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

Retail shelf research · Study 06

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.

Size availability measures stock at the variant level rather than the product level: for each listed footwear style, which sizes can actually be purchased. It surfaces broken size runs, which sizes deplete first, and the relationship between markdown depth and size availability — none of which is visible in a market-size chart.

This is the most technically demanding study in the programme, because it requires variant state rather than page state. A style can show as in stock while every size a normal adult wears is gone, and that gap is invisible to almost every published fashion dataset.

Category: Footwear — men's, women's, kids'
Platforms: Myntra, Ajio, Amazon
Geography: National catalogue, delivery-zone verified
Cadence: Daily variant sweep
Window: 30 days plus one full sale event
Findings: Added when the first window closes

Method · 2026-08-26Sampling frame, fields, cadence and metric definitions below are final. Findings for size availability are added when the first collection window (30 days plus one sale event) closes.

Why this study

Why footwear market size is the least useful number in the category

The buyable unit is a size, not a style

Nobody buys a style. They buy a size of a style, and if that size is gone the listing is a disappointment rather than an option. Every dataset that records stock at product level treats those two situations as identical.

Variant-level collection is what separates them, and it is where the entire value of this study sits. It is also why the engineering cost is real: size state is frequently rendered client-side and does not appear in the product listing payload.

Core sizes deplete first and asymmetrically

Size demand is not uniform. A relatively narrow band of sizes carries most of the volume, and those sizes empty first while the tails stay stocked, which is what produces a broken run.

Measuring which sizes go first, and how quickly, tells a merchandiser something about size-curve buying that no aggregate sell-through figure can. It is one of the few genuinely operational outputs available from public data in fashion.

The markdown trap

The relationship worth measuring is between discount depth and core-size availability. Deep markdowns frequently sit on styles whose size run has already broken — which is exactly why they were marked down.

Reported as a correlation across the catalogue, that becomes a concrete warning for anyone using discount depth as a competitive signal: the deepest discounts in footwear are disproportionately on stock nobody can buy.

A sale event is when the pattern is legible

Size runs break fastest during an end-of-season or festive sale, and then partially recover as replenishment lands. Capturing before, during and after one full sale event produces the clearest chart this programme will generate.

That is why the window is defined around an event rather than by a fixed number of days, and why the study sits late in the sequence — the collection has to be running before the event starts.

Figure — why page-level stock is not enough

← swipe to see the full diagram

Page-level datasets record one status for the whole style. This study records one row per style per size per capture, which is why it carries the highest engineering cost and the highest row count in the programme. The core band is fixed before collection so the broken-run rate cannot be tuned afterwards.

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.

Marketplace manager, footwear brand

Footwear · brand side

The problem

Your styles read as available on the platform dashboard while your core sizes are gone. You find out from returns and reviews, not from data.

What this study gives them

Size Availability Rate per size, broken-run rate per platform, and the gap between page-level status and what is actually buyable.

Metric that moves

Core-size SAR

Merchandising planner

Footwear · buying and planning

The problem

Your size curve is a buying assumption carried forward each season. You have never seen the observed depletion order across a live catalogue.

What this study gives them

Depletion order aggregated across the catalogue, plus recovery time after a sale event by platform.

Metric that moves

Depletion order, observed

Pricing and markdown lead

Footwear · commercial

The problem

You read competitor discount depth as aggression. In this category the deepest cuts sit disproportionately on stock nobody can buy.

What this study gives them

Discount depth plotted against core-size availability, so markdown signals are read with the size run attached.

Metric that moves

Markdown trap correlation

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
Size Availability Rate (SAR)	For each size, the number of styles where that size is in stock divided by the number of styles that offer that size at all.`SAR(s) = in_stock(s) / offered(s)`	Reports availability at the unit a shopper actually buys, rather than at the page level.
Broken-run rate	Share of styles listed as available that are missing at least one core size.`broken = missing_core / listed_available`	Quantifies the gap between a listing appearing available and being buyable by a typical shopper.
Depletion order	Rank order in which sizes transition from in stock to out of stock within a style, aggregated across the catalogue.	Turns a size curve from a buying assumption into an observed pattern.
Markdown trap correlation	Correlation between discount depth and the inverse of core-size availability, across the catalogue.`corr(discount_pct, −SAR_core)`	Tests whether the deepest discounts sit on the emptiest size runs. Discount depth alone cannot say.
Recovery time	Days from the end of a sale phase until core-size availability returns to its pre-event level, per platform.	Measures replenishment responsiveness, which no static market figure touches.
Size system normalisation	UK, EU and US size labels mapped to one internal scale, with the full mapping table published.	Without it, cross-platform size comparison is comparing labels rather than feet.

Data model

The record we collect

One row per style per size per capture. This is the highest row count in the programme and the reason the study needs a single-platform pilot before all three are committed.

footwear_variant_record.json SCHEMA

{ "captured_at": "2026-10-02T09:15:44+05:30", "platform": "myntra", "style_id": "<platform_product_id>", "brand": "<resolved_brand>", "gender": "men", "subcategory": "sneakers", "size_label_raw": "UK 8", "size_normalised": "uk_8", // via published mapping table "core_size": true, "size_offered": true, // style lists this size at all "size_in_stock": false, // this size is purchasable "style_page_status": "available", // what page-level stock would have said "mrp": null, "selling_price": null, "discount_pct": null, // derived "sale_phase": "during", // pre | during | post "capture_id": "<uuid>" }

Schema shape — values null until the window runsOne row per style × size × capture

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
Size-level stock state	The entire study. Requires variant state, which is frequently rendered client-side rather than exposed in the listing payload.	Daily
Page-level stock state	Captured alongside, deliberately, so the gap between what the page says and what is buyable can be quantified.	Daily
Size offered versus size in stock	A style that never offered UK 12 is not the same as one that offered it and sold out. Both states are retained.	Daily
Normalised size	UK, EU and US labels mapped to one scale via a published table. Without it, cross-platform comparison is meaningless.	Every capture
Core-size flag	Defined per gender from the published size band, fixed before collection so it cannot be tuned to the result.	Fixed at setup
MRP and selling price	Discount depth is computed from both, never taken from the displayed badge, and is central to the markdown-trap finding.	Daily
Sale phase	Pre, during and post, defined against the actual event calendar rather than by fixed dates.	Derived
Style identity	A stable key so a style can be followed through the event, which is what makes recovery time computable.	Every capture

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.

01Size Availability Rate by sizeAvailability across the full size range, showing exactly where the run breaks.

02The broken-run rateShare of styles listed as available that cannot fulfil at least one core size, by platform.

03Page-level versus variant-level availabilityThe gap between what the listing says and what is buyable — the study's central claim, quantified.

04Depletion orderThe observed sequence in which sizes empty, aggregated across the catalogue.

05The markdown trapDiscount depth plotted against core-size availability, testing whether the deepest cuts sit on the emptiest runs.

06The sale-event size curveCore-size availability before, during and after a full sale event — the single best chart in this programme.

07Recovery time by platformHow long each platform takes to restore core-size availability after the event closes.

The figure this study is designed to produce

The share of footwear styles listed as available that cannot actually fulfil the single most-ordered men's size.

Invisible to every page-level dataset. It requires variant state, which is why this study carries the highest engineering cost in the programme.

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 fashion stock 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.

Reading stock at page level

A style page reporting available while every core size is gone is the norm, not the exception. Page-level datasets systematically overstate buyable assortment.

Not distinguishing never-offered from sold-out

A style that never carried UK 12 and one that sold out of it are different facts. Merged, they make size curves look worse than they are.

Comparing raw size labels across platforms

UK, EU and US labels appear inconsistently. Without a normalisation table, cross-platform comparison compares labels rather than feet.

Defining core sizes after seeing the data

The core-size band must be fixed before collection. Setting it afterwards lets the broken-run rate be tuned to whatever conclusion is wanted.

Treating discount depth as a competitive signal on its own

In footwear, the deepest discounts disproportionately sit on broken size runs. Depth without size availability reads clearance as aggression.

FAQ

Questions about this study

Including why there are no figures on it yet.

Because it needs variant-level state. Size availability is frequently rendered client-side and does not appear in the listing payload, so it requires materially more engineering than a category price sweep.

It also produces the highest row count in the programme — one row per style per size per capture — which is why we pilot one platform before committing to three.

By a published size band per gender, fixed before collection begins. It is stated in the methodology note alongside the findings.

Fixing it in advance matters: a core-size band chosen after seeing the data can be set to produce almost any broken-run rate.

Because that is when size runs break fastest and then recover, and the before-during-after curve is far more informative than a flat 30-day baseline.

It does mean collection has to be live before the event starts, which is why this study sits late in the sequence but is scoped early.

Yes — it is an extension of variant-level catalogue collection rather than a new build. The same size normalisation and variant-state handling underpin both.

For a brand, the practical version of this study is your own styles: which sizes are breaking, on which platform, and how fast the retailer replenishes them.

Less than in quick commerce, since fashion platforms fulfil from central warehouses rather than dark stores. But serviceability and fulfilment centre allocation do vary.

We verify the session resolves to an intended delivery zone and report zone variance where it is material rather than assuming a single national catalogue.

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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