Fashion Apparel Competitive Intelligence Across Amazon, Myntra & RIGO for a D2C Apparel Brand

Industry

Fashion / Apparel / D2C E-commerce

Geography

India — pan-India apparel e-commerce coverage

Data Coverage

Apparel SKUs, pricing, discounts, attribute-enriched product data, ratings, availability — Amazon, Myntra, RIGO

Actowiz Solutions delivered broad-coverage fashion apparel competitive intelligence for a D2C apparel brand — tracking apparel products across Amazon, Myntra, and RGO with deep product-attribute enrichment (fabric, fit, pattern, sleeve, neckline, occasion) and high-frequency data refresh to keep pricing and assortment insights continuously current.

Client Overview

The client is a fast-growing direct-to-consumer apparel brand in the women's and men's everyday-wear categories, selling through its own D2C website and across major fashion marketplaces. As competition in Indian apparel e-commerce intensified, the brand needed continuous, structured visibility into how competing apparel products were priced, positioned, and described across the platforms where consumers actually shop.

Fashion apparel is a uniquely attribute-rich category. Two t-shirts at the same price can be completely different products — different fabric, fit, sleeve length, neckline, pattern, and intended occasion. Competitive intelligence in apparel is meaningless without capturing these attributes; a simple price-and-title feed does not tell the brand what it actually needs to know.

The client's core requirement was specific: competitive intelligence on fashion products, especially apparel, from major players including Amazon, Myntra, and RIGO — with broad catalogue coverage, product-attribute enrichment, and frequent data refresh so the intelligence never went stale.

Business Challenges

Broad Coverage Across Three Major Platforms

Amazon, Myntra, and RIGO together host an enormous apparel catalogue across thousands of brands and styles. Achieving broad, representative coverage — not just a thin sample — across all three platforms required substantial, reliable crawl infrastructure.

Inconsistent Product Attributes

Apparel attributes — fabric, fit, sleeve, neckline, pattern, occasion, rise, length — were described inconsistently across the three platforms. The same attribute appeared under different labels, in free-text descriptions, or only inside product images. Without normalisation, cross-platform comparison was impossible.

Stale Data Risk

Fashion pricing and assortment move fast — discounts change daily, new styles launch constantly, and products sell out. Data captured weekly was already misleading by the time it reached the brand's teams. The client explicitly needed freshness, not a one-time snapshot.

Cross-Platform Product Matching

The same or near-identical apparel styles appeared across platforms under different titles and SKUs. Matching comparable products across Amazon, Myntra, and RIGO required attribute-level similarity, not just title matching.

Platform Anti-Bot Defences

All three platforms operate meaningful anti-bot protection. Sustaining broad coverage with frequent refresh — without disruption — required professional crawl infrastructure.

Project Objectives

The client partnered with Actowiz Solutions to:

• Achieve broad apparel catalogue coverage across Amazon, Myntra, and RIGO
• Enrich every product with normalised apparel attributes (fabric, fit, pattern, sleeve, neckline, occasion, and more)
• Refresh pricing, discount, and availability data at high frequency to keep intelligence current
• Match comparable apparel styles across the three platforms for true competitive comparison
• Deliver structured, attribute-rich competitive intelligence into the brand's merchandising and pricing workflows

Actowiz Solutions Approach

Broad-Coverage Apparel Crawl Pipeline

Actowiz built dedicated crawlers for Amazon, Myntra, and RIGO, designed for breadth — systematically covering apparel categories, brands, and styles across each platform rather than a narrow sample. India-region residential infrastructure and platform-specific session handling sustained reliable coverage at scale.

Product-Attribute Enrichment Engine

Each captured apparel product was passed through an enrichment engine that extracted and normalised attributes from structured fields, free-text descriptions, and (where needed) image-derived signals. Attributes were mapped to a single canonical apparel taxonomy — fabric, fit, sleeve length, neckline, pattern, occasion, rise, length, and more — so that products were comparable across all three platforms regardless of how each platform originally described them.

High-Frequency Data Refresh

Pricing, discount, and availability data was refreshed on a high-frequency cycle, with the cadence tuned to the volatility of each data point — frequent refresh for pricing and stock, with full attribute re-checks on a regular schedule. Refresh cycles were further accelerated during major sale events. This ensured the intelligence delivered to the brand was always current, never stale.

Cross-Platform Style Matching

An attribute-based matching engine identified comparable apparel styles across Amazon, Myntra, and RIGO — using the normalised attributes (fabric, fit, pattern, etc.) rather than titles alone — enabling genuine like-for-like competitive comparison.

Merchandising-Ready Delivery

The attribute-enriched, freshly-refreshed competitive dataset was delivered into the brand's merchandising and pricing workflows via structured feeds and dashboards — with filtering by attribute, platform, brand, and price band.

Key Features

• Broad apparel coverage across Amazon, Myntra, and RIGO
• Normalised attribute enrichment — fabric, fit, sleeve, neckline, pattern, occasion, and more
• High-frequency pricing, discount, and availability refresh
• Cross-platform style matching via apparel attributes
• Sale-event refresh acceleration
• Merchandising- and pricing-ready structured delivery

Business Impact

Within 6 months:

• Continuous, attribute-enriched competitive visibility across three major apparel platforms
• ₹14 crore revenue uplift attributed to data-informed pricing and assortment decisions
• 31% improvement in promotional effectiveness through current, never-stale competitive data
• 3× faster merchandising decisions via attribute-level competitive comparison
• Reduced markdowns through earlier detection of competitive pricing shifts

Testimonial

"In apparel, a price without the fabric, fit, and pattern behind it tells you nothing. Actowiz gave us the full picture — across Amazon, Myntra, and RIGO — and kept it fresh."

— Head of Merchandising, D2C Apparel Brand

Conclusion

Fashion apparel competitive intelligence only works when it is broad, attribute-rich, and current. Actowiz Solutions delivered exactly that — wide coverage across Amazon, Myntra, and RGO, deep product-attribute enrichment, and high-frequency refresh — turning raw apparel listings into a continuously-current competitive intelligence layer that directly informed pricing, assortment, and merchandising decisions.