Which customers prefer to engage with a brand via mobile device and social apps, and which prefer email? Which wording in a post or email is most likely to resonate with a specific customer demographic? What’s the optimal time to send a message to a particular customer segment to yield the highest open rate? Which customers are likeliest to act upon information about ethically sourced product ingredients, for example?
By gathering and making sense of vast amounts of data from various points in the customer journey (loyalty and point-of-sale systems, web site clicks, email open rates, etc.) as well as sources such as social media platforms, AI is helping beauty brands answer questions like these. Then, with a more sophisticated understanding of their customers, markets and the trends driving them, they’re able to put a finer point on the hyper-personalized, hyper-localized customer experiences and journeys that drive customer engagement and loyalty. Not only can the combination of generative AI and agentic AI collect, correlate and analyze all that data, internal and external, structured and unstructured, it also can make highly specific and segmented recommendations, and even autonomously take action itself based on what it learns.
AI’s impact on a beauty brand’s marketing program can be substantial. By freeing data from siloed systems, then applying intelligent analysis and predictive tools to it, the luxury beauty brand Molton Brown, for example, has seen a 5x increase in revenue from email, a 20-25% increase in email open rates without increasing unsubscribes, and a 20% uplift in repeat purchases.
With the combination of synthesized data and AI, beauty brands can begin to capture gains like this via a range of highly targeted marketing activities, including:
Timely, curated and personalized offers. Retailers like Ulta Beauty are using generative AI along with AI agents to create and deliver timely, compelling and hyper-personalized product-related content in the exact moment a consumer’s interest in a product is piqued. These offers are tailored to the person based on a profile developed from internal data along with data from external sources like social media posts. So instead of pushing out blanket offers to wide swaths of customers, brands can send highly customized promotions via customers’ preferred channel(s), vastly increasing the chance those offers will lead to a purchase.
A retailer also could task an AI agent to identify a specific segment of customers that prefer “clean beauty” products, for example, then deliver information to them about the sourcing and ingredients associated with a new product, accompanied by an offer.
Reading between the lines to deliver timely suggestions and offers. During a live interaction with a beauty brand’s natural language-driven AI copilot, a customer mentions they’re looking to freshen up their makeup for springtime. Even a general, non-specific prompt like this can trigger the copilot to make well-informed, in-the-moment product recommendations and offers based on its knowledge of the customer’s preferences, new additions to the company’s spring product lineup, and prevailing beauty trends gleaned from social media platforms.
Curating elevated customer experiences. As convenient as people find on-line shopping, they also crave in-person community experiences. With the help of AI, beauty companies can segment their customers on a hyper-local basis, then reach out to them via their preferred channels with offers to come into a brick-and-mortar store for a hands-on beauty workshop, or to visit a pop-up inside a larger retailer for early access to a new product line. Immersive social experiences like this are a great way to build loyalty. They also can turn a brick-and-mortar store into a vibrant community hub built around the brand.
Empowering customers with interactive technology. From in-store smart mirrors that enable customers to visualize other tones, colors, etc., to mobile apps that turn a phone into a mirror to enable a customer to test different makeup looks, advanced, AI-connected capabilities can shape experiences that create brand loyalty and lead to purchases. These AI-driven innovations bridge the gap between physical and online shopping.
Meeting customers where they are. Which online marketplaces would be the best platforms for your products? Which beauty influencers and independent AI shopping agents should be aware of your brand and its products? Beauty brands can answer these questions with the help of research and recommendations from AI.
By bringing use cases like this together within a coordinated, data-driven, AI-supported multichannel marketing strategy, beauty brands can create the kinds of experiences that forge connections, deepen relationships, and create loyalty with consumers.
