Every claim on a cosmetic product — from "reduces wrinkles by 30% in four weeks" to "suitable for sensitive skin" — must be supported by evidence. Claim substantiation is the process by which cosmetics R&D teams generate, organise, and document the data that proves a product does what its marketing says it does. In the European Union, this is not optional. It is a regulatory requirement under EU Cosmetics Regulation 1223/2009 and the common criteria established by Commission Regulation (EU) No 655/2013. Getting claim substantiation wrong exposes companies to regulatory enforcement, product recalls, and reputational damage in international markets.
The Regulatory Framework for Cosmetic Claims
EU Cosmetics Regulation 1223/2009 establishes that the Responsible Person must ensure all claims made for a cosmetic product are substantiated and included in the Product Information File (PIF). The regulation itself does not prescribe specific testing methodologies, but it sets the principle: claims must be truthful, supported by evidence, and not misleading to the consumer.
Commission Regulation (EU) No 655/2013 goes further, defining six common criteria that every cosmetic claim must satisfy: legal compliance, truthfulness, evidential support, honesty, fairness, and informed decision-making. The evidential support criterion is the most operationally demanding — it requires that claims be supported by adequate and verifiable evidence, regardless of the type of claim. This applies equally to functional claims ("moisturises for 24 hours"), aesthetic claims ("visibly smoother skin"), and comparative claims ("more effective than product X").
Types of Evidence for Claim Substantiation
Instrumental Measurement
Instrumental testing uses calibrated scientific instruments to measure objective changes in skin or hair parameters. Corneometry measures skin hydration levels. Cutometry measures skin elasticity and firmness. Profilometry and optical imaging quantify wrinkle depth and skin surface topology. Spectrophotometry measures colour changes relevant to brightening or anti-pigmentation claims. These measurements produce numerical data — before-and-after comparisons with statistical significance testing — that constitute the strongest form of claim evidence for functional claims.
Clinical Trials and Dermatological Assessment
Clinical studies conducted under dermatological supervision assess a product's effects on human volunteers under controlled conditions. These trials typically follow standardised protocols: defined panel sizes (commonly 20 to 50 subjects for cosmetic studies), controlled application regimens, and evaluation at fixed time points. Dermatological assessment adds expert clinical evaluation — a dermatologist grades skin condition using validated scoring scales. For claims related to sensitive skin, hypoallergenicity, or dermatological tolerance, clinical assessment under patch testing protocols (such as the Human Repeat Insult Patch Test) provides the necessary evidence.
Consumer Perception Studies
Consumer panels evaluate subjective attributes that instruments cannot measure: perceived softness, fragrance pleasantness, texture preference, and overall satisfaction. While these studies carry less evidential weight than instrumental data for functional claims, they are essential for sensory and hedonic claims. A well-designed consumer study uses blind or semi-blind protocols, standardised questionnaires, and statistically meaningful panel sizes. The results are typically reported as percentages — "89% of participants reported smoother-feeling skin after two weeks" — and must be accompanied by the study methodology in the PIF.
In-Vitro and Literature-Based Evidence
Not every claim requires a new clinical study. Certain claims can be substantiated through in-vitro testing (laboratory tests on cell cultures or skin models), published scientific literature on active ingredients, or ingredient supplier efficacy data. The key requirement is relevance: the evidence must be applicable to the specific formulation at the specific concentration used, not merely to the ingredient in isolation at an unrelated concentration. R&D teams must critically evaluate whether supplier-provided efficacy data for a raw material at 5% concentration is meaningful when the finished formulation uses it at 0.5%.
From Formulation Data to Claim Documentation
Claim substantiation does not begin when the marketing team writes copy. It begins in the formulation laboratory, when R&D teams select active ingredients, define use concentrations, and plan the product development pathway with claims in mind. The connection between formulation decisions and claim evidence is direct: the choice of a specific emollient at a specific concentration determines what hydration claims can be supported; the inclusion of a UV filter at a defined level determines what SPF claims are achievable.
This means claim substantiation data is not separate from formulation data — it is an extension of it. The formulation record (ingredient list, concentrations, INCI declaration, regulatory profile) provides the foundation. Stability testing confirms the formulation remains effective throughout its shelf life. Efficacy testing then generates the specific data points that support each claim. In a well-managed R&D operation, these three data streams — formulation, stability, and efficacy — are connected and traceable.
How PLM Software Connects Claims to Evidence
Managing claim substantiation in spreadsheets and disconnected file systems creates structural problems. Study reports stored in one location, formulation records in another, and regulatory submissions in a third mean that no single system can answer the question: "what evidence supports claim X for product Y at its current formula version?" When a formula is modified — even a minor concentration adjustment — the validity of existing claim evidence may change, and disconnected systems cannot flag this automatically.
Purpose-built Product Lifecycle Management (PLM) software for cosmetics, such as Labify® Beauté, addresses this by maintaining the relationship between formulations, test data, and claims within a single connected platform. When a formula version is updated, the system can identify which claims depend on data generated for the previous version. When a stability study reveals degradation of an active ingredient over time, the claim substantiation record reflects this. When a regulatory submission requires a complete evidence package, the platform assembles it from data already present — formulation composition, stability results, efficacy test reports — without manual transcription between systems.
This integration extends to raw material data. With access to structured profiles for over 100,000 raw materials — including supplier-provided efficacy data, recommended use concentrations, and known functional properties — an AI-native platform can assist R&D teams in identifying which ingredients support specific claim targets during the formulation phase, rather than discovering evidence gaps after development is complete.
Common Pitfalls in Claim Substantiation
The most frequent failures in claim substantiation are not scientific — they are organisational. Evidence exists but cannot be located. A study was conducted on a previous formula version and was never re-evaluated after reformulation. Supplier data is accepted at face value without verifying concentration relevance. Consumer study methodology is not documented thoroughly enough to survive regulatory scrutiny.
These are data management problems, not science problems. They occur when claim evidence is treated as a marketing deliverable rather than as a regulated documentation requirement integrated into the product development lifecycle. The solution is structural: connect claim substantiation to the formulation and regulatory data that it depends on, manage it within the same system, and ensure that every claim can be traced back to its supporting evidence at any point — from initial development through post-market surveillance.