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

Retail shelf research · Study 04

Chocolate listing share versus search visibility

Craft and D2C chocolate brands arrived through marketplaces. Whether they converted listings into first-page visibility is a different question, and a measurable one.

Chocolate shelf and search visibility compares each brand tier's share of listed SKUs against its share of top-20 search results on a fixed query set, split into organic and sponsored slots. The gap between listing share and search share is the finding; a conventional market-share chart contains neither number.

Every chocolate infographic says the same thing: two multinationals own the category. That has been true for years and will be true next year. The question worth measuring is whether the dozens of Indian bean-to-bar and D2C brands that arrived through marketplaces are actually being found.

Category: Chocolate — bars, boxes, bean-to-bar, gifting
Platforms: Amazon, Flipkart, BigBasket, Blinkit, Zepto, Instamart
Geography: 30 delivery zones, 10 cities
Cadence: Weekly assortment, daily search capture
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 shelf and search visibility are added when the first collection window (30 days) closes.

Why this study

Why the two-player chocolate chart stopped being interesting

Everyone already knows the answer it gives

The concentration of Indian chocolate volume in a small number of multinational brands is not in dispute and has not moved much. Redrawing it is not research; it is redecoration.

What has changed in the last few years is the arrival of a long tail of Indian craft, single-origin and D2C chocolate brands that reached shelves through marketplaces rather than through distribution. Whether that tail has visibility is unmeasured and is a live commercial question for every one of those founders.

Listing share and search share are different numbers

A brand can hold a meaningful share of listed SKUs in a category and appear almost nowhere in the top twenty results for the queries that actually drive discovery. The two numbers routinely diverge, and the divergence is the finding.

We compute both against the same universe in the same window, so the gap is a measurement rather than a comparison of two studies run at different times with different definitions.

The query set has to be frozen before collection

Share of search is only comparable if the queries are held constant. Around 25 queries are fixed before the window opens — category head terms, attribute queries such as vegan and sugar-free, cocoa-percentage queries, and gifting terms — and published in full with the findings.

Changing the query set mid-window, or choosing it after seeing early results, is how share-of-search figures get quietly manufactured. Publishing the list is the guard against that.

Figure — what a share-of-search capture looks like

← swipe to see the full diagram

Slot positions are illustrative of the capture shape, not of any observed result. The query set is frozen before the window opens and published with the findings — a query list chosen after seeing early results can be made to support almost any conclusion.

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.

Founder or Growth lead, craft chocolate

Confectionery · D2C / bean-to-bar

The problem

You are listed on four platforms and sales are flat. You cannot tell whether that is a discovery problem, a pricing problem, or simply that the top of every relevant query is bought.

What this study gives them

Your listing share against your share of search on a frozen query set, split organic and sponsored, per platform.

Metric that moves

Visibility gap

Retail media planner

Confectionery · agency or in-house

The problem

You are asked to justify sponsored spend in a category where you have no independent read on how paid the first page already is.

What this study gives them

Sponsored share of top-20 slots per query, so budget conversations start from a measured baseline rather than a platform-supplied one.

Metric that moves

Category buyer, marketplace

Q-commerce / marketplace · confectionery

The problem

The craft tail is growing and you range it by instinct. You have no view of its price architecture against cocoa percentage.

What this study gives them

Price per 100 g by cocoa percentage within each tier, and which tiers hold visibility on claim-led queries like vegan and sugar-free.

Metric that moves

Cocoa-point premium

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
Listing share	A brand tier's listed chocolate SKUs divided by all listed chocolate SKUs, per platform per zone.`DSS = listed(tier) / listed(all)`	Counts shelf presence directly instead of inferring it from volume estimates.
Share of search	Slots held in the top 20 results across the fixed query set, divided by total available slots, split organic and sponsored.`SoS = slots(tier) / (20 × queries)`	Measures discoverability, which listing counts alone cannot express.
Visibility gap	Share of search minus listing share, per tier. A negative value means listed but not found.`gap = SoS - DSS`	The single number this study exists to produce. No market-share chart contains anything like it.
Sponsored share of first page	Share of top-20 slots on each query that are paid placements.	Distinguishes bought visibility from earned visibility, which a blended ranking never does.
Price per 100g by cocoa percentage	Selling price normalised to 100 grams, regressed against parsed cocoa percentage within each tier.`₹/100g ~ cocoa_pct, by tier`	Prices the craft positioning against a measurable product attribute rather than a brand label.
Tier assignment rule	Brands classified mass, premium mass, craft or D2C, and import by a published rule; the full assignment list ships with the findings.	Makes the segmentation auditable instead of a matter of the analyst's judgement.

Data model

The record we collect

Two record types share one product identity: an assortment record per SKU per zone, and a search record per query per slot per platform.

chocolate_visibility_record.json SCHEMA

{ "captured_at": "2026-09-14T11:12:55+05:30", "record_type": "search_slot", // assortment | search_slot "platform": "amazon_in", "pincode": "400058", "query": "dark chocolate", // from the frozen 25-query set "slot_position": 4, "rank_type": "sponsored", // organic | sponsored "brand": "<resolved_brand>", "tier": "craft_d2c", // mass | premium_mass | craft_d2c | import "sku_title": "<as_listed>", "pack_grams": 80, "cocoa_pct": 70, "claims": ["single_origin", "vegan"], "is_multipack": false, "is_gift_set": false, // excluded from ₹/100g "selling_price": null, "price_per_100g": null, // derived "capture_id": "<uuid>" }

Schema shape — values null until the window runsQuery set frozen before collection

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
Full category assortment by zone	The listing-share denominator. Defined from category pages, not from a brand list, so new craft entrants are caught.	Weekly
Top 20 search slots per query	The search-share numerator. The query set is fixed before the window and published with the findings.	Daily
Organic or sponsored classification	Bought visibility and earned visibility are different findings. Blending them favours whoever spends most.	Every search capture
Cocoa percentage	Parsed from title and attributes. The attribute that lets craft pricing be assessed against something measurable.	Weekly review
Brand tier	Assigned by a published rule with the full brand-to-tier list shipped alongside the findings.	Weekly review
Pack grams and multipack flag	The ₹/100g denominator. Gift sets with mixed weights are excluded rather than estimated.	Every capture
Product claims	Vegan, sugar-free, single-origin. These drive several of the fixed queries and segment the premium tier.	Weekly review
Selling price	Needed for price per 100 grams and for the cocoa-percentage premium.	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.

01Listing share by brand tierMass, premium mass, craft and D2C, and import, per platform, with the tier assignment list published in full.

02Share of search by tierTop-20 slot share across the fixed query set, reported organic and sponsored separately.

03The visibility gapSearch share minus listing share per tier — the core output, showing who is listed but not found.

04How paid the first page isShare of top-20 slots that are sponsored, by query, for the highest-intent category terms.

05Price per 100g by cocoa percentageThe cocoa-point premium within each tier, showing what craft positioning actually costs a shopper.

06Claim-led query performanceWhich tiers hold visibility on vegan, sugar-free and single-origin queries versus category head terms.

07Platform differencesWhether craft visibility is materially better on one platform than another, which is directly actionable for a D2C founder.

The figure this study is designed to produce

The share of chocolate listings held by craft and D2C brands, against their share of first-page search slots — and how much of that first page is paid.

Two numbers that are almost never measured against each other in the same window on the same universe.

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 visibility data meaningless

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.

Choosing the query set after seeing results

Share of search is only comparable across a frozen query list. Selecting queries once early results are visible manufactures whatever conclusion is wanted.

Blending sponsored and organic slots

A bought slot and an earned slot are different findings. Merging them systematically overstates organic discovery for the biggest spender.

Segmenting brands by judgement

Craft, premium and mass are useful tiers only if the assignment rule is published and the full brand list ships with the results.

Including gift sets in price per 100g

Mixed-weight gift boxes have no meaningful gram denominator. Estimating one contaminates the entire price distribution.

Comparing search share and listing share from different windows

The gap is only a measurement if both numbers come from the same universe in the same period.

FAQ

Questions about this study

Including why there are no figures on it yet.

Around 25 queries fixed before the window opens, covering category head terms, attribute queries such as vegan and sugar-free, cocoa-percentage queries and gifting terms.

The full list is published with the findings. Freezing and publishing it is what stops share-of-search figures from being quietly engineered.

By a published rule rather than by judgement, and the complete brand-to-tier assignment ships alongside the findings so anyone can disagree with a specific call.

Any segmentation of this kind involves borderline cases. Publishing the assignment list is the only honest way to handle them.

Because in a category where two brands have very large media budgets, a blended first-page share mostly measures spend.

Separating the two lets a craft brand see whether its problem is discoverability or simply that the top of the page is bought — which points at completely different responses.

Less than on quick commerce, but not not at all — availability and fulfilment signals feed ranking, so results are not identical across zones.

We capture search per zone in the panel rather than assuming a national result set, and report zone variance where it is material.

Yes, and for a D2C brand it is usually the single most useful measurement available: your listing share, your search share, the gap, and how much of the page above you is paid.

Same design, your query set, your priority platforms, delivered on your reporting cadence.

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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