A footwear brand needing size-level stock and pricing across Myntra, Ajio, and Amazon — to catch size-specific stock-outs and feed broader fashion catalog intelligence.

Industry

Fashion • Footwear

Region

India

Platforms

Myntra • Ajio • Amazon

Platforms Tracked

Size-Level

Stock Depth

Stock-Out%

By Size

Catalog-Feed

Ready

Contact Sales

Key Takeaways

Actowiz tracked footwear stock and pricing at size level across Myntra, Ajio, and Amazon, exposing size-specific stock-outs invisible to headline availability. Because a shoe in stock in one size and sold out in another behaves very differently on the digital shelf, every size within a listing was enumerated for its own stock and price, then normalized into one comparable schema. Size-level stock-out rates surfaced chronic gaps for replenishment, and the unified output fed directly into the client's broader fashion catalog intelligence — replacing a shallow product-level view with the size-level truth that actually drives lost sales.

What did the client need?

The client is a footwear brand that needed stock visibility not at the product level but at the size level — because a shoe in stock in one size and sold out in another behaves very differently on the digital shelf. This tracking also feeds the client's broader fashion catalog intelligence.

Actowiz tracked footwear stock and pricing at size level across Myntra, Ajio, and Amazon, exposing size-specific stock-outs invisible to headline availability and normalizing everything into one comparable schema.

What made this hard?

Size-level fragmentation. Stock varies by size within one listing, so per-size capture was essential.
Three platform structures. Myntra, Ajio, and Amazon each render size/stock differently.
Interaction-gated stock. Size availability often loads only after selecting the size.
Comparable schema. All three platforms had to normalize into one structure.
Feeding wider catalog. Output had to slot into the client's existing fashion catalog intelligence.

How did Actowiz solve it?

Actowiz built size-aware crawling across the three platforms, capturing per-size stock and price into one unified schema.

Approach

Size-aware agents. Every size within a listing enumerated for its own stock and price.
Three-platform pipelines. Myntra, Ajio, Amazon extracted into one schema.
Stock-out modelling. Size-level stock-out rates computed per style.
Price capture. Listed/sale price captured alongside size stock.
Catalog integration. Output aligned to feed the client's fashion catalog dataset.

Data Attributes Extracted

Attribute	Description
Brand / Style	Footwear brand and style
Size	Individual size variant
Platform	Myntra / Ajio / Amazon
Price / Sale Price	Listed and sale price
Stock Status	In-stock / sold-out per size
Stock-Out %	Share of sizes out of stock
Product URL	Listing link
Scrape Date	Cycle date

What were the results?

Size-level stock truth. Size-specific stock-outs surfaced across all three platforms.
Replenishment signal. Chronic size gaps flagged for supply action.
Comparable across platforms. One schema for Myntra/Ajio/Amazon.
Feeds catalog intelligence. Output slotted into the wider fashion dataset.

Project at a Glance

Metric	Value
Industry	Fashion • Footwear
Region	India
Platforms	Myntra, Ajio, Amazon
Depth	Size-level stock and price
Metric	Size-level stock-out %
Integration	Feeds fashion catalog intelligence
Output	Unified size-level dataset

Client Feedback

“Product-level stock told us nothing — the problem was always a missing size. Actowiz tracked it size by size across all three platforms and plugged straight into our catalog data.”

— Head of Merchandising, Footwear Brand

Frequently Asked Questions

Q: Why track stock at size level?

A: Because a style in stock in one size and sold out in another loses sales; product-level stock hides that.

Q: Which platforms were tracked?

A: Myntra, Ajio, and Amazon, normalized into one comparable schema.

Q: What is captured per size?

A: In-stock/sold-out status and price for each individual size within a listing.

Q: How does it feed catalog intelligence?

A: The unified size-level output slots directly into the client's wider fashion catalog dataset.

Need a custom data pipeline for your category?

Actowiz Solutions designs custom, large-scale scraping and market-intelligence pipelines with rigorous QA. Visit actowizsolutions.com to discuss your data requirement.

Real Results from Real Clients

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

▶

1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

▶

2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

▶

1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner