Platform · Croma

Croma Data Scraping Services

Where the listed price is the start of the calculation, not the end of it — exchange, EMI and bank offers all sit on top.

Croma data scraping is the automated collection of publicly visible Croma data with the offer stack captured as separate fields — exchange value, no-cost EMI, bank offers and bundles — so that what a shopper actually pays can be reconstructed rather than approximated by the listed price.

In Indian consumer electronics the listed price is rarely what changes a purchase decision. The exchange value on an old handset, whether EMI is no-cost, and which bank card carries an instant discount routinely matter more — and none of them is a discount on the price.

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croma_offers.jsonl LIVE FEED

{"croma_sku":"2447* redacted","model_number":"XM-55* redacted", "listed_price":54990.00,"mrp":74900.00, "exchange_value_max":6000.00, "exchange_basis":"headline_requires_device_entry", "emi_no_cost":true,"emi_tenures":[6,9,12], "bank_offer_value":3000.00, "bank_issuer":"Example Bank","instrument_type":"credit_card", "bank_offer_min_txn":50000.00, "offer_path":"listed_only","effective_price":54990.00} {"croma_sku":"2447* redacted", "offer_path":"bank_offer+exchange", "effective_price":45990.00, "path_conditions":["holds Example Bank credit card", "trades in eligible device at max value"], "pincode":"400058","installation_available":true}

2 of 486,200 sku-pincode rowsoffer paths computed 4 per row · schema v2.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Croma or its owners. Croma and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Croma at a glance

How we handle Croma specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: Croma online, with store-level availability where exposed
Offer stack: Exchange, EMI, bank offers, bundles — each its own field
Effective price: Computed per offer path, with the path recorded
Conditionality: Every offer carries the condition it depends on
Serviceability: Delivery and installation availability by pincode
Refresh: Daily standard; sub-daily on launch and festive windows
Identifiers: Croma SKU, brand model number and EAN where visible
Region: India

Platform specifics

What makes Croma data different from general retail

These are the reasons a Croma dataset needs its own handling rather than a shared retail schema.

The offer stack is the pricing, and none of it is a discount

A Croma listing routinely carries four things sitting on top of the price, and each works differently.

Exchange value reduces the amount paid but depends entirely on the device traded in. It is not available to a first-time buyer at all.
No-cost EMI changes the payment shape rather than the total, and for many buyers it is the deciding factor even though the amount paid is unchanged.
Bank offers are instant discounts conditional on a specific card and often a minimum transaction. They are real money off, but only for a subset of shoppers.
Bundles add a product rather than reducing the price.

Collapsing these into a single effective price requires assuming which shopper you mean, and any such number is wrong for most of them. We capture each as its own field with its condition attached, and compute effective price per offer path with offer_path recorded, so a client can pick the path that matches the customer they compete for.

Exchange value is a competitive lever nobody tracks

Exchange offers on smartphones and large appliances are quoted per traded-in model and move independently of the listed price. A retailer can hold price and become materially cheaper by raising exchange value, and a price-only dataset records no change at all.

Where the exchange value is quoted publicly against specified models, we capture it against those models. Where it is quoted only after entering device details, we record the maximum stated value and flag it as a headline figure rather than an obtainable one, because the two are not the same and conflating them overstates the offer.

Bank offers are conditional, and the condition matters

An instant discount of a stated amount on one issuer's cards above a minimum transaction is not a price cut. It is a price cut for a subset of buyers.

We capture bank_offer_value, the issuer, the instrument type and the minimum transaction, all as displayed. This keeps the arithmetic honest: an effective price computed on a bank offer is valid only for a shopper holding that card, and the fields make that visible instead of burying it.

We do not attempt to model what share of shoppers hold a given card. That is a modelling exercise on data we do not have, and presenting it as measurement would be misleading.

Serviceability by pincode, and installation as part of it

Large appliances and televisions are not simply delivered. Installation and demonstration are part of the purchase, and availability varies by pincode independently of stock.

We capture delivery serviceability, stated delivery window and installation availability per pincode, kept separate from stock state. An item in stock but not installable in a pincode is a different commercial situation from one that is out of stock, and merging them into a single availability flag makes both unreadable.

Scope

What we collect on Croma, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Listed price and MRP captured separately
exchange_value_max with the models it is quoted against, where public
Exchange values flagged as headline where the obtainable value needs device entry
emi_no_cost flag, tenure options and any processing fee stated
bank_offer_value with issuer, instrument type and minimum transaction
Bundle contents described as displayed, kept out of the price field
effective_price computed per offer_path, with the path recorded
Delivery serviceability, delivery window and installation availability by pincode
Store-level availability where the platform exposes it
Croma SKU, brand model number and EAN retained together
Rating, review count and review velocity

❌ What we do not, and why

Customer identities or any personal data
Anything behind a login, including cart-stage or account-specific pricing
Actual exchange quotes, which require entering device details
Modelled estimates of how many shoppers hold a given bank card
Extended warranty or service quotes, configured per order
Croma internal cost, margin or vendor terms

Core Croma fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
croma_sku / model_number / ean	The three identifiers, retained together
listed_price / mrp	As displayed, captured separately
exchange_value_max / exchange_basis	Headline exchange value and what it is quoted against
emi_no_cost / emi_tenures	Whether no-cost EMI is offered and over what tenures
bank_offer_value / bank_issuer	Instant discount and whose card it requires
bank_offer_min_txn / instrument_type	The conditions the offer depends on
bundle_contents	What is included, described as displayed
offer_path / effective_price	Which combination was assumed, and the price under it
pincode / serviceable	Delivery serviceability for this pincode
installation_available	Separate from stock and from delivery
store_stock_state	Store-level availability where exposed
captured_at	IST timestamp at minute precision

Use cases

What teams do with Croma data

True competitive position in Indian electronics

Exchange, EMI and bank offers captured separately reveal where a competitor is genuinely cheaper for a given shopper, which a listed-price comparison misses entirely.

Exchange-value monitoring

A retailer can hold price and become materially cheaper by raising exchange value. A price-only dataset records no change; this one does.

Bank-offer landscape tracking

Issuer, instrument and minimum transaction on every offer show which card partnerships are being used to buy share in a category.

Serviceability and installation coverage

Delivery and installation availability by pincode, separate from stock, show where a product is genuinely purchasable rather than merely listed.

The 24-hour sample — run on your sources, not ours

Send us a Croma item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned inside two business days
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Croma is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Croma data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what consumer electronics data covers, and a Croma-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Croma data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because doing so requires assuming which shopper you mean. Exchange value applies only to someone trading in a device; a bank offer only to someone holding that card; no-cost EMI changes payment shape rather than total.

A single effective price is wrong for most shoppers. We compute it per offer path and record the path, so you can pick the one matching the customer you actually compete for.

Only where it is publicly quoted against named models. Where the obtainable value requires entering device details, we capture the maximum stated value and flag it as a headline figure.

The distinction matters. A headline exchange value and an obtainable one are frequently far apart, and treating them as the same overstates the offer.

No, they are a separate field with the issuer, instrument type and minimum transaction attached. A bank offer is a real discount, but only for shoppers holding that card.

We do not model what share of shoppers that is. Doing so would require data we do not have, and presenting a modelled share as measurement would be misleading.

Yes, separately from stock and from delivery serviceability. For large appliances and televisions, installation is part of the purchase and its availability varies by pincode independently of stock.

An item in stock but not installable in a pincode is a different situation from one out of stock, and a single availability flag makes both unreadable.

The two retailers compete closely, and the datasets are structured differently by design. Croma collection is built around the offer stack, because that is where its pricing complexity sits. Reliance Digital collection is built around network scale and store-level fulfilment.

Most clients tracking Indian electronics take both, and the fields are aligned so they can be compared directly.

See real Croma data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

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Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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