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

Retail shelf research · Study 05

Cheese assortment depth by format and zone

Range, not rank. A brand with one hero SKU and a brand with a full format range are not comparable, and a top-twenty list treats them as if they were.

Cheese assortment depth measures how many distinct SKUs and how many product formats each brand offers, per platform per delivery zone. It captures range and geographic coverage — particularly the collapse in regional co-operative presence outside their home states — which a brand ranking cannot express.

Cheese is a format war disguised as a brand category. Slices, cubes, blocks, spreads, mozzarella, processed and a growing artisanal tier all compete for the same chiller, and a list of the top twenty brands flattens every bit of that into one column.

Category: Cheese — all formats including artisanal
Platforms: Blinkit, Zepto, Instamart, BigBasket, Amazon, JioMart
Geography: 30 delivery zones, 10 cities
Cadence: Weekly assortment census
Window: 30 days
Findings: Added when the first window closes

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

Why this study

Why a top-twenty cheese brand list is the wrong shape for this category

A ranking compares brands that are not doing the same thing

One brand on a cheese shelf may be a national player carrying eight formats in thirty SKUs. Another may be a regional co-operative with three SKUs that outsell everything else in its home state. A third may be an importer with two artisanal lines in four metros.

Ranking those by a single share figure produces a table where every row means something different. Depth and coverage, reported separately, keep them distinguishable.

The format matrix is the honest visual for this category

The clearest way to describe a cheese shelf is a grid: brands down the side, formats across the top, each cell filled or empty. Every cell is a yes-or-no fact about whether a brand offers that format in that zone, which makes it very hard to dispute and very easy to read.

It also surfaces the thing rankings hide — that most brands are strong in one or two formats and absent from the rest, and that the pattern of absence differs by platform.

The home-state effect is the finding nobody publishes

Regional dairy co-operatives are major players inside their home states and close to invisible outside them. That boundary is sharp, it is measurable, and it does not appear in any national ranking.

By assigning each co-operative a home state and flagging whether each sampled zone falls inside it, the drop-off becomes a number: SKUs listed at home versus SKUs listed away.

Slice counts are a parsing trap

A great many cheese SKUs are sold by slice count with no stated weight, which breaks any attempt at price per 100 grams. Estimating a slice weight quietly is how price distributions in this category get contaminated.

Rows without a reliable weight are excluded from price normalisation and the exclusion count is published. Where a slice-to-gram assumption is used, it is stated and the affected rows are flagged.

Figure — the shape of the output

← swipe to see the full diagram

The clearest single visual this study produces. Every cell resolves to a yes-or-no fact — does this brand offer this format in this zone — which makes it very hard to dispute and very quick to read. It also surfaces what rankings hide: most brands are strong in one or two formats and absent from the rest.

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.

Range planner, dairy brand

Cheese · national or import

The problem

You know your SKU count. You do not know which of your formats are missing in which zones on which platform — and whether that is your decision or the retailer's.

What this study gives them

Format coverage matrix per zone plus platform range gaps, showing where the same brand is ranged deep on one app and thin on another.

Metric that moves

Format coverage %

Marketing head, regional dairy co-operative

Cheese · state co-operative

The problem

Your position at home is strong and you have no measurement of how sharply it falls away outside your state, or which cities are worth entering.

What this study gives them

Home advantage quantified as a multiple, plus zone presence by city so expansion targets are chosen from data rather than adjacency.

Metric that moves

Home vs away SKU ratio

Assortment analyst, retail platform

Q-commerce / grocery · chilled buying

The problem

Chiller space is fixed and you are ranging on brand reputation rather than on observed format gaps in your own catchments.

What this study gives them

Assortment Depth Index against the category median per zone, with the formats no brand is covering in that catchment surfaced explicitly.

Metric that moves

ADI vs category median

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
Assortment Depth Index (ADI)	Distinct SKUs for a brand in a zone divided by the category median distinct SKUs per brand in that zone.`ADI = skus(brand,zone) / median_skus(zone)`	Separates a brand with a full range from one with a single hero pack. A share ranking cannot.
Format coverage	Number of formats a brand offers in a zone divided by the total number of formats present in the category.`coverage = formats(brand) / formats(all)`	Describes range shape rather than range size, which is what a chiller decision actually turns on.
Home advantage	Mean ADI in zones inside a co-operative's home state minus mean ADI in zones outside it.`adv = mean(ADI\|home) - mean(ADI\|away)`	Quantifies the regional boundary that national rankings erase entirely.
Zone presence	Share of sampled zones where the brand has at least one listed SKU.	Distinguishes a narrow national brand from a deep regional one, which look identical in a ranking.
Platform range gap	Difference in a brand's distinct SKU count between the platform that ranges it deepest and the one that ranges it shallowest, in the same zone.	Shows where a brand's range is being cut by the retailer rather than by the brand.
Price per 100g by format	Selling price normalised to 100 grams within each format, with slice-count rows excluded or flagged.`₹/100g = price / (grams/100)`	Makes processed, mozzarella and artisanal comparable on the one denominator that holds across them.

Data model

The record we collect

An assortment census rather than a price tracker. The weekly cadence is sufficient because range changes slowly, which also keeps this the cheapest study in the programme to run.

cheese_assortment_record.json SCHEMA

{ "captured_at": "2026-09-14T10:31:07+05:30", "platform": "bigbasket", "city": "Bengaluru", "pincode": "560034", "brand": "<resolved_brand>", "brand_origin": "regional_coop", // national | regional_coop | import | private_label "home_state": "<state_or_null>", "is_home_zone": true, // derived from pincode ∈ home_state "cheese_format": "slice", // slice | cube | block | spread | mozzarella | processed | artisanal "sku_title": "<as_listed>", "pack_grams": null, "slice_count": 10, "weight_stated": false, // false → excluded from ₹/100g "selling_price": null, "price_per_100g": null, // derived, null where weight not stated "stock_status": "listed_in_stock", "distinct_sku_key": "<canonical>", "capture_id": "<uuid>" }

Schema shape — values null until the window runsWeekly census · 30 zones · 6 platforms

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
Distinct SKU count per brand per zone	The ADI numerator. Canonical SKU keys prevent the same product listed twice from inflating depth.	Weekly
Cheese format	The dimension that makes this category legible. Assigned by a published rule from title and attributes.	Weekly review
Brand origin and home state	National, regional co-operative, import or private label, with a home state assigned to co-operatives.	Fixed at setup
Home-zone flag	Derived from whether the sampled pincode falls inside the brand's home state. Drives the home-advantage figure.	Derived
Pack grams and slice count	Both retained. Slice-count-only rows are excluded from price normalisation rather than estimated.	Weekly
Listing status per zone	Presence and absence are both data here. Absence across a whole city is the regional finding.	Weekly
Selling price	Secondary to range in this study, but needed for price per 100 grams within format.	Weekly
Artisanal and import tier	Small but growing, and concentrated in a few metros. Tracked separately rather than folded into the national tier.	Weekly

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.

01The format coverage matrixBrands down the side, formats across the top, filled or empty per zone. The clearest single visual this programme produces.

02Assortment Depth Index by brandRange depth relative to the category median, per platform, showing who carries a range and who carries a hero pack.

03The home-state effectCo-operative SKU counts inside versus outside their home states, stated as a multiple.

04Zone presence by brand originHow national, regional, import and private-label tiers differ in geographic reach.

05Where regional cheese is unavailableThe sampled cities in which regional co-operative cheese is effectively absent from every platform.

06Platform range gapsBrands whose range is deep on one platform and thin on another in the same zone — a retailer decision, not a brand one.

07Price per 100g by formatWith the count of rows excluded for missing weight published alongside.

The figure this study is designed to produce

How many cheese SKUs a regional dairy co-operative lists inside its home state, against how many it lists outside it.

A boundary every national brand ranking erases, expressed as a multiple.

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 assortment data unreliable

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.

Counting listings instead of distinct SKUs

The same product listed twice, or a variant page counted as a product, inflates depth for whoever has the messiest catalogue.

Estimating weight from slice count

A large share of cheese SKUs state slices and not grams. Quietly assuming a slice weight contaminates the whole price distribution.

Ignoring format

Depth without format shape is misleading. Fifteen SKUs across one format is a different business from fifteen across six.

Averaging co-operatives into a national figure

The entire regional finding is the home-versus-away gap. A national average is the one calculation guaranteed to erase it.

Treating absence as missing data

A brand not listed in a zone is a measurement, not a gap. Dropping those rows makes every brand look nationally present.

FAQ

Questions about this study

Including why there are no figures on it yet.

Because a ranking places brands doing entirely different things in one column. A national player with eight formats and a regional co-operative with three high-volume SKUs are not comparable on a single share figure.

Depth and coverage, reported separately, keep the difference visible.

They are captured with the slice count retained, and excluded from price-per-100-gram analysis where no weight is stated. The exclusion count is published with the findings.

Where a slice-to-gram assumption is applied, it is stated explicitly and the affected rows are flagged so anyone can recompute without it.

For dairy co-operatives it follows the federation's own state, fixed at panel setup and published with the findings. National brands and imports carry no home state and are excluded from the home-advantage calculation.

Borderline cases are listed rather than resolved silently.

Because range changes slowly. Assortment decisions play out over weeks, not hours, so daily capture would multiply cost without adding resolution.

This makes it the least expensive study in the programme to run, which is part of why it sits early in the sequence.

Yes. The most common version is a range-gap analysis: which of your formats are ranged in which zones on which platforms, and where a competitor carries a format you do not.

Same weekly census design, your brand list, your priority zones.

Run this design on your own category

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