Beauty brands are scaling across categories and channels, and the pressure to deliver smarter, faster marketing has never been higher. As global skincare revenue is projected to hit $206.6 billion and haircare $99.7 billion in 2025 (Statista), brands are racing to keep up in a competitive market. Artificial intelligence in beauty has quickly become essential for brands looking to stay ahead—helping teams cut through information overload, gain clearer visibility across product categories and move faster with data-backed decisions.
At the same time, the beauty space has become increasingly segmented by product categories. Skincare, hair care, cosmetics and fragrance each follow distinct product cycles, audience expectations and storytelling needs, making a one-size-fits-all approach to campaigns ineffective.
This complexity only increases as brands compete across a growing mix of prestige, mass market, indie, clinical and star-led players—a shift highlighted in our Beauty Industry Growth Playbook, which shows how performance is becoming harder to track and compare as segments multiply.
If we go back to product categories, beauty marketing and PR teams often find themselves sifting through endless media mentions to distinguish a mascara drop from a serum launch. This can be time-consuming, inefficient and simply exhausting.
Instead, tracking brand performance at scale requires a category-specific lens: something that’s nearly impossible to manage manually. That’s where artificial intelligence makes the difference. AI helps beauty brands streamline category-level media monitoring, gain deeper consumer insights and react faster in an increasingly competitive landscape.
In this article, we’ll explore how AI is transforming the beauty industry and why it’s becoming essential for any brand looking to lead this space.
Why do beauty brands need artificial intelligence?
Within beauty brands, each product category—skincare, hair care, cosmetics and fragrance—often operates as its own “mini-brand,” with dedicated teams, distinct goals and separate KPIs. This structure is essential for growth, but it introduces a clear challenge: how do you measure media coverage, campaign performance and competitor activity by category, without being overwhelmed by data?
PR and marketing teams often had to comb through media coverage and competitor benchmarks manually, just to understand how the brand is performing overall. But when an article mentions a brand like L’Oréal, Estée Lauder or Coty—without clarifying whether it’s referencing a lipstick, cleanser or conditioner—it points to a missed critical insight. That lack of specificity becomes a bottleneck, slowing decisions and clouding category-level performance.
This is where AI in the beauty industry brings structure, clarity and speed to an increasingly siloed ecosystem.
How can artificial intelligence in beauty help brands be more efficient?
AI-Powered Categorization: Filtering Between Multiple Product Categories
For brands managing multiple product categories, the volume of coverage, mentions and data points grows fast—and so does the complexity of tracking what matters. Artificial intelligence in the beauty industry, specifically AI-powered categorization, helps solve this by doing what manual methods can’t: identifying product-specific mentions, tagging content by category and surfacing insights instantly.
Take a skincare team, for example. Instead of manually filtering through irrelevant makeup or fragrance mentions, they can access insights already categorized by product type—showing what’s performing, where and in what context.
This level of precision is only possible when AI operates within a platform built for it. Launchmetrics, a leading Brand Performance Cloud, captures and organizes brand coverage across print, online, broadcast and social channels—tailored specifically to the needs of Fashion, Lifestyle and Beauty brands. Each mention is tied to the Voice behind it—Media, Influencers, Celebrities, Owned or Partners—so teams can easily filter by product category, region or channel. AI helps categorize and structure this data at scale, reducing manual effort and enabling teams to interpret what matters, based on a dataset that’s accurate, consistent and built from a single source of truth.
Media Impact Value® (MIV®): An AI-Powered Brand Performance Metric
If your coverage is centralized, categorized and filtered, the next layer is truly understanding what that coverage is worth through our propietary metric: Media Impact Value® (MIV®).
MIV is Launchmetrics’ AI-powered algorithm that assigns a monetary value to every placement across print, social and media channels, regions and Voices. It is calculated on both qualitative and quantitative attributes such as reach, engagement, source credibility, placement quality, audience engagement and more, to enable Fashion, Lifestyle and Beauty (FLB) brands to have an apples-to-apples comparison of placements. For instance, an Instagram post by a Celebrity such as Kim Karshadian vs an online article by Vogue U.S can be measured on the same scale, giving brands a key KPI to compare the true value of each.
This is extremely useful when showing the ROI of your campaigns, a collaboration or a strategy. As metrics such as reach and engagement scratch the surface of brand performance, while MIV helps to show how impact each activation and post really was, so teams can optimize their strategies.
AI Analyze Coverage: Understanding Your Coverage Immediately
Rather than sifting through your feed and scanning through hundreds of placements, AI can be used to to understand what your coverage is actually saying about your brand.
Launchmetrics’ Analyze Coverage was built to provide an executive summary of your feed for instant insights. With the click of a button, users can draw instant insights from a sample of its feed to uncover:
- Executive Summary: the key conversations and coverage patterns appearing repeatedly in your feed.
- Key Themes: the 3-4 common narratives appearing in your feed, with an MIV penetration score to understand which conversations are driving the most impact.
- Follow Up Questions: designed as if a strategic analyst is next to you, this feature surfaces questions to guide you in either challenging assumptions, uncover blindspots or unveil potential opportunities to look further into.
Implementing AI in the beauty industry: What this looks like in practice
Within the Launchmetrics platform, two metric-focused tools in particular—Discover and Insights—bring this level of visibility to life in powerful, actionable ways. To distinguish both modules:
- Discover helps brands monitor brand mentions, influencer content and media placements across categories in real time.
- Insights transforms that data into visualized dashboards and reports that highlight what’s driving performance and how competitors stack up.
Together, they provide a clear view of what’s working, where and why, with our proprietary metric Media Impact Value® (MIV®). Using MIV, brands can track category-level performance, benchmark against competitors, monitor PR coverage more precisely and uncover emerging global trends.
Let’s see this in practice through the lens of a regional director of a large brand such as L’Oréal.
Use Case: Managing PR & Marketing Performance Between Beauty Categories and Markets
Let’s imagine a regional director at a global brand is overseeing launches across skincare and cosmetics. Their goal is to understand how each category is performing across multiple markets, platforms and Voice types.
When a renowned brand such a L’Oreal has strong global visibility, it’s not uncommon for coverage to reference the master brand without distinguishing between product lines. Naturally, this happens with a brand of this scale. What this highlights is how easily category-level visibility becomes blurred, making it difficult to track what is driving media impact within each category: a serum launch, a lipstick drop or a strategic influencer partnership.
To bring this scenario to life, we’ll use L’Oréal as an illustrative example of the world’s most influential beauty brands. With broad category reach and high media volume, this example offers a powerful lens into how complex brand performance data becomes at scale, and how AI-powered categorization helps make that data more granular and easier to interpret.
Unlocking Category-Specific Monitoring
In our media monitoring tool, the regional director can monitor brand coverage across global print, online and social media. AI-powered categorization automatically tags each mention by beauty category (eye makeup, skincare, fragrance and more) so teams can easily isolate insights for their specific focus.
Combined with location filters, this allows regional teams to see which product lines are performing where. Whether it’s a skincare campaign in the UK or a makeup launch in Italy, every mention is tied to the Voice behind it: Media, Influencer, Celebrity, Owned or Partner. This makes it easier to understand who’s driving impact across each market.
By layering in MIV, the team can then assign a consistent monetary value to that exposure. From there, they can benchmark categories, compare regional performance and identify which placements and Voices are delivering the most value.
For context around their coverage, the regional director can also analyze their coverage around the conversations emerging from their make up coverage, particular from Traditional Media. Using the Analyze Coverage feature, they would able to see in the example below that the “Affordable Glamour” key theme appeared in roughly 12% of their coverage, driving 16% of their MIV. From the AI analysis, it confirms how their make-up coverage is repeatedly positioning products as celebrity-adjacent or editor-loved steals, indicating further credibility to their coverage.
These insights can then be shared through tailored newsletters, reports or coverage books filtered by category, so every stakeholder receives what’s most relevant to their role.
Benchmarking with Precision
With category-specific visibility in place, the next step is measuring how performance stacks up against competitors. In our competitive benchmarking platform, the director can benchmark L’Oreal Paris for instance, against others in the same category and region to see how their Share of Voice compares.
MIV can also be used as a KPI to track progress over time—especially useful for spotting seasonal trends or evaluating the success of time-bound initiatives like gifting campaigns, events or new product launches. This clarity enables teams to make smarter decisions around campaign timing and strategy.
And by analyzing top placements ranked by MIV, the director can identify the trends, topics and Voices generating the most media impact. Whether it’s a standout influencer, a strategic media partnership or a viral campaign, Insights makes it easy to see what’s working and replicate that success.
See How AI Can Help Beauty Teams Navigate Category Complexity
In an industry where product launches, media moments and partnerships move faster than ever, clarity matters as much as speed. Artificial intelligence helps beauty teams move beyond volume, bringing structure to performance data and sharper insight into what is actually driving impact.
Our Beauty Industry Growth Playbook explores how category expansion and segment fragmentation are reshaping beauty performance, and why brands need a more precise lens to benchmark, prioritise and grow with confidence.
Whether you are scaling a skincare portfolio, tracking momentum across fragrance or evaluating performance across markets and Voices, the Playbook offers the latest insights to guide you in an increasingly complex space.
Learn More About the Analyze Coverage Feature
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Frequently Asked Questions
Beauty brands typically use AI to personalize experiences, support product development and make sense of large volumes of data. In marketing and PR, this often shows up in media monitoring, content categorization and performance measurement. Tools like Launchmetrics use AI to identify product-specific mentions, separate coverage by category and reveal which Voices drive impact across channels and regions, without manual data sorting.
AI only performs as well as the foundation it is built on. When data is clean, consistent and structured, the insights become accurate and actually useful. At Launchmetrics, AI-powered categorization identifies product-specific mentions accurately, tags coverage by category and Voice and structures content across print, online and social channels, giving teams a clear, shared view of performance. This helps make internal conversations around focus, priorities and next steps far more grounded.
For most beauty brands, the real challenge is brand performance reporting at scale. That means choosing a platform where AI supports data analysis in a practical way, saving time and reducing manual effort. Key factors include a beauty-specific data foundation, a consistent metric such as Media Impact Value and AI-powered categorisation that makes insights easy to navigate and digest.
This becomes especially important as brands expand across multiple regions, channels and product categories. As Elenora Papi, Franchising and New Channels Communication Specialist at Kiko Milano, explains in our case study: “Without Launchmetrics, it would be really hard to measure our results, considering how many contents, PR campaigns and countries we manage. It’s a tool we rely on every day. Without it, working efficiently and understanding what’s working would be almost impossible.”
By Launchmetrics Content Team
