator content, but platform-native storytelling, frictionless UX, and a business model that accounts for layered cost structures: platform fees, creator commissions, media spend, and real-time optimization. Social commerce isn’t just digital shelf space, it’s a feed-based ecosystem, where visibility is earned through speed, specificity, and constant iteration.

Internally, this has forced L’Oréal to move away from siloed teams. E-commerce, media, affiliate, analytics, and supply chain now operate in agile squads. These groups are tasked not with static planning but with real-time response to platform behavior: testing, learning, and reallocating as they go.

Creator-centric models demand new economics

One of the biggest adjustments has been philosophical. In social commerce, the content isn’t just marketing, it’s the product layer itself. And creators are no longer distribution partners, they’re co-creators of conversion pathways.

This shift has added complexity to how L’Oréal evaluates ROI. It’s no longer about campaign reach or CPM. Success depends on matching the right creator to the right product for the right consumer moment and feeding the platform’s algorithm accordingly. That requires rapid experimentation, a deep understanding of how feeds function, and a willingness to invest in creator relationships at scale.

As Elkins noted, this has required a cultural change. The company must now be as creator-centric as it is consumer-centric an adjustment many traditional organizations are still struggling to make.

Preparing for agentic AI and the end of search as we know it

The most forward-looking part of the keynote centered on agentic AI autonomous shopping agents already reshaping behavior on platforms like Amazon. L’Oréal has begun restructuring product content to better align with how these agents interpret context. That means moving away from technical descriptors and toward use-case-driven language: not just “SPF 50,” but “ideal for a beach holiday in Spain.”

This change will have a cascading effect. It forces brands to enrich their product data for both emotional and functional relevance and to ensure consistency across every digital channel. As agents scrape content across the web, inconsistencies will directly impact a brand’s visibility.

Highly functional products may benefit. But for brands that rely heavily on lifestyle positioning and visual storytelling, the shift could prove more challenging. Elkins made it clear: the emotional logic of traditional branding will need to translate into machine-readable cues or risk being left behind.

Inside the new operating model

Beneath the technology, the transformation is organizational. L’Oréal has restructured its teams to reflect platform behavior rather than internal reporting lines. Agile squads now operate across functions, building fluid plans that adjust based on real-time data from partners and consumers alike.

This is not a one-time reorg. It’s an ongoing recalibration designed to match the velocity of change in commerce environments that no longer operate on linear timelines. Algorithms are dynamic, consumer needs are nonlinear, and platform logic is opaque. To keep up, the business model itself must be as adaptive as the markets it serves.

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