Why this study Who it's for Metrics The record Collection design What we publish Reading the numbers Mistakes FAQ

Retail shelf research · Study 01

Ghee digital shelf share across Indian quick commerce

Measuring the shelf a shopper actually sees, platform by platform and pincode by pincode — not a redrawn market-share estimate.

Ghee digital shelf share measures how much of the visible ghee assortment each brand occupies on a given platform in a given delivery zone, alongside normalised price per litre and out-of-stock rate. It is an observed shelf census, not a dispatch or consumption estimate, which is what conventional ghee brand-share charts are built from.

A ghee market-share pie tells you what a research house estimated about last year. It cannot tell you that a co-operative brand holds a quarter of listings in one city and almost none three states away, or that the same one-litre jar is priced ninety rupees apart on two apps delivering to the same street.

Category: Ghee — all pack formats
Platforms: Blinkit, Zepto, Instamart, BigBasket, Amazon, JioMart
Geography: 30 delivery zones, 10 cities
Cadence: Daily price, weekly assortment
Window: 30 days minimum
Findings: Added when the first window closes

Method · 2026-08-26Sampling frame, fields, cadence and metric definitions below are final. Findings for the ghee shelf are added when the first collection window (30 days) closes.

Why this study

Why a ghee market-share chart cannot answer the questions buyers actually have

Share is estimated. Shelf is observable.

Conventional ghee share figures are modelled from dispatch data, retail panels and consumption surveys. They are legitimate for sizing a market and close to useless for a category manager deciding which pack to push on which platform next month.

Digital shelf share is a different kind of number entirely. It counts what is actually listed and buyable, on a named platform, in a named delivery zone, at a recorded minute. Nothing is modelled. The trade-off is honest: it describes the online shelf rather than the whole market, and we say so.

Ghee is a three-tier category pretending to be one

The ghee shelf holds at least three distinct businesses. There is the commodity tier — large national and co-operative brands competing hard on price per litre. There is the regional co-operative tier, whose presence collapses the moment you cross a state line. And there is a fast-growing premium tier selling A2, bilona and grass-fed ghee at a multiple of the commodity price to a very different shopper.

A single share ranking flattens all three into one league table. Splitting the shelf by tier and reporting price per litre within each is where the actual competitive picture is.

Pack format is where the price story hides

Ghee sells in pouches, jars, bottles and tins from 200 ml to 5 litres, and several packs are labelled by weight rather than volume. Comparing pack prices across that ladder is meaningless.

Once everything is normalised to rupees per litre, two things usually become visible at once: the small-pack penalty within a brand, and the fact that the brand which looks cheapest on the shelf is frequently third or fourth cheapest per litre.

Platform private labels are the fastest-moving part of the shelf

Every major quick-commerce platform now runs its own dairy line, and those SKUs are ranged by the same company that controls placement. Tracking private-label shelf share month on month is one of the few genuinely new recurring numbers available in this category.

We treat private label as a brand tier of its own rather than folding it into the national tier, because the competitive dynamic it creates is different in kind.

Figure — the ghee pack ladder

← swipe to see the full diagram

The ghee shelf spans a twenty-five-fold pack range, and several packs are labelled by weight rather than volume. Any brand ranking built on pack price is ranking pack sizes. Everything is normalised to ₹ per litre before a single comparison is made.

Who this is for

Who reads this study, and what they do with it

Written from the questions we are actually asked when this category comes up on a scoping call. If none of these is you, the study is still readable — but the pilot offer at the bottom probably is not.

Head of E-commerce, dairy brand

Dairy / edible fats · national or co-operative

The problem

You know your national share. You do not know whether your 1 litre jar is listed in the same pincodes as the co-operative you compete with, or what it costs per litre next to theirs on the same app at the same hour.

What this study gives them

Digital shelf share by platform and zone, price per litre against the tier you actually compete in, and out-of-stock rate computed against listed SKUs only.

Metric that moves

Available Zone Coverage

Category Manager, quick commerce platform

Q-commerce · grocery buying

The problem

Your ghee range decisions are made per dark store cluster, but you see your own catalogue, not what the other five platforms range in the same catchment.

What this study gives them

Competitive assortment depth per zone, private-label share tracked month on month, and the price positions competing platforms hold on identical packs.

Metric that moves

Range gap per cluster

Consumer sector analyst

Equity research · India FMCG

The problem

Company disclosures give you quarterly volume commentary. They do not tell you whether premium A2 ghee is actually converting listings into shelf, or where the private labels are taking space.

What this study gives them

A dated, method-published series on shelf share by brand tier and the premium multiple, repeatable quarter on quarter against a frozen panel.

Metric that moves

Premium multiple, QoQ

Metric definitions

What we compute, and how

Definitions are fixed before collection begins. A metric defined after the data is in can be shaped to whatever conclusion is wanted, which is why these are published first.

Metric	How it is computed	Why it beats a market-share figure
Digital Shelf Share (DSS)	A brand's listed ghee SKUs divided by all listed ghee SKUs, per platform per zone, then averaged across zones with equal weight.`DSS = listed_skus(brand) / listed_skus(all)`	Counts the assortment a shopper is actually shown, rather than modelled volume moving through a supply chain.
Price per litre	Selling price divided by pack volume in litres, after pack-size parsing. Weight-labelled packs converted with a stated density constant.`unit_price = price / pack_litres`	Makes a 200 ml jar and a 5 litre tin comparable. Pack price alone ranks brands wrongly in almost every case.
Out-of-stock rate	Captures flagged out of stock divided by captures where the SKU was listed in that zone. Never divided by the full catalogue.`OOS% = oos_captures / listed_captures`	Separates a genuine supply failure from a SKU that was never ranged in that zone — two very different problems.
Available Zone Coverage (AZC)	Share of sampled zones where the brand had at least one SKU in stock, averaged across the window.`AZC = zones_with_stock / zones_sampled`	A brand can be listed everywhere and buyable nowhere. This is the online analogue of weighted distribution.
Premium multiple	Median price per litre of the premium tier divided by the median price per litre of the commodity tier, computed within each zone.`x = median(premium) / median(commodity)`	Prices the A2 and bilona positioning directly, which no share ranking can express.
Platform price spread	P90 minus P10 of price per litre for an identical pack across platforms, within the same pincode and the same capture hour.	Same product, same street, same hour. The cleanest price comparison the channel allows.

Data model

The record we collect

One row per SKU, per platform, per delivery zone, per capture. Study-specific fields sit alongside the standard shelf record rather than in a separate table.

ghee_shelf_record.json SCHEMA

{ "captured_at": "2026-09-14T11:04:22+05:30", "platform": "blinkit", "city": "Ahmedabad", "pincode": "380015", "zone_ring": "central", "category_path": ["Dairy & Breakfast", "Ghee & Oils", "Ghee"], "brand": "<resolved_brand>", "raw_brand_string": "<as_listed>", "brand_tier": "coop", // coop | national | premium | private_label "sku_title": "<as_listed>", "pack_value": 1.0, "pack_unit": "l", "pack_format": "jar", // pouch | jar | tin | bottle "fat_source": "cow", // cow | buffalo | mixed | unstated "claim_a2": false, "claim_bilona": false, "mrp": null, "selling_price": null, "unit_price_per_litre": null, // derived, not scraped "stock_status": "listed_in_stock", // listed_in_stock | listed_oos | not_listed "search_rank": null, "is_sponsored": false, "delivery_eta_min": null, "parser_confidence": null, "capture_id": "<uuid>" }

Schema shape — values null until the window runsJSON · CSV · Parquet

Values are shown as null because the window has not run. Derived fields are marked as such — they are computed from captured values, never scraped from a displayed badge.

Collection design

What we capture, and how often

Cadence follows a tiered design: highest frequency on the fields where a change alters a decision, lower on the ones that move slowly.

Field or signal	Why this study needs it	Capture frequency
Selling price by zone	Ghee pricing differs across platforms serving the same street, and promotional pricing moves within days.	Daily, 11:00 IST
MRP as displayed	Captured separately from selling price so discount depth is computed rather than trusted from a badge.	Daily
Pack value and unit	The denominator for price per litre. Parsed deterministically, with a model-assisted fallback for the tail.	Every capture
listed vs in_stock	Kept as separate states. Merging them makes a range decision look like a supply failure.	Daily
Brand, resolved and raw	Both are retained so a brand-map revision never breaks reproducibility of an already-published figure.	Every capture
Brand tier	Assigned by a published rule, not by judgement. The tier split is where the category's real structure shows.	Weekly review
Full category assortment	The universe is defined from category listing pages per zone, not from a brand seed list, so new entrants are caught.	Weekly
Search rank on a fixed query set	Listing share and search visibility rarely match, and the gap is a finding in itself.	Daily

Planned output

What will be published when the window closes

Decided before collection starts, so the sample can be designed to support them rather than reverse-engineered to fit whatever came back.

01Digital shelf share by platformBrand share of listed ghee SKUs on each of the six platforms, with the spread across zones shown rather than hidden in an average.

02Price per litre by brand tierDistribution of rupees per litre within the commodity, co-operative, premium and private-label tiers, with the premium multiple stated.

03The small-pack penaltyPrice per litre by pack size within the same brand, showing how much more a 200 ml jar costs per litre than a 5 litre tin.

04Out-of-stock rate by brand and platformComputed against listed SKUs only, with the denominator printed next to the figure.

05Platform price spread on identical packsThe rupee gap between the cheapest and dearest platform for the same pack, same pincode, same hour.

06Regional co-operative coverage mapAvailable Zone Coverage for co-operative brands inside and outside their home states.

07Private-label shelf sharePlatform own-brand share of listings, set up as a repeatable month-on-month tracker.

The figure this study is designed to produce

The rupee spread on an identical one-litre ghee pack across platforms delivering to the same pincode within the same hour.

Written before collection begins so the sample is designed to answer it. It will be filled from measured data or not at all.

How to read it

How to read the numbers when they land

When the findings land on this page, three qualifiers will sit next to every figure, and they are worth understanding before you read any of them.

The sample is a panel, not a census

Thirty delivery zones across ten cities is a deliberately chosen panel weighted toward metros. It is not India. Every figure on this page will be phrased as "across 30 sampled zones in 10 cities", never as "in India", because the narrower claim is the one the data actually supports.

The denominator is stated, always

Availability figures are computed against SKUs that were listed in that zone, never against the full catalogue. A SKU that was never ranged in a zone is a range decision, not a stock-out, and merging the two produces unavailability numbers that send supply chain teams after problems that do not exist.

Coverage gaps are published, not hidden

Each finding carries the successful-capture rate for its platform and week. Where a platform's coverage dropped below the threshold in a given week, that week is excluded and the exclusion is noted. A study that reports no gaps is a study that did not look for them.

If a figure on this page is ever wrong, tell us and we will correct it visibly with the date of the correction. Raw payloads are retained with capture IDs precisely so that any published number can be traced back to the observations behind it.

What goes wrong

Measurement mistakes that make ghee shelf data useless

Each of these produces a plausible-looking number that is wrong in a direction the reader cannot detect. They are listed because the design above exists specifically to avoid them.

Comparing pack prices instead of price per litre

The ghee shelf spans a twenty-five-fold pack range. Any ranking built on pack price is ranking pack sizes, not brands.

Collecting nationally instead of by zone

Quick-commerce catalogues are selected per dark store. A national ghee figure is an average of local markets that describes none of them.

Merging listed and out-of-stock into one availability number

A co-operative brand absent from a city is a range decision. The same brand listed and unavailable is a supply failure. One number cannot mean both.

Starting from a brand list

Defining the universe from a list of known brands guarantees you miss private labels and new premium entrants, which are usually where the movement is.

Trusting the discount badge

Displayed discount percentages are computed against a reference price that is not always the one that was live. Compute depth from captured MRP and captured selling price.

FAQ

Questions about this study

Including why there are no figures on it yet.

Because the collection window has not closed. Publishing figures now would mean presenting an estimate as a measurement, which is exactly the failure mode this study exists to avoid.

Findings will be appended here with their collection dates, sample size, successful-capture rate and known gaps.

No, and the distinction matters. Market share estimates the volume or value a brand moves through the whole market, usually modelled from dispatch and panel data.

Digital shelf share counts the visible assortment on named platforms in named delivery zones. It describes the online shelf only. It is directly observable, which market share is not.

They are converted to litres using a stated density constant, and the converted rows are flagged so anyone reading the findings can see how many figures depend on the conversion.

Where a pack gives neither a reliable weight nor a volume, the row is excluded from price-per-litre analysis rather than guessed at, and the exclusion count is published.

Yes, and that is usually more useful than our aggregate view. Same design, your brands, your priority cities and pincodes, your reporting cadence.

A pilot returns real data within 24 hours and production collection goes live in 5 to 10 business days after scoping.

Blinkit, Zepto, Swiggy Instamart, BigBasket, Amazon and JioMart. The first three carry the overwhelming majority of quick-commerce volume in India; the other three matter for basket-shop ghee purchases and larger pack sizes.

If your category concentrates elsewhere — regional grocery apps, a specific marketplace — the platform panel is a configuration, not a fixed part of the design.

Run this design on your own category

Our aggregate view is context. Your brands, your competitive set and your priority pincodes are what change a decision. A pilot returns real data within 24 hours; production collection goes live in 5 to 10 business days.

Get a free pilot Book a 20-min scoping call

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