How a Brand Accelerator Built Myntra Browse-Rank Intelligence Across Its Portfolio

Browse-rank

popularity order, not PDP

Attribute-enriched

price band, colour, fabric, occasion

Portfolio-wide

decisions across managed brands

Client: Omnichannel brand accelerator (name withheld)

Industry: Fashion / Marketplace Management

Use Case: Category browse-rank intelligence

Platform: Myntra (menswear first), roadmap to more marketplaces

Delivery: Daily browse-rank feed, attribute-enriched

About the Client

Our client is an omnichannel brand accelerator that manages the marketplace presence of global consumer brands across Asia and the Middle East. Running assortment, pricing and replenishment decisions for many brands at once, the team needed to see how categories actually behave on the platforms it sells on — not one product at a time, but the whole shelf, ranked the way shoppers see it.

The Challenge

• Browse-rank, explicitly not PDP. Most fashion "data" is product-detail-page scraping — everything about one product. The client wanted the opposite: category-browse data sorted by popularity, showing which styles and brands own the top of a category and how that shifts day to day. This is a fundamentally different (and higher-value) signal, and it's precisely what most vendors don't deliver.
• Attributes to make rank actionable. A rank list alone is trivia; the team needed each listing enriched (price band, colour family, fabric, occasion) to turn "what's ranking" into assortment and pricing decisions.
• Portfolio scale. Decisions span many managed brands, so the data had to support cross-brand analysis, not a single-brand view.
• Expandable. Starting on Myntra menswear, with a clear roadmap to more categories and marketplaces — the structure couldn't be a one-platform dead end.

The Actowiz Solution

1. Browse-Rank Capture (Popularity Order)

We captured Myntra category pages in the shopper's popularity sort order — delivering true browse-rank, showing which styles and brands lead each category and how positions move over time. Not PDP scrapes: the market view the client actually asked for.

2. Attribute Enrichment

Each listing was enriched with price band, colour family, fabric and occasion, so rank data became merchandising intelligence — the team could see not just what ranks but why a segment is winning.

3. Portfolio-Ready Structure

Data was structured for cross-brand analysis, so the accelerator could apply insights across the brands it manages, not just one at a time.

4. Extensible to More Marketplaces

The schema and pipeline were built to add categories and marketplaces without re-work, matching the client's expansion roadmap.

The Results

• Market view — true category browse-rank (not PDP) showed which styles and brands own the top of each category — and how it shifts daily
• Actionable — attribute enrichment turned rank lists into assortment and pricing decisions across the managed portfolio
• Cross-brand — one structure served decisions for many brands, not a single-brand snapshot
• Scalable — built to extend to more categories and marketplaces without rebuilding

Client Feedback

"PDP scrapes tell us about a product. Browse-rank tells us about the market. Finding a partner who understood that distinction — and enriched it so we could act — is why this worked across our whole portfolio."

— Category Head, omnichannel brand accelerator

Why It Worked

• Right signal, not just data. Understanding that the client wanted browse-rank, not PDP, was the entire solution — a distinction most vendors miss.
• Enrichment makes rank usable. Attributes turned a popularity list into merchandising decisions.
• Built to scale. A portfolio-ready, extensible structure matched how an accelerator actually operates.

Frequently Asked Questions

What's the difference between browse-rank and PDP data?

PDP data details one product; browse-rank captures how a category orders products by popularity — the market view for assortment, trend and share-of-shelf decisions. We deliver either, but browse-rank is often the higher-value ask.

Do you enrich listings with attributes?

Yes — price band, colour family, fabric, occasion and more, so rank data becomes actionable merchandising intelligence.

Can you cover multiple brands and marketplaces?

Yes — the structure is portfolio-ready and extends across categories and marketplaces (Myntra, Ajio, Nykaa, Amazon and beyond) without rebuilding.

How fresh is the feed?

Daily by default, with intra-day refresh available during platform sale events.

Is it compliant?

We collect only publicly displayed listing, rank and price information — no accounts, no personal data — under Actowiz's responsible-scraping framework.