Consumer Profile
More about consumer profile, which include age, gender, skin type, concerns...
CosmetiQ-MCP enriches each review with demographic and skin-attribute tags, extracted via multilingual LLMs and cross-checked against marketplace metadata.
Gender
feminine · masculine · unisex · not specified
Age band
teen · adult · mature · all ages · not specified
Skin type
dry · oily · combination · normal · sensitive · unknown
Skin concerns
acne, blackheads, dullness, pigmentation, redness, …
Consumer-profile data enter CosmetiQ-MCP through two complementary routes:
Explicit metadata. Many retail platforms ask shoppers to tick boxes—age range, gender, skin type—before submitting a review. Where available, we ingest those fields verbatim.
LLM inference from review text. When the form is missing - or incomplete - we let multilingual models read what the reviewer actually says:
“As a teenager guy with super oily T-zone…” →
age = teen,gender = masculine,skin_type = oily.“Finally something that calms my breakouts.” →
concern_breakouts = 1.
Why this layer matters:
Precise targeting – Compare how Gen-Z oily-skin users vs. Millennial dry-skin users rate the same serum.
Persona-led formulation – Match actives, texture and fragrance to the ingredients cohort that values them most.
Claim validation – Prove “sensitive-skin approved” by isolating reviews with the
SkinType_sensitiveflag.Regional nuance – Overlay profile tags with region data to see, for example, whether acne remains the top concern for teens in Spain but not in Germany.
Linked to product, ingredient and region keys, the Consumer Profile layer transforms raw opinions into actionable personas your R&D and Marketing teams can design for with confidence.
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