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

Retail shelf research · Study 07

Impulse confectionery on quick commerce

Hard-boiled candy is the purest impulse category in Indian retail. Quick commerce has no counter to put it on. This study asks what replaced it.

Impulse confectionery measurement counts where low-price confectionery surfaces on quick commerce — category rails, recommendation modules and price-point collections — and at what price points it survives. It is restricted to publicly rendered surfaces, and that restriction is stated because it defines what the study can and cannot conclude.

Candy is bought at a counter, for change, without a decision. Quick commerce has no counter. Whether the impulse category has found a digital equivalent or has simply lost its shelf is genuinely unmeasured, and it is the most novel question in this programme.

Category: Confectionery — hard-boiled, toffee, mints, gum
Platforms: Blinkit, Zepto, Instamart, BigBasket
Geography: 30 delivery zones, 10 cities
Cadence: Weekly
Window: Two-week feasibility pilot, then 30 days if viable
Findings: After the feasibility pilot — scope may narrow

Method · 2026-08-26Sampling frame, fields, cadence and metric definitions below are final. Findings for impulse-surface presence are added when the first collection window (a two-week feasibility pilot) closes.

ScopePublicly rendered surfaces only. Cart and checkout modules require an authenticated session and are excluded from every metric on this page.

Why this study

Why this question has no existing answer

The physical impulse shelf has no direct digital equivalent

In a kirana store or a supermarket, impulse confectionery works because of position: at the counter, at eye level, in the last three seconds of a transaction. None of those levers exist in an app in the same form.

Quick commerce has analogues — browse rails, recommendation modules, low-price collections — but whether they carry impulse products with anything like the same density is not something anyone has counted.

The ten-rupee price point may not survive online

A large share of Indian confectionery volume sits at price points that are difficult to make work in a delivery model with a minimum basket and a delivery fee. A ten-rupee SKU is not a viable order.

Counting how many sub-fifty-rupee confectionery SKUs are actually listed per zone, and where the distribution's floor sits, is a direct measurement of whether the category's core price architecture made the transition.

Rail placement is increasingly bought

Where impulse products do surface, the question is whether they earned the slot or bought it. Sponsored classification on every captured slot separates the two, and in a low-margin category the answer has real commercial weight.

The scope boundary is part of the finding

Being explicit that authenticated surfaces are out of scope is not a weakness of this study; it is what makes its conclusions safe to quote. A finding of the form "across publicly rendered surfaces, impulse confectionery occupies X% of slots" is precise and defensible.

A finding that quietly implied full coverage of every surface a shopper sees would be neither.

Figure — what this study can and cannot see

← swipe to see the full diagram

Part of the impulse shelf sits behind an authenticated session. Drawing that boundary in public is what makes the eventual finding safe to quote: this study can say what share of publicly rendered impulse slots confectionery holds, and it will not claim to know what a logged-in shopper sees in their cart.

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.

Channel head, confectionery

Confectionery · distribution

The problem

General trade carries your volume at price points that may not exist online at all. Nobody has counted whether they do.

What this study gives them

Price-point survival per zone and where the price floor actually sits, restricted to publicly rendered surfaces and stated as such.

Metric that moves

Sub-₹20 SKU share

Brand manager, impulse SKUs

Confectionery · marketing

The problem

The counter that carried your category does not exist in an app, and you have no measurement of what replaced it or whether you are on it.

What this study gives them

Impulse slot share by brand across category rails, recommendation modules and price collections, with slot position retained.

Metric that moves

Impulse slot share

Q-commerce category buyer

Platform side · confectionery

The problem

Low-price confectionery is hard to make work in a delivery basket and you are ranging it without a competitive read.

What this study gives them

How competing platforms range the sub-₹50 tier by zone, and how much of that visible shelf is sponsored.

Metric that moves

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
Impulse slot share	Confectionery slots on publicly rendered impulse surfaces divided by all slots on those surfaces, per platform per zone.`share = slots(conf) / slots(all)`	Measures presence on the digital equivalent of the counter, which no category report attempts.
Price-point survival	Share of listed confectionery SKUs at or below defined price thresholds, per zone.`survival = skus(≤ ₹20) / skus(category)`	Tests directly whether the price architecture that carries the category offline exists online.
Price floor	Lowest selling price at which any confectionery SKU is listed in a zone, and how far above it the bulk of the distribution sits.	Shows where the delivery model's economics cut the category off.
Price per 100g at low price points	Selling price normalised to 100 grams for SKUs below the low price threshold, excluding piece-count packs with no stated weight.`₹/100g = price / (grams/100)`	Reveals whether small online packs carry a per-gram penalty relative to larger formats.
Sponsored share of impulse slots	Share of captured impulse-surface slots that are paid placements.	Distinguishes bought presence from earned presence in a category with thin margins.
Surface coverage disclosure	Count and type of surfaces observed, and explicit listing of surfaces excluded because they require authentication.	Defines exactly what the other five metrics do and do not cover. Published with every figure.

Data model

The record we collect

The public_render flag is the most important field on this record. Rows where it is false are excluded from analysis and counted in the coverage disclosure.

impulse_surface_record.json SCHEMA

{ "captured_at": "2026-09-14T18:44:02+05:30", "platform": "blinkit", "pincode": "110016", "surface": "category_rail", // category_rail | reco_module | price_collection | search "surface_name": "<as_displayed>", "public_render": true, // false → excluded from analysis "slot_position": 7, "is_sponsored": false, "brand": "<resolved_brand>", "sku_title": "<as_listed>", "conf_type": "hard_boiled", // hard_boiled | toffee | mint | gum | chocolate "selling_price": null, "price_band": "p20", // p10 | p20 | p50 | above "pack_grams": null, "piece_count": null, "weight_stated": false, // false → excluded from ₹/100g "capture_id": "<uuid>" }

Schema shape — values null until the pilot runsPublicly rendered surfaces only

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
Surface type and name	Category rails, recommendation modules on public product pages, and price-point collections. Recorded as displayed.	Weekly
Public render flag	The scope boundary as a field. Anything requiring authentication is excluded from analysis and counted in the disclosure.	Every capture
Slot position	Position within the surface. Presence at slot 2 and slot 40 are not the same finding.	Weekly
Sponsored classification	Bought presence and earned presence are different results, and matter more in a thin-margin category.	Every capture
Selling price and price band	The direct test of whether the category's low price points survived the move online.	Weekly
Confectionery type	Hard-boiled, toffee, mint, gum. Hard-boiled is the purest impulse case and is reported separately.	Weekly review
Pack grams and piece count	Both retained. Piece-count packs with no stated weight are excluded from per-gram analysis rather than estimated.	Weekly
Full confectionery assortment per zone	The denominator for price-point survival. Collected from category pages, not from rails.	Weekly

Planned output

What will be published, if the pilot supports it

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

01Surface coverage disclosurePublished first, not last: which impulse surfaces were observable and which were excluded for requiring authentication.

02Impulse slot share by brandShare of publicly rendered impulse-surface slots held by each confectionery brand, per platform.

03Price-point survivalShare of confectionery SKUs at or below the low price thresholds, and where the price floor actually sits by zone.

04Sponsored share of impulse slotsHow much of the visible impulse shelf is paid placement.

05Price per 100g at low price pointsWhether small online packs carry a per-gram penalty against larger formats.

06Hard-boiled candy specificallyReported separately from chocolate and gum, since it is the category with the least obvious digital home.

07The go or no-go noteIf the pilot shows public impulse surfaces are too thin, that conclusion is published here and the study folds into the chocolate study.

The figure this study is designed to produce

The share of confectionery SKUs on quick commerce sitting at or below the price point that carries the category in kirana stores.

Restricted to publicly rendered surfaces, and the findings will state that boundary rather than implying full coverage.

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 this study is designed to avoid

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.

Implying coverage of surfaces you cannot observe

Cart and checkout modules that need a session are out of scope. A study that does not say so lets readers assume a completeness it does not have.

Counting rail slots without positions

Presence in a rail is nearly meaningless without position. Slot 2 and slot 40 are different results.

Estimating weight from piece counts

Many low-price confectionery packs state pieces and not grams. Assuming a piece weight contaminates the per-gram distribution.

Using the rail as the denominator for price-point survival

Survival must be computed against the full listed category, not against the SKUs that already made it onto a rail.

Publishing a thin pilot as a full study

If public impulse surfaces turn out to be sparse, that is the finding. Padding it into a standalone report would be the mistake.

FAQ

Questions about this study

Including why there are no figures on it yet.

Because part of the impulse shelf on quick commerce sits behind an authenticated session, and we restrict collection to publicly rendered surfaces.

That means the study can say what share of publicly rendered impulse slots confectionery holds. It cannot say what a logged-in shopper sees in their cart, and it will not claim to.

Then that is the finding, and it gets published here. The study folds into the chocolate study as a section rather than becoming a thin standalone page.

Cutting one study of eight after a pilot is a healthy outcome. Discovering in month five that it never worked is not.

Because it is the purest impulse case in Indian retail: bought at a counter, for change, with no decision. It is the category with the least obvious digital equivalent, which is exactly what makes it worth measuring.

Chocolate, gum and mints are captured too, but reported separately, since each has a different relationship to planned purchase.

Yes, and in a fairly direct way: it tells you whether your low price-point SKUs are listed at all in the zones you care about, where you appear in browse surfaces, and how much of that surface is paid.

For most confectionery brands the more urgent question is the first one, and it is answerable within days rather than weeks.

The chocolate study measures listing share against search visibility — a discovery question for a considered purchase. This one measures browse-surface presence and price-point survival — a distribution question for an unconsidered one.

They share a schema and a zone panel, which is why folding one into the other remains a clean option.

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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