A ghee / dairy FMCG brand needing to measure its digital shelf share against competitors across four q-commerce platforms and ten cities — by SKU count, ₹/litre, and OOS%.

Industry

FMCG • Dairy / Ghee

Region

India — 10 cities

Platforms

Blinkit, Zepto, BigBasket, Amazon Fresh

Q-Commerce Platforms

Cities Tracked

₹/Litre

Normalized Pricing

OOS%

Availability Signal

Contact Sales

Client Overview

The client is a ghee and dairy FMCG brand that needed an objective read on its digital shelf presence across India's leading q-commerce platforms. National sales numbers hid platform- and city-level realities — where competitors held more shelf, priced sharper, or stayed in stock while the client's SKUs went dark.

Actowiz built a recurring measurement of ghee digital shelf share across Blinkit, Zepto, BigBasket, and Amazon Fresh in 10 cities, capturing SKU count, ₹/litre pricing, and out-of-stock rate — the three signals that together define shelf share in q-commerce.

The Challenge

Four platforms, one view. Each platform structures listings and location logic differently, yet all had to roll up into one comparable shelf-share picture.
City- & pincode-level variation. Assortment, price, and availability vary by delivery zone, so 10 cities each needed geo-accurate capture.
₹/litre normalization. Ghee sells in varied pack sizes (500g, 1L, 2L, 5L), so raw prices had to be normalized to ₹/litre for fair comparison.
Out-of-stock as a shelf signal. An in-catalog but out-of-stock SKU is lost shelf — OOS% had to be captured, not just listing presence.
Competitor set breadth. Multiple national and regional ghee brands had to be tracked alongside the client for true share math.

The Solution by Actowiz Solutions

Actowiz deployed a geo-targeted, multi-platform extraction framework capturing ghee listings by city and pincode, then normalized everything into a shelf-share model.

Approach

Multi-platform pipelines. Independent extractors for Blinkit, Zepto, BigBasket, and Amazon Fresh feeding one schema.
Geo-targeted capture. Pincode/city context set per request so listings, price, and stock reflect each local shelf.
₹/litre normalization. Every SKU's price converted to ₹/litre for like-for-like comparison across pack sizes.
Shelf-share modelling. SKU count and share-of-listings computed per brand, per platform, per city.
OOS tracking. In-stock/out-of-stock captured per SKU to measure availability gaps.

Data Attributes Extracted

Attribute	Description
Brand / Company	Ghee brand and parent company
SKU / Product	Product title and pack size
Platform	Blinkit / Zepto / BigBasket / Amazon Fresh
City / Pincode	Location context
Price / ₹ per Litre	Listed price and normalized ₹/litre
SKU Count	Listings per brand per platform/city
Stock Status	In-stock / out-of-stock
Scrape Date	Cycle date

Implementation Workflow

Step	Phase	Description
1	Scope	Confirm brands, competitor set, platforms, and 10-city pincode map.
2	Geo Capture	Extract ghee listings per platform per pincode.
3	Normalization	₹/litre conversion and brand mapping.
4	Shelf-Share Model	Compute SKU count, share, and OOS% per cut.
5	QA	Validate pricing, geo accuracy, and dedup.
6	Delivery	Deliver dashboards/dataset per cycle.

Quality Assurance

Validation Check	Rule Applied
Pincode validation	Only approved city pincodes used
Price normalization	₹/litre computed correctly from pack size
Brand mapping	SKUs mapped to correct brand/company
OOS accuracy	Stock status matches source at capture
Deduplication	No duplicate SKU–platform–pincode rows
Schema conformance	Output matches agreed shelf-share schema

Results & Business Impact

Shelf-share clarity. Brand-by-brand digital shelf share visible per platform and city for the first time.
Price competitiveness. ₹/litre normalization exposed where the client was over- or under-priced locally.
Availability gaps. OOS% surfaced cities/platforms where the client was losing shelf to stock-outs.
Actionable geography. City-level cuts prioritized where to fix distribution and pricing.

Why the Client Chose Actowiz Solutions

Multi-platform in one schema. Four platforms, comparable output.
Geo-accurate. Pincode-level truth, not national averages.
Fair comparison. ₹/litre normalization built in.
Recurring. Repeatable cycle for trend tracking.

Project at a Glance

Metric	Value
Industry	FMCG • Dairy / Ghee
Region	India — 10 cities
Platforms	Blinkit, Zepto, BigBasket, Amazon Fresh
Signals	SKU count, ₹/litre, OOS%
Granularity	Platform × city × pincode
Cadence	Recurring
Output	Dashboard / structured dataset

Client Feedback

"We finally saw ghee shelf share the way shoppers actually experience it — by city, by platform, in ₹ per litre, with the out-of-stock gaps we'd been blind to. It changed where we put our push."

— Head of E-Commerce, Dairy FMCG Brand

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