Multi-Platform Fashion Catalog, Size-Level Stock & Pricing Intelligence — 7 India Platforms

Client Overview

The client is an India-based fashion and lifestyle brand competing across a mix of single-brand storefronts and large multi-brand marketplaces. Their merchandising and pricing teams needed a dependable read on how assortment, pricing, and stock behaved across seven key platforms — Bewakoof, NewMe, The Souled Store, Savana, Urbanic, Nykaa Fashion, and Forever New.

These platforms don’t compete on the same terms — some are single-brand streetwear labels with a handful of drops a month, others are sprawling marketplaces onboarding new labels every week. Tracked separately in each site’s own format, the numbers rarely lined up when it was time to compare. The client wanted one monthly extraction run, one schema, across all seven — so pricing, stock, and assortment data would land in a directly comparable form.

The Challenge

In fashion, pricing and stock are fractured below the product level. A single listing usually hides a dozen size and colour combinations, and each behaves differently — in stock in M, sold out in L; discounted 20% in blue, full price in black. None of that shows up if you capture one headline price per product. Building a comparable picture across seven structurally different sites raised distinct hurdles:

• Variant-level fragmentation. The same SKU can be in stock in one size and sold out in another, or discounted differently by colour — invisible to any tool capturing a single headline price.
• Sale-cycle velocity. All seven platforms run frequent sale events where Price, Sale Price, and Final Price diverge sharply and shift within the same day.
• Catalog churn. Marketplaces like Nykaa Fashion onboard and delist brands continuously, while single-brand sites refresh drops on their own calendars — so last month's coverage may already be stale.
• Fragmented category taxonomies. Each platform defines its own category tree — one site's "Co-ords" is another's "Sets" — making like-for-like comparison impossible without a normalized schema.
• Deep, nested variant structures. Size and colour selectors render client-side and must be interacted with — not just read from initial page load — to reveal true per-variant price and stock.
• Unstructured size charts. Size guidance arrives as images, embedded widgets, or brand-specific tables rather than a consistent field, resisting standardization without deliberate parsing.
• Region-sensitive availability. Serviceability and in-stock status vary by delivery pincode, so queries without a consistent, representative location produce misleading availability.
• Review & rating depth. Ratings, review counts, and review text sit behind pagination and lazy-loading, and must be captured consistently for sentiment and quality benchmarking.
• Duplicate & re-listed products. The same style is frequently re-listed under new product IDs during restocks or migrations, requiring stable deduplication across monthly pulls.

Results & Business Impact

• Cross-platform assortment benchmarking. Merchandising teams can see exactly how category depth and brand mix compare across all seven platforms — marketplace and single-brand alike — category by category.
• Precision discount tracking. Separating Price, Sale Price, and Final Price at variant level, like-for-like across platforms, exposes true discounting behavior and supports sharper promotional planning and margin protection.
• Stock-out intelligence. Size-level availability across every platform highlights which sizes or styles are chronically out of stock, informing replenishment and vendor conversations.
• Data-backed vendor & category reviews. Ratings, review volume, and tag data give category teams objective evidence of which brands and styles resonate on each platform — replacing anecdotal reporting.
• Consistent, auditable monthly feed. A stable, deduplicated dataset across all seven platforms on a predictable cadence removes the manual effort and inconsistency of ad hoc catalog checks.

Client Feedback

"Before this, comparing ourselves across seven sites meant seven different spreadsheets that never quite matched. Now it's one feed, down to the size and colour — we can finally see who's discounting what, and where we're losing sizes, in the same view."

— Head of Merchandising, Fashion & Lifestyle Brand

Need multi-platform catalog & stock intelligence for your brand?

Actowiz Solutions designs custom, large-scale scraping and enrichment pipelines with rigorous QA — variant-level depth, normalized schemas, and predictable delivery. Visit actowizsolutions.com to discuss your data requirement.