Resources · Retail shelf research

Retail shelf research

Measured, not estimated. Each study publishes its method first and its findings when the window closes.

Retail shelf research from Actowiz measures what is actually listed, priced and buyable on Indian quick-commerce and marketplace platforms, at delivery-zone level. Each study page publishes its sampling frame, metric definitions and collection cadence before any figure appears, so that every number can be checked against the design that produced it.

Almost every category chart in circulation redraws a syndicated market-share estimate from a period that closed several quarters ago. These studies measure a different thing entirely: the shelf a shopper is actually shown, on a named platform, in a named pincode, at a recorded minute.

Studies: 8 in the current programme
Sampling frame: 30 delivery zones, 10 cities
Platforms: Quick commerce, marketplaces, fashion
Method: Published before findings
Status: Collection windows in progress

Current studies

Eight studies, one collection spine

Each study configures the same pipeline: a fixed panel of delivery zones, a canonical shelf record, published metric definitions and a stated cadence. Only the category and the extension fields change.

Study 01 · Dairy · Quick commerce

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.

Study 02 · Home care · Price index

Detergent price per wash across Indian platforms

The only detergent price comparison that means anything — and the dosage assumption it stands or falls on, stated up front.

Study 03 · Frozen · Availability

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.

Study 04 · Confectionery · Visibility

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.

Study 05 · Dairy · Assortment

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.

Study 06 · Fashion · Variant stock

Footwear size availability across Indian fashion platforms

A style listed as available can be unbuyable in every common size. Page-level stock cannot see that; variant-level stock can.

Study 07 · Confectionery · Feasibility pilot

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.

Study 08 · Beverages · Pilot study

The bottled water pack-size price curve

Near-identical product, unambiguous denominator, an eighty-fold pack range. The cleanest possible test of a price-normalisation pipeline — which is why this one runs first.

How it works

One pipeline, one panel, one record

The three diagrams below are the whole method. Everything else on the study pages is a configuration of them.

Figure — the shared collection spine

← swipe to see the full diagram

Eight bespoke studies would take a year. Eight configurations of one pipeline take a quarter. The stages that most often go wrong are normalisation and QA, which is why parser accuracy and successful-capture rate are published with every finding.

Figure — the sampling panel

← swipe to see the full diagram

Three pincodes per city, chosen to span a central, a mid-market and a peripheral catchment, because assortment varies more inside a city than between cities. The panel is fixed once. Changing it mid-programme breaks month-on-month comparison across all eight studies at the same time.

Figure — why the denominator decides the answer

← swipe to see the full diagram

These three states are kept separate on every record. Merging the grey box into the red one is the single most expensive error in this kind of data — it turns a category range decision into an apparent supply crisis and sends the wrong team after it.

The shared method

What every study on this page has in common

A fixed panel, set once

Thirty delivery zones across ten cities — three pincodes per city, chosen to span a central, a mid-market and a peripheral catchment, because assortment varies more inside a city than between cities. The panel is fixed before the first study and never changed mid-programme, since a stable panel is what makes month-on-month comparison possible at all.

One canonical record

Every study writes one row per SKU, per platform, per zone, per capture, into the same shape. Category-specific fields sit alongside it as extensions rather than in a separate table. That is what allows eight studies to run on one pipeline rather than eight.

Listed and in-stock stay separate

A product never ranged in a zone and a product ranged but unavailable are different facts. Merged into one availability number they produce figures that send teams after supply problems that do not exist. Every availability metric on these pages is computed against listed SKUs only, with the denominator printed next to the figure.

Normalisation is where the work is

Rupees per litre, per wash, per hundred grams. Pack-size parsing is deterministic first with a model-assisted fallback for the tail, and parser accuracy is measured against a labelled hold-out set and published. A price comparison built on unparsed pack sizes is comparing pack sizes, not prices.

Coverage gaps are published

Each study reports its successful-capture rate by platform and week. Where coverage fell below threshold in a week, that week is excluded and the exclusion is stated. A study reporting no gaps is a study that did not look for them.

Why the method is published before the findings

Every decision that determines whether a figure is worth anything is made before collection starts: which denominator, how many zones, what counts as out of stock, which queries, how pack sizes are normalised. Publishing that first means the eventual numbers can be checked against the design that produced them.

It also means nothing on these pages presents an estimate as a measurement. Where a figure has not been collected yet, the page says so.

Scope and limits

What these studies do not claim

They describe the online shelf, not the whole market. A large share of Indian grocery and confectionery volume still moves through general trade. These studies measure quick commerce and marketplaces, and every finding is phrased that way.

They are a panel, not a census. Thirty zones across ten cities skews metro by construction. Findings are stated as "across 30 sampled zones in 10 cities", never as national figures.

They are aggregates, published with a lag. We publish distributions, medians and spreads rather than live per-SKU competitor price tables, and we place a lag between the end of a collection window and publication. Granular real-time competitor pricing is a client deliverable under contract, not a public post.

They are corrected in public. Raw payloads are retained with capture IDs so any published number can be traced to the observations behind it. If a figure is wrong, tell us and the correction is published with its date.

Run any of these designs on your own category

Same sampling frame, same record, your brands and your priority zones. A pilot returns real data within 24 hours and production collection goes live in 5 to 10 business days after scoping.

Get a free pilot Book a 20-min scoping call

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