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

Retail shelf research · Study 02

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.

Price per wash converts detergent pack prices into a comparable unit by dividing selling price by the number of washes a pack delivers at its stated dosage. It is the only way to compare powder, liquid and bar formats, and it depends entirely on a dosage assumption, which this study publishes rather than buries.

Detergent is sold in three formats that cannot be compared by pack price, by weight, or by volume. Powder, liquid and bar only become comparable at the level of a single wash — and the moment you convert to cost per wash, the format that looks cheapest on the shelf usually stops being the cheapest.

Category: Laundry detergent — powder, liquid, bar, pods
Platforms: Blinkit, Zepto, Instamart, BigBasket, Amazon, JioMart
Geography: 30 delivery zones, 10 cities
Cadence: Daily price capture
Window: 45 days, then quarterly re-runs
Findings: Added when the first window closes

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

Why this study

Why a top-ten detergent brand ranking answers nothing

Three formats, three denominators, no comparison

A powder pack is priced per kilogram, a liquid bottle per litre, a bar per unit. A brand ranking places them in one league table without ever reconciling that, which means the ranking is really measuring how the market splits across formats rather than how the brands compete.

Cost per wash is the reconciliation. It is also the number a household actually experiences, which is why it travels further than any share figure.

The dosage assumption is the whole study

Converting a pack to washes requires knowing how much detergent one wash uses, and that varies by format, by machine type and by brand instruction. This is the study's single point of fragility and we treat it as such.

Where the pack's own usage instructions are available in the listing's attributes or description, we take the dosage from there and mark the row pack_stated. Where they are not, we apply a documented category constant per format and mark the row category_constant. Every published figure states what share of its rows were assumed rather than stated.

A vendor publishing a single confident cost-per-wash number without disclosing this has either solved a problem nobody else has or has quietly picked a constant and hoped.

Top-load and front-load are different products at the same price

Front-load machines require low-foam formulations dosed differently from top-load powders. A cost-per-wash figure that mixes them is averaging two categories.

Machine type is therefore a dimension on every record, and the findings will report front-load and top-load separately rather than blending them into one national number.

Pack size drifts while price holds

The most interesting recurring finding in home care is rarely a price change. It is a pack getting smaller while the price stays where it was — a real change in cost per wash that never appears as a price movement.

Because this study runs quarterly against a frozen SKU panel, that drift is visible as a time series rather than as an anecdote. It requires holding the pack-size history per SKU, which is why pack_history_id is on the record from day one.

Figure — three formats, one denominator

← swipe to see the full diagram

Powder, liquid and bar are not comparable by pack, by weight or by volume. They only become comparable at the level of a single wash. That conversion needs a dosage assumption, and the assumption is the study's one point of fragility — so it is a field on every row, not a footnote.

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.

Revenue Growth Management lead

Home care · multinational or Indian HPC

The problem

Pack-price benchmarking tells you nothing across three formats. You need cost per wash, and every internal attempt at it has died on the dosage assumption.

What this study gives them

Cost per wash with dosage provenance on every row, the liquid premium quantified, and front-load reported separately from top-load.

Metric that moves

₹ per wash vs competitor

Pricing Manager, D2C detergent brand

Home care · challenger / D2C

The problem

You compete on convenience and cannot tell whether shoppers are paying your premium knowingly, or whether the incumbents have quietly closed the per-wash gap.

What this study gives them

Your cost per wash against the category distribution per platform, plus shrinkflation flags when a competitor's pack shrinks at constant MRP.

Metric that moves

Liquid premium, tracked

Trade marketing, format transition

Home care · channel strategy

The problem

Liquid's share of volume is reported to you. Liquid's share of the visible shelf is not, and the two move at different speeds.

What this study gives them

Format shelf share by platform and zone, tracked quarterly on a frozen SKU panel so movement means movement.

Metric that moves

Format share of listings

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
Cost per wash	Selling price divided by the number of washes in the pack, where washes equal pack quantity divided by dose per wash.`per_wash = price / (pack_qty / dose_per_wash)`	Reconciles powder, liquid and bar into one comparable unit. A brand ranking never does this.
Assumed-dosage share	Share of rows behind a published figure where dose came from a category constant rather than the pack's stated instructions.	Tells the reader how much of the number rests on an assumption. Published next to every cost-per-wash figure.
Liquid premium	Median cost per wash for liquid divided by median cost per wash for powder, minus one, computed within zone.`premium = med(liquid)/med(powder) - 1`	Prices the convenience shift directly, rather than inferring it from format share movement.
Format shelf share	Share of listed detergent SKUs by format, per platform per zone.	Shows whether liquid is winning the shelf independently of whether it is winning volume.
Shrinkflation flag	Raised where pack quantity falls across two or more consecutive captures while MRP holds constant for the same SKU identity.`qty ↓ ∧ mrp = const`	Surfaces a real change in cost per wash that never appears as a price movement.
Cost-per-wash index	Category median cost per wash rebased to 100 at the first quarterly run, tracked forward on a frozen SKU panel.	Turns a one-off study into a time series. Requires the panel to be frozen from the first run.

Data model

The record we collect

The dosage fields carry their own provenance. Any figure computed from an assumed dose can be recomputed later if a better source appears.

detergent_wash_record.json SCHEMA

{ "captured_at": "2026-09-14T11:06:41+05:30", "platform": "bigbasket", "pincode": "560034", "brand": "<resolved_brand>", "sku_title": "<as_listed>", "format": "liquid", // powder | liquid | bar | pod | sheet "machine_type": "front_load", // top_load | front_load | hand | any "pack_qty": 2000, "pack_unit": "ml", "dose_per_wash": null, "dose_unit": "ml", "dose_source": "pack_stated", // pack_stated | category_constant "washes_per_pack": null, // derived "mrp": null, "selling_price": null, "cost_per_wash": null, // derived "pack_history_id": "<stable_sku_key>", "shrinkflation_flag": false, "stock_status": "listed_in_stock", "capture_id": "<uuid>" }

Schema shape — values null until the window runsDosage provenance retained per row

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
Selling price and MRP	Both, separately. Discount depth is computed from captured values rather than taken from the displayed badge.	Daily
Pack quantity and unit	The numerator of the wash count. Parsed deterministically, with a labelled hold-out set used to report parser accuracy.	Every capture
Dose per wash and its source	The single assumption the whole study rests on. Provenance is a field, not a footnote.	Weekly review
Format and machine type	Front-load and top-load are dosed differently. Blending them averages two categories into one wrong number.	Weekly review
Stable SKU key	Required to detect pack-size drift at constant price across quarterly runs.	Every capture
Format shelf share	Count of listed SKUs by format per zone, to see whether liquid is taking shelf independently of taking volume.	Weekly
Promotional mechanic	Multibuy and bundle offers change effective cost per wash and are not comparable to a straight percentage off.	Daily
Private-label detergent lines	Tracked as their own tier. Platform own-brands price aggressively per wash and are ranged by the placement owner.	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.

01Cost per wash by formatDistribution for powder, liquid, bar and pods, with the assumed-dosage share stated next to each.

02The liquid premiumHow much more a liquid wash costs than a powder wash, and how that varies by platform and zone.

03Front-load versus top-loadReported separately, because the two are dosed differently and blending them is the most common error in this category.

04Format shelf share by platformWhether liquid is winning the shelf, measured as share of listed SKUs rather than inferred from sales commentary.

05Cheapest wash on the shelfThe lowest defensible cost per wash available in each zone, and which format and pack size it comes from.

06Pack-size drift at constant priceSKUs where pack quantity fell while MRP held, published from the second quarterly run onward.

07Cost-per-wash indexCategory median rebased to 100 at the first run, updated each quarter on the frozen panel.

The figure this study is designed to produce

How much more a liquid detergent wash costs than a powder wash, against how much of the shelf liquid has taken.

A convenience premium the pack price hides completely. Written before collection so the sample can answer it.

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 detergent price data misleading

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.

Comparing pack prices across formats

A one-kilogram powder pack and a two-litre liquid bottle are not comparable at any level except the wash. Everything above that is a category-mix artefact.

Hiding the dosage assumption

Every cost-per-wash figure depends on a dose. A study that does not state where the dose came from, and how many rows were assumed, is not auditable.

Blending front-load and top-load

They use different formulations at different doses. Averaging them produces a number that describes neither.

Treating multibuy as a percentage discount

A two-for-one changes effective cost per wash differently from a flat thirty per cent off. Capture the mechanic, not just the outcome.

Re-running against a changed SKU panel

If the panel drifts between quarters, the index measures panel composition rather than price. Freeze it at the first run.

FAQ

Questions about this study

Including why there are no figures on it yet.

Because the 45-day window has not closed. This page publishes the method now so that the figure, when it lands, is auditable.

Findings will carry their collection dates, sample size and the share of rows where dosage was assumed rather than stated.

From the pack's own usage instructions wherever the listing exposes them, in which case the row is marked pack_stated. Otherwise a documented category constant is applied per format and the row is marked category_constant.

Every published figure states what proportion of its rows fell into each category. That proportion is how you judge how much weight to put on the number.

Because they are dosed differently and formulated differently. A front-load machine uses a low-foam detergent at a lower dose than a top-load powder.

Averaging the two produces a cost per wash that no household actually experiences. It is one of the most common errors in published home-care price comparisons.

Yes, and that is the strongest use of it. The requirement is that the SKU panel and the zone panel are frozen at the first run, otherwise later movements measure panel composition rather than price.

We set the panel with you before the first collection, precisely because retrofitting a tracker onto a one-off study means starting the time series again.

Yes. Pods are the easiest format to convert, since dose is one unit per wash, and they usually sit at the top of the cost-per-wash range.

Sheets and other emerging formats are captured where listed but reported separately while their shelf presence is thin, rather than being folded into a headline that they would distort.

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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