Derma Reader 2.0

If a customer's skin condition is not accurately assessed in a cosmetics store, recommending the wrong products leads to decreased satisfaction. Previously, beauty advisors (BAs) would diagnose with the naked eye or roughly judge by measuring only some values with simple portable measuring devices, resulting in a lack of data-driven personalized recommendations.

As a result, customers found it difficult to find products that suited them, and stores had difficulty persuading customers due to a lack of objective evidence.

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Derma Reader 2.0 quantifies dozens of indicators, such as pore size, wrinkle depth, pigmentation, and oil distribution, by capturing multi-faceted images of the customer's facial skin through a high-resolution skin image sensor + AI analysis engine [87]. By comparing this with the big data (recommendation matrix by skin type) accumulated by Kiehl's, it immediately derives the most suitable product line and usage for each customer.

For example, it provides customized recipes with scientific basis, such as “○○ toner recommended for excessive oil in the T-zone, and △△ serum and cream combination for insufficient moisture in the cheeks.” In addition, this device stores the customer's skin history and compares improvements upon revisits, allowing customers to visually confirm the effects of product use, leading to repurchase.

In short, the differentiation lies in applying analysis accuracy at the level of a dermatologist to the store front.

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It will be first introduced to Kiehl's stores (B2C retail). Beauty advisors at department store/road shop locations will use this device to conduct customer consultations.

In the future, it can be expanded to other L’Oréal Group brand stores or spas/skin care clinics (B2B) as a utilization license.

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L’Oréal is already a leader in beauty tech, including virtual makeup try-on devices, and will standardize this product in Kiehl's stores worldwide to differentiate its services. In the future, it is possible to plan for expansion into an O2O personal care app by linking data online/mobile, or releasing a home device version for easy measurement at home.

However, managing skin data privacy issues and training BAs to explain the results of AI recommendations well are challenges.

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It was highlighted as an innovative case in the beauty field by winning the CES 2026 Innovation Award. As the on-site response, “Customers understand their skin with numbers, so the trust is different,” shows, it is mainly positively evaluated as a tool to improve the store experience. In particular, there is high industry interest as a case of combining personalization trends and retail tech.

However, some point out that it is not a completely unique innovation because other brands are already introducing similar skin diagnosis services. There are also questions about whether AI recommendations are always optimal (since there are subjective preference factors for each person).

Overall, it is recognized as a useful tool rather than an overestimation.

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