The data

Structured intelligence from public disclosures

Textile Signals collects, normalizes, and structures product composition data from publicly available brand disclosures every season. Value comes from normalization, longitudinal tracking, group/brand mapping, and insight — not raw listings.

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The data

Structured intelligence from public disclosures

Textile Signals collects, normalizes, and structures product composition data from publicly available brand disclosures every season. Each product's fabric label is broken down into standardized data — materials, percentages, garment pieces, and production origins.

Value comes from normalization, longitudinal tracking, group/brand mapping, and insight — not raw listings.

13,732 products indexed across 9 brands
12 individual materials tracked
5 seasons of historical data (SS24 — SS26)
15 production countries mapped
Avg. composition: 47.6% natural, 38.6% synthetic, 12.4% semi-synthetic

Sample data point — Zara (Inditex) · SS26

{
  "brand": "Zara",
  "group": "Inditex",
  "season": "SS26",
  "totalProducts": 3,280,
  "avgPrice": 44.70,
  "materialTypeBreakdown": {
    "Natural": 41.2,
    "Synthetic": 45.6,
    "SemiSynthetic": 11.8,
    "Other": 1.4
  },
  "materialMix": {
    "NaturalOnly": 600,
    "MixedFibres": 1820,
    "SyntheticOnly": 540
  },
  "topMaterials": "Cotton: 30.0%, Polyester: 24.4%, Viscose: 13.2%",
  "productionCountries": [
    "China" (1060),
    "Bangladesh" (600),
    "Turkey" (540),
    "Morocco" (360)
  ]
}

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