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

Retail shelf research · Study 03

Ice cream availability by pincode and time of day

The cold chain's real exam is not whether a tub is listed at 11am. It is whether it is buyable at 9pm in a peripheral pincode on a 41-degree day.

Ice cream availability is measured as the share of listed frozen SKUs that are in stock, captured three times daily across 30 delivery zones and joined to daily maximum temperature. It measures fulfilment rather than market size, which is what conventional ice cream market charts describe.

Ice cream is the one grocery category where the ten-minute promise and the physics are in open conflict. Freezer space in a dark store is scarce and expensive, demand is spiky and weather-driven, and none of that is visible in a market-size chart.

Category: Ice cream and frozen desserts
Platforms: Blinkit, Zepto, Instamart, BigBasket
Geography: 30 delivery zones, 10 cities
Cadence: Three captures daily — 08:00, 14:00, 21:00 IST
Window: 45 to 60 days, including a hot-weather stretch
Findings: Added when the first window closes

Method · 2026-08-26Sampling frame, fields, cadence and metric definitions below are final. Findings for availability by daypart are added when the first collection window (45 days) closes.

Why this study

Why an ice cream market-size chart tells you nothing operational

Market size is a growth story. Availability is an operations story.

Every ice cream infographic in circulation shows the same thing: a category growing at a healthy compound rate, with four or five familiar names ranked by share. It is true, it is dated, and it answers no question a supply chain or category team is actually asking.

The operational question is narrower and much harder to answer from secondary data: at what point in the day, in which kind of zone, does the frozen shelf stop being able to fulfil?

Time of day is the variable nobody publishes

Frozen availability is not a constant. It degrades through the day as demand draws down a fixed freezer allocation, and it recovers overnight on replenishment — unevenly, because replenishment schedules differ by platform and by dark store.

Capturing once a day records whichever point on that curve the crawler happened to hit. Three captures a day, at fixed times, records the curve itself. That single design decision is the difference between a chart and a finding.

Freezer capacity is a hard physical constraint

A dark store can add another shelf of biscuits far more easily than another freezer. That makes frozen the category where assortment and availability trade off most sharply, and where peripheral zones diverge most from central ones.

Reporting availability by zone ring — central, mid, peripheral — rather than by city average is how that divergence becomes visible. A city-level number averages it away.

Temperature is the covariate that makes it research

Joining daily maximum temperature from a public weather feed to zone-level availability converts a descriptive stock-out chart into something with an explanatory variable in it.

This is the part of the design that is genuinely hard to copy, and it is why the window has to run 45 to 60 days rather than two weeks. A single heatwave week is an anecdote. Six weeks with a temperature covariate is a measurement.

Figure — three fixed capture times

← swipe to see the full diagram

Frozen availability degrades through the day and recovers on replenishment. Fixed capture times are what make the curve reconstructable. The dashed line is the shape being tested, not a measurement — the actual curve is what the study exists to produce.

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.

Supply Chain lead, frozen

Ice cream · national or regional

The problem

Your fill rate to the platform looks fine. What happens to your SKUs inside a dark store freezer by 9pm is invisible to you, and that is where the sale is lost.

What this study gives them

Availability by daypart and zone ring, computed against listed SKUs, with the evening dip stated in percentage points per platform.

Metric that moves

21:00 availability rate

Category head, frozen desserts

Ice cream · brand marketing

The problem

You are buying visibility in a category where the binding constraint is freezer volume, not attention. You cannot tell which formats are being squeezed out.

What this study gives them

Availability split by tub, stick, cone, cup and cassata, so format-level squeeze shows up before it appears in offtake.

Metric that moves

Format availability gap

Q-commerce operations analyst

Platform side · fulfilment

The problem

Frozen is your hardest category and you benchmark it against yourself. You have no external view of how the other three platforms hold availability at the same hour in the same catchment.

What this study gives them

Cross-platform availability at fixed capture times, with delivery ETA drift as an independent stress signal.

Metric that moves

Evening dip vs peer set

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
Availability rate	In-stock captures divided by captures where the SKU was listed in that zone, computed separately per daypart.`avail = in_stock / listed`	Measures whether the category can fulfil, which market size cannot express at all.
Evening dip	Availability rate at the 11:00 capture minus availability rate at the 21:00 capture, per zone.`dip = avail(morning) - avail(night)`	Isolates intraday depletion, the pattern that a single daily snapshot destroys entirely.
Zone-ring gap	Difference in mean availability between central and peripheral zones within the same city.	Exposes fulfilment inequality inside a city, which any city-level average hides.
Format availability	Availability rate computed separately for tubs, sticks, cones, cups and cassata.	Large tubs occupy the most freezer volume and behave differently from impulse formats.
Temperature elasticity	Coefficient on daily maximum temperature in a model of availability with zone, platform and day-of-week controls.`avail ~ β·tmax + zone + platform + dow`	Attaches an explanatory variable to the stock-out pattern instead of just describing it.
Delivery promise drift	Change in stated delivery ETA for frozen SKUs between the coolest and hottest deciles of days in the window.	A second, independent signal of the same cold-chain stress the availability figure measures.

Data model

The record we collect

Three captures a day across 30 zones and four platforms is the highest-volume collection in this programme. The daypart field is what makes the volume worth it.

frozen_availability_record.json SCHEMA

{ "captured_at": "2026-09-14T21:02:09+05:30", "daypart": "night", // morning | afternoon | night "platform": "zepto", "city": "Hyderabad", "pincode": "500081", "zone_ring": "peripheral", // central | mid | peripheral "brand": "<resolved_brand>", "sku_title": "<as_listed>", "format": "tub", // tub | stick | cone | cup | cassata "pack_value": 700, "pack_unit": "ml", "weight_labelled": false, // excluded from ₹/100ml if true "selling_price": null, "stock_status": "listed_oos", // listed_in_stock | listed_oos | not_listed "delivery_eta_min": null, "tmax_c": null, // joined from public weather feed "day_of_week": "sunday", "capture_id": "<uuid>" }

Schema shape — values null until the window runs3 captures/day · 30 zones · 4 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
Stock status by daypart	The core measurement. Fixed capture times are what make the intraday curve reconstructable.	08:00, 14:00, 21:00 IST
listed vs not_listed	A tub absent from a peripheral dark store was never ranged there. That is a different finding from a stock-out and must not be merged with one.	Every capture
Zone ring classification	Central, mid and peripheral, fixed at panel setup. Fulfilment inequality inside a city is one of this study's main outputs.	Fixed at setup
Product format	Freezer volume per unit differs enormously between a family tub and a stick. Formats behave differently and are reported separately.	Weekly review
Delivery ETA	An independent signal of cold-chain stress that moves before availability does.	Every capture
Daily maximum temperature	Joined per city per day from a public weather feed. The covariate that turns description into explanation.	Daily join
Day of week	Weekend frozen demand differs materially from weekday. Left uncontrolled it contaminates the daypart finding.	Derived
Selling price	Secondary here, but needed to separate genuine stock-outs from SKUs pulled during a promotion reset.	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.

01The intraday availability curveAvailability rate at each of the three dayparts, by platform, with the evening dip stated in percentage points.

02Central versus peripheralThe fulfilment gap between zone rings inside the same city, which city averages hide completely.

03Availability by formatTubs, sticks, cones, cups and cassata reported separately, since freezer volume per unit differs enormously.

04Temperature and availabilityThe relationship between daily maximum temperature and zone-level availability, with the model specification published.

05Weekend versus weekdayWhether the evening dip deepens on weekends, controlled for temperature.

06Delivery promise under stressHow stated ETAs for frozen items move between the coolest and hottest days in the window.

07Platform comparisonWhich platforms hold frozen availability best at 21:00, which is the hardest moment in the day for this category.

The figure this study is designed to produce

How many times more likely a listed ice cream SKU is to be out of stock at 9pm than at 11am — and how much wider that gap runs in peripheral pincodes.

Answerable only with fixed-time captures across a zone panel. A daily snapshot cannot produce it 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 frozen availability data wrong

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.

One capture per day

Frozen availability moves within the day. A single daily capture records an arbitrary point on a curve and then gets read as if it were the level.

Merging not-listed with out-of-stock

A tub never ranged in a peripheral dark store is a range decision. Merged into a stock-out figure it manufactures a supply crisis that does not exist.

Reporting city averages

The whole finding in frozen is the spread between central and peripheral zones. A city average is the one number guaranteed to destroy it.

Running a two-week window

Frozen availability is weather-driven. A short window records whatever the weather did that fortnight and calls it a category pattern.

Ignoring format

A family tub and an impulse stick compete for the same freezer on completely different volume-per-unit terms. Blended availability describes neither.

FAQ

Questions about this study

Including why there are no figures on it yet.

Because frozen availability degrades through the day and recovers on replenishment. A single daily capture records one arbitrary point on that curve.

Three fixed capture times — 08:00, 14:00 and 21:00 — make the curve itself reconstructable, and the intraday shape is the most useful thing this study produces.

Because it converts a descriptive stock-out chart into something with an explanatory variable. Without it, the finding is 'availability varies'; with it, the finding relates availability to a measurable external driver.

The weather join uses public daily maximum temperature per city, and the model specification is published alongside the coefficient so anyone can check it.

Because a shorter window records whatever the weather did that fortnight. With a temperature covariate in the design, the window needs enough variation in temperature to support the estimate.

It also means the study starts collecting well before it publishes, which is why the design page goes up first.

The core panel is quick commerce, because that is where frozen fulfilment is genuinely constrained and where the ten-minute promise is being tested.

BigBasket is included as a scheduled-delivery comparison point. Marketplace frozen delivery is a different operating model and mixing it into the same availability figure would confuse two things.

Yes. Same three-daypart design, your SKU list, your priority cities, with zone rings set to match your own distribution planning rather than ours.

For a brand, the most actionable output is usually the peripheral-zone evening availability figure, since that is where listing gains most often fail to convert into buyable stock.

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.

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