e.l.f., L’Oréal and Tarte are among beauty brands leaning into AI for data mining, content creation, loyalist engagement via chat, and product discovery as a way to deepen consumer connections.
The details were outlined in an official announcement, with the central theme being less about novelty and more about repeatable, everyday use cases that make marketing feel more responsive to what consumers want in the moment.
Table of contents
Jump to each section:
- How beauty brands are applying AI across the customer journey
- Why “product discovery” is becoming a marketing channel
- What this signals about loyalty, not just acquisition
- What marketers should know about AI in beauty marketing
How beauty brands are applying AI across the customer journey
The notable part of this update is the breadth of use cases being normalized at once: AI for data mining, AI-assisted content creation, AI-driven chat with loyalists, and AI-supported product discovery.
A useful way to read that list is as a map of where marketing teams are trying to remove friction:
- Less time spent turning customer signals into segments (data mining).
- More speed and variation in content output (content creation).
- More “always-on” responsiveness once a customer is already engaged (chatting with loyalists).
- More guidance when the customer is deciding what to buy (product discovery).
Two strategic observations sit underneath that pattern:
1) AI value is increasingly measured in “decision latency,” not novelty. The win is shortening the time between signal and response.
2) Brands are turning service-like interactions into marketing moments. A chat that helps a loyalist find the right product is also a retention and cross-sell touchpoint.
There is also a tension worth naming. Many teams treat AI as a campaign accelerator. The more durable advantage often comes from using AI between campaigns, in the unglamorous middle where people browse, ask, compare, and hesitate.
Why “product discovery” is becoming a marketing channel
Product discovery used to be framed as UX or merchandising. When AI is involved, discovery starts behaving like a channel because it shapes what a customer sees, how they interpret options, and what they do next.
That distinction matters because “discovery” is where intent is formed, not just captured.
If a customer is exploring products and the experience helps them narrow to what fits their needs, the brand has effectively done three marketing jobs at once: reduced choice overload, increased confidence, and made the purchase feel self-directed.
A concise insight marketers can borrow here:
3) In categories with many similar products, the best personalization is often “help me choose,” not “show me more.”
That is also why content creation and product discovery show up together. In practice, the content that performs is often the content that answers the discovery question, not the content that merely announces a product.
What this signals about loyalty, not just acquisition
The mention of “chatting with loyalists” is a clue about where AI is being placed in the relationship.
The default assumption is that loyalty is driven by points, perks, and promotional cadence. The contrasting reality is that loyalty often grows through relevance and responsiveness after the first purchase. AI makes that operationally easier, especially when the interaction is conversational and tied to product selection.
4) Loyalty is being rebuilt as an experience layer, not a program layer.
This reframes what “consumer connection” means in practical terms. It is less about brand storytelling at scale and more about being useful at the exact moment a person is trying to decide.
What marketers should know about AI in beauty marketing
Beauty is showing a pragmatic pattern: use AI where it reduces friction in interpretation, creation, conversation, and choice. Here are the broader takeaways.
1. Treat AI as a response system, not just a content engine.
Content creation is only one piece. The stronger strategic posture is being able to respond quickly when customers reveal intent through behavior or questions.
2. Invest where customer signals become actionable.
Data mining only matters if insights change what a customer experiences. Teams should focus on the handoff from “signal detected” to “experience adjusted.”
3. Make product discovery measurable like a channel.
If discovery experiences are influencing purchase decisions, marketers can evaluate them with channel-like metrics tied to progression, confidence, and conversion, not just traffic.
4. Use loyalist conversations to reduce churn, not to “engage.”
Conversations with loyalists should map to specific friction points: shade matching, routine building, replenishment timing, and product fit. Utility tends to be stickier than generic engagement.
5. Assume the competitive edge will be operational consistency.
Many brands can test AI. Fewer can maintain quality and relevance across day-to-day interactions. Consistency is the real differentiator when AI touches customer trust.
The bigger shift is that AI is pulling marketing closer to the point of decision. When the tools are used for discovery and conversation, marketing stops being something that happens before purchase and becomes part of how customers navigate choice.
That should change how teams allocate effort. Not every investment needs to chase a breakthrough campaign. In many categories, the compounding advantage will come from improving the hundreds of micro-decisions customers make between awareness and checkout.
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