What is
Personalized Skincare Tutorial?
An engine that matches skincare
and related solutions based on compatibility using skin analysis data
An engine that matches skincare
and related solutions based on compatibility
using skin analysis data
Data Acquisition
Quantitatively collects skin
moisture-oil indicators, pigment
distribution, and sensitivity data
to generate skin condition profiles.
Facial Zone Algorithm
Structurally analyzes moisture-oil deviation by skin area
and skin type characteristics
to model skin condition vectors.
Personalized
Product Matching
Cross-analyzes skin condition
vectors and product ingredient
characteristic data to calculate
compatibility-based
skincare combinations.
Basic Care
AI analyzes skin moisture-oil indicators
and sensitivity coefficients to quantitatively
calculate product compatibility for each basic
skincare step: cleanser, toner, essence, and cream.
Computation Structure
▶ Formulation matching based on skin type
▶ Moisture retention prediction model applied
▶ Ingredient-skin reaction data cross-analysis
Ingredients
AI compares and analyzes skin profiles
against ingredient databases to derive active
ingredient groups suitable for the skin condition.
Computation Structure
▶ Calming, moisturizing, and regenerating
ingredient priority calculation
▶ Irritation-potential ingredient filtering
▶ Ingredient compatibility scoring
Treatment
Links skin condition indicators with treatment
characteristic data to exclude excessive or unnecessary
treatments and propose compatibility-based options.
Computation Structure
▶ Treatment suitability assessment
based on skin sensitivity
▶ Recovery prediction model applied
▶ Risk coefficient calculation
Anti-aging
Structures anti-aging management strategies
based on skin elasticity indicators and
aging prediction data.
Computation Structure
▶ Elasticity decrease pattern analysis
▶ Wrinkle occurrence prediction model
▶ Collagen response indicator applied
Home Care Device
Compares and analyzes skin condition vectors
with device output characteristics to calculate
output intensity and usage suitability.
Computation Structure
▶ Output intensity suitability analysis
▶ Skin reaction simulation
▶ Usage frequency optimization model
Clearly proven business impact
by many cases
+14%
Sales
Contribution
Of the total store sales, the share generated
from purchases after Twinit
experience averaged 14%.
×3
Walk-in
Customer Increase
The number of visitors to offline stores
that adopted Twinit AI increased
to an average of 200% compared to before.
+67%
Online Traffic
Connection Rate
Average monthly sales increased by 67% per
store during the Twinit solution
deployment period.
