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Platform · Americanas

Americanas Data Scraping Services

Where the number a shopper reads first is a monthly instalment, not a price — and freight is quoted per postcode, not per country.

Americanas data scraping is the automated collection of publicly visible Americanas data with the instalment plan captured as a first-class field alongside the cash price, freight quoted per CEP, and payment-method-specific prices held separately.

Brazilian ecommerce listings lead with an instalment figure and a payment-method discount, and a schema with one price column will record neither of the two numbers a shopper is actually comparing.

Free pilot on your own Americanas list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

americanas_offers.jsonl LIVE FEED
{"product_id":"55* redacted","ean":"789* redacted", "cash_price":1899.00,"currency":"BRL", "payment_method_prices":[ {"method":"instant_transfer","price":1709.10}, {"method":"card","price":1899.00}], "primary_presented_price":"instalment_monthly", "instalment_count":12, "instalment_monthly":158.25, "instalment_interest_free":true, "cep":"01310-* redacted","freight_cost":24.90, "seller_type":"retailer_own","is_default_offer":true} {"product_id":"55* redacted", "cep":"69900-* redacted","freight_cost":119.80, "cep_panel":"published with any national figure"}
2 of 3,208,400 product-offer rowsCEP panel published with any national figure · schema v1.9

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Americanas or its owners. Americanas 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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B2C Marketplace
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Tmall
Americanas at a glance

How we handle Americanas specifically

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

Platform
Americanas, Brazil
Headline signal
Instalment plan — count, monthly amount and interest status
Payment prices
Method-specific prices held separately
Freight
Quoted per CEP, with a published destination panel
Sellers
Own stock separated from marketplace sellers
Currency
Real retained, no silent conversion
Refresh
Daily standard; sub-daily on national sale events
Region
Brazil
Platform specifics

What makes Americanas data different from Western marketplaces

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

The instalment plan is the headline, and it needs three fields

A Brazilian listing typically leads with an instalment presentation — a number of monthly payments and the amount of each — and shoppers compare on that figure at least as much as on the total.

  • The instalment count matters on its own. A longer plan at the same total is a materially better offer to a shopper managing monthly outgoings.
  • Whether the plan carries interest changes the total, and an interest-free plan is a genuine commercial concession rather than a presentation choice.
  • The monthly amount is the number in the largest type on the page.

We capture instalment_count, instalment_monthly and instalment_interest_free as separate fields alongside the cash price, and never fold any of them into a single price column. A competitive comparison that ignores the plan misses the field the market actually competes on.

Payment method changes the price, so it is a dimension

Listings frequently show a lower price for particular payment methods — a discount for bank-slip or instant-transfer payment, against a card price that supports instalments.

This creates a genuine trade-off rather than a single best price: the lowest figure and the instalment option are usually mutually exclusive. A dataset that records the lowest number reports a price nobody choosing instalments can obtain.

We capture payment_method_prices as a set with each method's price, and record which figure the listing presented as primary. Both the lowest available price and the instalment-supporting price are retained, and any computed comparison states which basis it used.

Freight is per CEP, so a national price does not exist

Brazil is large and freight varies substantially by destination postcode. A single national delivered price is not a thing that exists.

We capture freight quotes against a stated CEP panel, with the seller's origin where displayed, and publish the panel alongside any national figure rather than quoting one destination as if it described the country.

On low-value items freight can exceed the item price, so a delivered comparison is frequently the only one that means anything — and it is always specific to a route.

State tax varies inside one country, so a price gap can be statutory

Brazilian consumption tax on goods is levied at state level, and the rate applying to a sale depends on the origin and destination states rather than on the country. That means a displayed price can differ between two Brazilian destinations for reasons that have nothing to do with a pricing decision.

  • An intra-country price comparison can attribute to commercial strategy a gap that is entirely a matter of statute.
  • The origin state matters, not just the destination, because the rate depends on the pair.
  • Marketplace sellers sit in different states, so two offers on one product can carry different tax positions.

We capture the destination as a CEP and the seller origin where displayed, and record the displayed price gross with the tax basis as shown. We do not compute a net-of-tax figure ourselves — the rate for an origin-destination pair is a determination we cannot make reliably from public data, and asserting one would be inventing precision. What we supply is the gross price with both endpoints recorded, so a client with the rate tables can do the normalisation and check it.

Every marketplace seller has its own freight curve

Freight depends on the route, and on a marketplace each seller ships from its own origin. That means one product does not have a freight curve across the CEP panel — it has one per seller.

The consequence is that the cheapest seller changes by destination. A seller who is cheapest delivered to one region can be the most expensive delivered to another, and both statements are true simultaneously.

We capture freight per seller per CEP rather than per product per CEP, with the seller origin where displayed. Any delivered-price ranking is therefore specific to a destination, and where a national ranking is wanted it is computed across the published CEP panel with the panel stated.

Collapsing this to one freight figure per product is the most common way a Brazilian delivered-price analysis produces a ranking that is wrong for most of the country.

Marketplace sellers sit alongside own stock

Americanas sells its own stock and hosts marketplace sellers on the same product pages, and the two differ in what the price represents and in who owns the delivery promise.

We capture seller_type and the seller name where displayed, plus which offer held the default position. Where the page gives no indication the field is recorded as unknown rather than assumed to be the retailer.

For brand clients this is the channel-integrity field: marketplace sellers listing a brand's products outside its distribution arrangements are invisible in any dataset that stores one price per product.

Portuguese product data and Brazilian identifiers

Titles, attributes and category paths are in Portuguese, and titles frequently carry pack, variant and promotional information inline rather than in structured attributes.

We retain the published title and category path with translation supplied as separate fields, and publish parser accuracy against a labelled hold-out set rather than asserting it. EAN is retained where present, which in this market is inconsistent enough that attribute-based grouping is often needed as a fallback with the rule published.

Prices stay in real with currency on every record and no silent conversion; the daily rate is supplied separately where a converted view is required.

Scope

What we collect on Americanas, 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

  • instalment_count, instalment_monthly and instalment_interest_free as separate fields
  • Cash price retained alongside, never merged with the plan
  • payment_method_prices as a set, with the primary presented figure recorded
  • Both the lowest available price and the instalment-supporting price retained
  • Freight quotes against a stated CEP panel, with the panel published
  • Seller origin where displayed, including origin state where shown
  • Freight captured per seller per CEP, not per product per CEP
  • Gross prices with the displayed tax basis, and no net-of-tax figure computed by us
  • seller_type separating own stock from marketplace, unknown where not shown
  • is_default_offer, so page ownership is tracked over time
  • Portuguese titles and category paths as published, translation separate
  • Parser accuracy published against a labelled hold-out set
  • Real retained with currency, daily rate supplied separately

❌ What we do not, and why

  • Customer identities or any personal data
  • Anything behind a login, including account-specific pricing or coupons
  • Instalment figures folded into a single price column
  • National delivered prices quoted from a single CEP
  • Seller identity where the page does not indicate one
  • Silent currency conversion inside a price field

Core Americanas fields

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

Field What it is on this platform
product_id / ean Platform identifier and EAN where present
cash_price / currency Price in real, never silently converted
instalment_count / instalment_monthly Plan length and monthly amount
instalment_interest_free Whether the plan carries interest
payment_method_prices Set of method-specific prices
primary_presented_price Which figure the listing led with
cep / freight_cost Destination postcode and its quote, per seller
seller_origin_state Where the seller ships from, where displayed
tax_basis_displayed Gross basis as shown; we do not compute net ourselves
cep_panel Published alongside any national figure
seller_type / seller_name Own stock or marketplace, unknown where not shown
is_default_offer Whether this offer held the page at capture
title_raw / title_translated Published title, translation separate
captured_at Timestamp at minute precision, BRT
Use cases

What teams do with Americanas data

Competitive analysis on the field the market uses

Instalment count, monthly amount and interest status captured separately allow comparison on the number shoppers actually read, not just the total.

Payment-method trade-off analysis

Method-specific prices held as a set expose that the lowest figure and the instalment option are usually mutually exclusive.

Delivered-cost comparison by route

Freight against a published CEP panel, since a national delivered price does not exist and on low-value items freight can exceed the item.

Marketplace channel integrity

Seller type on every offer surfaces sellers placing a brand's products outside its distribution arrangements.

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

Send us a Americanas 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.
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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
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  • 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.

Americanas is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Americanas 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 Americanas-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

Americanas data scraping: frequently asked questions

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

Because it is the number in the largest type on the page, and shoppers compare on it at least as much as on the total. A longer interest-free plan at the same total is a materially better offer to someone managing monthly outgoings.

We capture the count, the monthly amount and whether the plan is interest-free as three separate fields, and never fold any of them into a price column.

It depends which shopper you compete for, and the trade-off is real: the lowest figure and the instalment option are usually mutually exclusive.

We retain the method-specific price set, the primary presented figure, and the cash price. Any computed comparison records which basis it used, so the choice is visible rather than assumed.

Only as a figure computed across a stated CEP panel, with the panel published. A single national delivered price does not exist because freight varies substantially by destination.

On low-value items freight can exceed the item price, so quoting one route as if it described the country would be misleading.

No, and we would be wary of a vendor that does. Consumption tax here is levied at state level and the applicable rate depends on the origin and destination pair, which is not something we can determine reliably from public listing data.

We supply the gross displayed price with the tax basis as shown and both endpoints recorded, so a client holding the rate tables can normalise it and check the result. Asserting a net figure ourselves would be inventing precision.

Because on a marketplace each seller ships from its own origin, so one product has as many freight curves as it has sellers.

The practical consequence is that the cheapest seller changes by destination — a seller cheapest delivered to one region can be the most expensive delivered to another. Collapsing this to one freight figure per product is the most common way a Brazilian delivered-price ranking ends up wrong for most of the country.

The published title and category path are retained and translation supplied separately. Titles here carry pack, variant and promotional information inline rather than in structured attributes, so parsing them is a substantial part of the work.

We publish parser accuracy against a labelled hold-out set rather than asserting it.

Inconsistently present, which is why attribute-based grouping is available as a fallback with the rule published and both counts reported.

Building a project on the assumption that EAN is always there is a common way for Brazilian marketplace work to fail at the matching stage.

See real Americanas 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.

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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