Platform · Flipkart

Flipkart Data Scraping Services

Q: Can you cover Flipkart Minutes?

Yes, as its own catalogue collected at pincode level with listed and in_stock held separately, the same design as our other quick commerce collection. We keep it separate from the marketplace because dark store ranging and marketplace listings are different data shapes. Merged, most fields would be null on most rows.

With bank and exchange offers held as mechanics, because folding them into a price invents a number.

Flipkart data scraping is the automated collection of publicly visible Flipkart data — listing price with all seller offers separated, bank and exchange offers captured as mechanics with their conditions, sale event phases recorded as context, and Flipkart Minutes kept as its own catalogue — so discount depth is not inflated by offers a given shopper cannot use.

A Flipkart listing showing a large discount is often stacking a bank offer and an exchange value on top of the price cut. Whether any shopper gets all three depends on their card and their old device. Treating the headline number as the price is the most common error in Indian marketplace data.

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flipkart_offers.jsonl LIVE FEED

{"flipkart_product_id":"MOBEX7712049", "listing_price":54999,"mrp":74999, "discount_pct":26.7, "bank_offer":{"issuer":"Example Bank", "discount":3000,"min_spend":50000, "cap":3000}, "exchange_offer_max":18000, "emi_no_cost":true,"emi_tenure":"6,9,12", "effective_price":"not_computed", "effective_reason":"depends_on_card_and_device", "seller_name":"Example Retail LLP", "is_default_offer":true,"is_f_assured":true, "sale_event":"none","catalogue":"marketplace"} {"flipkart_product_id":"MOBEX7712049", "seller_name":"Unknown Trading Co", "seller_offer_price":51200, "is_default_offer":false, "seller_offer_in_stock":true}

2 of 7,884,200 listing-seller rowsoffer lists captured · sale phase tagged · schema v3.8

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Flipkart or its owners. Flipkart 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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Flipkart at a glance

How we handle Flipkart specifically

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

Platform: Flipkart marketplace, with Flipkart Minutes as a separate catalogue
Offer structure: Bank and exchange offers as mechanics, never folded into one price
Sellers: All seller offers per listing, with F-Assured and seller identity
Sale events: Phase recorded as context on every record collected during one
Quick commerce: Flipkart Minutes kept separate, collected at pincode level
Identity: Flipkart product identifier plus brand normalisation
Refresh: Daily standard; sub-daily during sale event phases
Region: India

Platform specifics

What makes Flipkart data different

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

Bank and exchange offers are mechanics, not discounts

A meaningful share of Flipkart's advertised value is delivered outside the listing price: bank card offers with a specific issuer and cap, no-cost EMI, and exchange or upgrade value on an old device.

Why folding them in is wrong

Bank offers depend on the shopper's card. Which issuer, which card type, minimum spend, maximum discount. None of that is public per shopper.
Exchange value depends on the device being traded in and its assessed condition.
No-cost EMI is a financing term, not a price reduction, though it changes willingness to pay.
They stack conditionally, and the conditions are not always fully published.

We capture each as a structured mechanic with its conditions — bank_offer with issuer, cap and minimum spend; exchange_offer with maximum value; emi_no_cost with tenure. The listing price stays clean.

We do not compute a single effective price from them. Presenting one would embed assumptions about a shopper we cannot see, and it is the specific reason Indian marketplace price indices frequently disagree with what buyers experienced.

Seller offers, not just the default price

A Flipkart listing frequently carries multiple seller offers. The default shown is not always the cheapest, and for brand teams the interesting sellers are rarely the default one.

Unauthorised sellers show up in the offer list, not on the default offer.
Price dispersion across sellers on one listing indicates channel control problems.
F-Assured status affects buyer confidence and placement, and differs by seller on the same listing.
Seller-level stock means the cheapest offer may be unavailable.

We collect the full offer list with seller identity, F-Assured status and per-offer availability, plus is_default_offer. Collecting only the default price is the single biggest gap in most Flipkart datasets and it is exactly the gap that hides grey-market activity.

Sale event phases need flagging, not averaging

Flipkart's major sale events run in phases over days, with early access windows, headline days and a tail. Prices during those windows are not baseline prices.

A price index computed across a sale event without flagging it shows a market-wide crash that is a scheduled commercial event. Comparing that period to a normal one compares two different market states.

We record sale_event and sale_phase as context on every record collected during an event, so baseline and event periods are separable. Event-only bundles and exclusives are flagged too, since counting them as range additions inflates assortment growth and then produces phantom delistings afterwards.

Flipkart Minutes is collected as its own catalogue at pincode level rather than merged into the marketplace, for the same reason we separate quick commerce elsewhere: dark store ranging and marketplace listing are different data shapes.

Scope

What we collect on Flipkart, 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

Listing price clean, with offers held as separate structured mechanics
Bank offers with issuer, cap and minimum spend as published
Exchange and upgrade offers with maximum value
No-cost EMI availability and tenure
Full seller offer list with identity, F-Assured status and per-offer availability
Sale event and phase as context on records collected during events
Event-only bundles and exclusives flagged
Flipkart Minutes as a separate pincode-level catalogue
Ratings, review text and review counts without reviewer profiles

❌ What we do not, and why

A single effective price computed from bank, exchange and EMI offers
Whether a specific shopper qualifies for a given bank offer
Seller Hub or any credentialed Flipkart system
Sales volumes or seller economics
Reviewer names, profiles or review histories

Core Flipkart fields

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

Field	What it is on this platform
flipkart_product_id	Platform product identifier, the join key
listing_price / mrp / discount_pct	Clean listing price, printed MRP and computed discount
bank_offer	Structured: issuer, discount, cap and minimum spend as published
exchange_offer_max	Maximum exchange value advertised, where shown
emi_no_cost / emi_tenure	No-cost EMI availability and tenure
seller_name / seller_offer_price	Per-seller identity and price across the offer list
is_default_offer / is_f_assured	Whether this is the shown offer, and F-Assured status
seller_offer_in_stock	Availability per seller offer, since the cheapest may be unbuyable
sale_event / sale_phase	Which event and phase the observation falls within
is_event_exclusive	Event-only bundle or exclusive, flagged at collection
catalogue	marketplace or minutes, kept separate

Use cases

What teams do with Flipkart data

Defensible discount depth measurement

Listing price stays clean with offers held as mechanics, so discount depth reflects the price rather than a stacked total that assumes a card and a trade-in the shopper may not have.

Unauthorised seller detection

The full seller offer list with identity and availability is collected rather than only the default price, surfacing sellers on your listings that you have not authorised.

Sale event benchmarking without contaminating baselines

Event and phase context on every record keeps promotional periods separable, so a scheduled sale is not read as a market-wide price collapse.

Marketplace and quick commerce compared cleanly

Flipkart Minutes is collected as its own pincode-level catalogue, so dark store ranging is not confused with marketplace assortment.

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

Send us a Flipkart 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.

Flipkart is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Flipkart 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 ecommerce data scraping covers, and a Flipkart-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

Flipkart data scraping: frequently asked questions

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

Because whether a shopper gets a bank offer depends on their card issuer and card type, and exchange value depends on the device they are trading in. None of that is public per shopper.

We capture each as a structured mechanic with its conditions and keep the listing price clean. A single effective price would embed assumptions about a buyer we cannot see — and that is exactly why Indian marketplace indices often disagree with what buyers experienced.

Yes, the full offer list with seller identity, F-Assured status and per-offer availability, plus a flag for which offer is shown by default.

Collecting only the default price is the biggest gap in most Flipkart datasets, because unauthorised sellers appear in the offer list rather than on the default offer. For brand protection, the non-default offers are the whole dataset.

Event and phase are recorded as context on every record collected during them, so baseline and event periods stay separable.

Without that flag, a price index across a sale event shows a market-wide crash that is a scheduled commercial event. We also flag event-only bundles, since counting them as range additions inflates assortment growth and creates phantom delistings afterwards.

Yes, as its own catalogue collected at pincode level with listed and in_stock held separately, the same design as our other quick commerce collection.

We keep it separate from the marketplace because dark store ranging and marketplace listings are different data shapes. Merged, most fields would be null on most rows.

The percentage against MRP is computable and we deliver it, but MRP is a printed maximum rather than a prevailing market price, so a large discount against MRP does not always mean a competitive price.

For competitive work we recommend indexing against observed competitor prices rather than against MRP. We provide both so you can see the difference.

We quote individually. Drivers are category or SKU scope, whether full seller offer lists are required, refresh frequency, and whether Minutes coverage is included as a second catalogue.

Default-offer-only collection on a defined category sits at the lighter end; full offer lists across a broad catalogue with event-period sub-daily refresh sits higher. One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Flipkart 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.

Get a free sample Talk to an engineer

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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