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Product-Attribute Enrichment for Apparel Intelligence — Amazon, Myntra, RGO | Actowiz

Author : Actowiz Solutions | Published On : 03 Jul 2026

Industry

Fashion / Apparel / Retail Analytics

Geography

India — pan-India apparel e-commerce coverage

Data Coverage

Apparel SKUs with deep attribute enrichment, category tagging, trend attributes — Amazon, Myntra, RIGO

Actowiz Solutions delivered deep product-attribute enrichment for a fashion retailer's apparel catalogue intelligence programme — capturing apparel products across Amazon, Myntra, and RGO and enriching each with a normalised, comprehensive attribute set so the retailer's buying and trend teams could analyse the market by attribute, not just by price.

Client Overview

The client is a multi-brand fashion retailer with significant apparel operations, competing across both its own channels and major marketplaces. The retailer's buying, trend, and merchandising teams needed to understand the apparel market at the attribute level — which fabrics, fits, patterns, necklines, and occasions were gaining or losing share — not merely which products were cheapest.

Standard competitive feeds gave the retailer prices and titles. But apparel decisions are made on attributes. A buyer planning a women's-wear range needs to know how 'oversized' is trending versus 'regular fit', how 'cotton' compares to 'rayon' and 'linen blends' in a price band, and which necklines and patterns competitors are launching. Without attribute-enriched data, this analysis was impossible.

The requirement was clear: broad apparel coverage across Amazon, Myntra, and RIGO, with rich product-attribute enrichment and reliably refreshed data — turning the apparel catalogue into a structured, analysable dataset.

Business Challenges

Attributes Buried in Inconsistent Sources

Apparel attributes were scattered — some in structured specification fields, some in free-text descriptions, some only visible in product images. Each platform structured and labelled attributes differently.

No Common Apparel Taxonomy

Amazon, Myntra, and RIGO each used their own category trees and attribute vocabularies. 'Slim fit', 'slim', and 'tailored' might mean the same thing — or not — across platforms. A unified taxonomy did not exist.

Depth vs Breadth Trade-Off

Capturing deep attributes for a few products is easy; capturing deep, accurate attributes across a broad catalogue is hard. The retailer needed both breadth of coverage and depth of enrichment.

Attribute Accuracy

Enrichment is only useful if accurate. Mislabelling fabric or fit would corrupt the buying team's analysis. Enrichment needed validation, not just extraction.

Keeping Enriched Data Current

New apparel styles launched constantly. The enriched catalogue needed regular refresh so trend analysis reflected the current market, not a stale snapshot.

Project Objectives

The client partnered with Actowiz Solutions to:

  • Capture broad apparel coverage across Amazon, Myntra, and RIGO

  • Build a unified, canonical apparel attribute taxonomy

  • Enrich every product with deep, normalised attributes — fabric, fit, sleeve, neckline, pattern, occasion, length, rise, and more

  • Validate enrichment accuracy for buying-grade reliability

  • Refresh the enriched catalogue regularly to support current trend analysis

Actowiz Solutions Approach

Canonical Apparel Attribute Taxonomy

Actowiz built a unified apparel attribute taxonomy covering the full attribute set buyers care about — fabric and composition, fit, silhouette, sleeve length and style, neckline, pattern, print type, occasion, length, rise, closure, and more. Each platform's native attribute vocabulary was mapped into this canonical taxonomy.

Multi-Signal Attribute Extraction

The enrichment engine extracted attributes from every available signal — structured specification fields, free-text product descriptions and titles, and image-derived signals where attributes were not stated in text. Combining these signals produced a far more complete attribute set than any single source.

Attribute Normalisation & Validation

Extracted attributes were normalised into the canonical taxonomy and validated for accuracy through consistency checks and confidence scoring — flagging low-confidence enrichments for review so the buying team received buying-grade, reliable data.

Broad-Coverage Crawl

Dedicated crawlers achieved broad apparel coverage across Amazon, Myntra, and RIGO, ensuring the enriched dataset represented the real market rather than a thin sample.

Regular Refresh & Trend Tagging

The enriched catalogue was refreshed on a regular cycle, with new launches captured and tagged so the retailer's trend team always analysed the current apparel market. Attribute-level trend tags surfaced rising and declining attributes.

Sample Data Snapshot (Illustrative)

  • Women's Top (Amazon): Cotton fabric, regular fit, short sleeves, round neck, casual wear

  • Women's Top (Myntra): Rayon fabric, boxy fit, sleeveless, V-neck, party wear

  • Women's Dress (RIGO): Georgette fabric, A-line fit, 3/4 sleeves, square neck, festive wear

  • Men's Shirt (Amazon): Linen blend fabric, slim fit, full sleeves, spread collar, formal wear

  • Men's Shirt (Myntra): Cotton fabric, regular fit, full sleeves, button-down collar, casual wear

  • Women's Kurta (RIGO): Cotton fabric, straight fit, 3/4 sleeves, mandarin collar, ethnic wear

  • Men's T-Shirt (Myntra): Cotton fabric, oversized fit, short sleeves, crew neck, casual wear

Key Features

  • Unified canonical apparel attribute taxonomy

  • Multi-signal extraction — structured fields, text, and image-derived attributes

  • Deep enrichment: fabric, fit, sleeve, neckline, pattern, occasion, length, rise, and more

  • Confidence-scored, validated attribute accuracy

  • Broad apparel coverage across Amazon, Myntra, and RIGO

  • Regular refresh with attribute-level trend tagging

Business Impact

Within 7 months:

  • Apparel market made fully analysable at the attribute level across three platforms

  • 29% improvement in range-planning accuracy through attribute-level competitive insight

  • Earlier detection of rising attributes (fits, fabrics, patterns) ahead of competitors

  • ₹9 crore revenue from ranges informed by attribute-trend signals

  • Buying decisions shifted from intuition to attribute-backed evidence

Testimonial

"Our buyers think in fabrics, fits, and necklines — not just prices. Actowiz turned three messy marketplace catalogues into one clean, attribute-rich dataset we can actually plan ranges from."

— VP Buying, Multi-Brand Fashion Retailer

Conclusion

Apparel intelligence lives or dies on attributes. Actowiz Solutions delivered deep, validated, normalised product-attribute enrichment across Amazon, Myntra, and RGO — transforming inconsistent marketplace catalogues into a unified, analysable apparel dataset that let the retailer's buying and trend teams plan ranges on evidence rather than instinct.

https://www.actowizsolutions.com/product-attribute-enrichment-apparel-amazon-myntra-rgo.php