A client needing one clean product catalogue from dozens of fashion and lifestyle stores

Use Case

Catalogue aggregation for search and recommendations

Region

India

Sources

Fashion marketplaces, multi-brand stores, D2C brand storefronts

Number of sources

35+ platforms

Key metric 1

Price, MRP and discount per product

Key metric 2

Size-level availability

Key metric 3

New, changed and removed products

Contact Sales

Key Takeaways

Actowiz delivered multi-platform catalogue data from 35+ fashion and lifestyle platforms to a commerce discovery app. Listings were normalised into one schema, mapped to a single category tree, matched across platforms and served through a REST API with change deltas plus a daily full snapshot, giving the app one clean, current catalogue to search and recommend from.

What did the client need?

The client runs a commerce discovery app in India that helps shoppers find fashion and lifestyle products across many stores in one feed. Its search, filters and recommendation engine all depend on the product catalogue.

The team needed multi-platform catalogue data from 35+ marketplaces, multi-brand stores and D2C storefronts. Every product had to carry the same fields, sit in the same category tree and update quickly when prices, sizes or stock changed.

They wanted the data through an API their engineers could call directly, not files to clean by hand.

What made this hard?

35+ different catalogues. Each platform has its own page structure, category names, size labels and price display.
The same product, many listings. One item can appear on a brand store and several marketplaces with different titles and IDs.
Constant change. New arrivals, price drops and size sell-outs happen daily, so a weekly file would always be out of date.
Messy attributes. Colours, sizes and fits use free text, such as 'Navy', 'Dark Blue' or 'Midnight', that must be standardised before filtering works.
Images stay with retailers. Product images remain the retailers' property, so the feed could reference them by URL but not copy or license them.

How did Actowiz solve it?

Actowiz built a collection, normalisation and matching pipeline that publishes multi-platform catalogue data through a REST API.

Approach

Schema normalisation. Every platform's listing was mapped to one product schema with typed prices, standard size and colour values, and consistent timestamps.
Category mapping. Platform categories were mapped to the client's own category tree, with unmapped categories flagged for review.
Product matching and de-duplication. Brand, title, colour and key attributes were used to link the same product across platforms, while separate listings kept their own prices and stock.
Change detection. Each crawl was compared with the last to mark products as new, changed or removed.
API delivery. A REST API served change deltas through the day, and a full daily snapshot allowed the client to reconcile its database.
Public pages only. Collection covered publicly visible product pages, with no logins and no personal or customer data.

Data Attributes / Fields

Field	Description
product_id	Stable Actowiz ID linking matched listings across platforms
source_platform	Platform where the listing was collected
brand / title	Normalised brand name and cleaned product title
category_path	Product's place in the client's category tree
mrp / selling_price	List price and current selling price in INR
discount_pct	Discount calculated from MRP and selling price
sizes_available / colour	In-stock sizes and standardised colour
image_urls	Links to the retailer's product images (referenced, not copied)
product_url / in_stock	Link to the listing and overall stock flag
first_seen_at / last_updated_at	When the product first appeared and last changed

What were the results?

One catalogue, not 35+. With multi-platform catalogue data, the app searched one normalised catalogue instead of handling each platform's format.
Better matching. Linked listings let the app show the same product's price on several platforms side by side.
Fresher recommendations. Deltas meant new arrivals and sell-outs reached the app the same day.
Simpler engineering. The daily snapshot gave the client a clean reconciliation point, reducing custom ingestion code.

Project at a Glance

Metric	Value
Use Case	Multi-platform catalogue data for a commerce discovery app
Region	India
Sources	35+ fashion and lifestyle marketplaces, multi-brand stores and D2C storefronts
Metrics	Product details, MRP, selling price, discount, sizes, colour, stock, image URLs
Reconciliation / Method	Schema normalisation, category mapping, cross-platform product matching, change detection
Cadence	Intraday deltas plus a daily full snapshot
Output	REST API (JSON); image URLs only – images remain the retailers' property

Client Feedback

“We needed one clean product catalogue from dozens of fashion stores, and Actowiz delivered it through a REST API our engineers could call directly. The daily snapshot and deltas keep our search and recommendations current without any manual clean-up.”

— Product Lead, Commerce Discovery App

Frequently Asked Questions

Q: What is multi-platform catalogue data?

A: It is product listing data collected from many online stores and converted into one schema, so products, prices and stock can be compared and searched together.

Q: How are the same products matched across platforms?

A: Brand, cleaned title, colour and key attributes are compared to link listings, with uncertain matches flagged for review rather than merged.

Q: Do you supply product images?

A: No. The feed includes image URLs only. Images remain the retailers' property, and licensed image datasets for AI training are outside our scope.

Q: Can new products on Shopify or D2C stores be detected?

A: Yes. Public storefronts can be added as sources, and change detection flags new, changed and removed products in each run.

Need a multi-platform catalogue API?

Actowiz can scope multi-platform catalogue data for your platforms and categories, starting with a free sample. Visit actowizsolutions.com to discuss your data requirement.

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