Platform · Rakuten

Rakuten Data Scraping Services

Q: How do you handle campaign periods?

With campaign_context on every record collected during a known campaign window, so baseline and campaign data stay separable. Without it, a points-rate average computed across a campaign overstates normal conditions substantially. We also distinguish shop-level from platform-campaign multipliers where possible, since a shop competing hard on its own is a different signal from one riding a platform-wide campaign.

Where points are the promotional mechanic, and a price without the multiplier is only half the offer.

Rakuten data scraping is the automated collection of publicly visible Rakuten data — shop-level listings with points multipliers captured as a separate value layer, campaign periods flagged as context, shipping treatment recorded and shop identity preserved — because on Rakuten a large share of the effective offer is delivered in points rather than in price.

A Rakuten listing showing a modest discount may be offering ten times the normal points. To a Japanese shopper that is a substantial value transfer. To a price-only dataset it is invisible.

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rakuten_offers.jsonl LIVE FEED

{"rakuten_item_code":"shop123:item-7712", "shop_id":"shop123", "shop_name":"Example Store", "product_key":"aw-rkt-44810", "match_confidence":0.92, "price_jpy":4980, "points_rate":1, "points_multiplier":10, "multiplier_source":"platform_campaign", "points_conditions":"entry required, cap 1000pt", "campaign_context":"super_sale_window", "shipping_terms":"free over 3980", "effective_price":"not_computed", "effective_reason":"points_value_depends_on_tier"} {"rakuten_item_code":"shop901:item-2210", "price_jpy":5280, "points_multiplier":1, "note":"higher price, no multiplier — cheaper on price alone"}

2 of 3,204,880 shop-item rowscross-shop matched 89.2% · schema v2.5

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

How we handle Rakuten specifically

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

Platform: Rakuten Ichiba shop listings in Japan
The mechanic: Points multipliers as a separate value layer, never folded into price
Structure: A marketplace of shops — shop identity on every record
Campaigns: Campaign period flagged as context, since multipliers spike during them
Shipping: Shipping treatment captured, as it varies sharply by shop
Currency: JPY, with prices retained exactly as displayed
Refresh: Daily standard; sub-daily during major campaign periods
Region: Japan

Platform specifics

What makes Rakuten data different from Western marketplaces

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

Points are the promotion, not a loyalty footnote

Rakuten's points programme is central to how the marketplace competes. Shops and the platform both run points multipliers, and during campaigns those multipliers reach levels where they represent a significant share of transaction value.

Why price-only collection misreads the market

A shop with a higher price and a large multiplier can be the better offer, and a price index will rank it as worse.
Promotional intensity is understated, because the promotion is happening in points rather than in price.
Campaign periods look quiet in price data while being the most aggressive periods in the market.
Multipliers stack — shop-level, platform campaign and card-linked — with conditions.

We capture points_rate and points_multiplier as separate fields with conditions where published, alongside the listed price. We do not compute a single effective price from them.

The reason is the same discipline applied elsewhere on this site: points value depends on the shopper's membership tier, card and redemption behaviour. Converting points to currency at a fixed rate embeds an assumption we cannot verify, and it would produce a confident number that is wrong for most shoppers.

A marketplace of shops, so shop identity matters

Rakuten Ichiba is a marketplace where individual shops operate their own storefronts, set their own prices, run their own points multipliers and set their own shipping terms.

Price is shop-set, so a comparison across shops compares merchant decisions rather than platform pricing.
Shipping terms vary enormously by shop and materially affect total cost.
The same product appears across many shops with different prices, points and shipping.
Shop reputation and review volume differ, which affects which offer a shopper actually takes.

We put shop_id and shop_name on every record and deliver the offer set per product rather than only the cheapest or the first. As on other marketplaces, the non-default offers are where brand and channel questions get answered.

Campaign periods need flagging, not averaging

Rakuten runs major campaign periods during which points multipliers rise sharply across the platform. Data collected during those windows does not describe baseline conditions.

A points-rate average computed across a campaign period will overstate normal conditions substantially, and comparing a campaign period to a normal one compares two different market states — the same problem as sale events on Southeast Asian and Indian marketplaces.

We record campaign_context on every record collected during a known campaign window, so baseline and campaign periods stay separable. Shop-level multipliers and platform-level campaign multipliers are recorded separately where distinguishable, since a shop competing hard on its own is a different signal from a shop riding a platform-wide campaign.

Scope

What we collect on Rakuten, 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

Shop identity on every record, with the full offer set per product
Listed price in JPY exactly as displayed
Points rate and multiplier as separate fields with conditions where published
Shop-level and platform campaign multipliers distinguished where possible
Campaign period flagged as context on every record collected within one
Shipping terms and thresholds per shop
Shop review volume and rating as business signals
Product identity matched across shops with confidence
First-seen dates and listing lifecycle

❌ What we do not, and why

A single effective price computed by converting points to currency
Whether a specific shopper qualifies for a card-linked multiplier
Shop-side sales, margin or fee data
Member-tier pricing requiring a signed-in session
Reviewer names, profiles or review histories

Core Rakuten fields

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

Field	What it is on this platform
rakuten_item_code	Platform item code, the record key
shop_id / shop_name	Shop identity, mandatory since price and terms are shop-set
product_key / match_confidence	Cross-shop product identity with confidence
price_jpy	Listed price exactly as displayed
points_rate / points_multiplier	Base points rate and any multiplier, as separate fields
points_conditions	Conditions attached to the multiplier where published
multiplier_source	shop, platform_campaign or card_linked where distinguishable
campaign_context	Which campaign window, if any, the observation falls within
shipping_terms / free_shipping_threshold	Shipping treatment, which varies sharply by shop
shop_review_count / shop_rating	Shop-level reputation as a business signal
first_seen	When the listing was first observed

Use cases

What teams do with Rakuten data

Points-aware competitive analysis

Points rates and multipliers are captured alongside price, so a shop competing through points rather than price is correctly identified instead of ranking as uncompetitive.

Cross-shop offer comparison on one product

The full offer set per product with shop identity, points and shipping shows which shops are winning an item and on what mechanic.

Campaign versus baseline promotional intensity

Campaign context on every record keeps campaign windows separable, so points-rate averages reflect either baseline or campaign conditions rather than a blend of both.

Channel monitoring for brands entering Japan

Shop identity across the offer set surfaces which merchants are listing your products and at what price and points level, which a cheapest-offer view conceals.

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

Send us a Rakuten 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.

Rakuten is usually collected alongside its competitors

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

Rakuten data scraping: frequently asked questions

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

Because points value depends on the shopper's membership tier, card and redemption behaviour. Converting at a fixed rate embeds an assumption we cannot verify and produces a confident number that is wrong for most shoppers.

We deliver points_rate, points_multiplier and the conditions as separate fields so you can model points value on your own assumptions rather than inheriting ours.

Because Rakuten Ichiba is a marketplace of independent shops. Each sets its own price, its own points multiplier and its own shipping terms.

Comparing across shops compares merchant decisions, not platform pricing. And for brands, the non-default offers are where channel questions get answered — so we deliver the full offer set rather than only the cheapest.

With campaign_context on every record collected during a known campaign window, so baseline and campaign data stay separable.

Without it, a points-rate average computed across a campaign overstates normal conditions substantially. We also distinguish shop-level from platform-campaign multipliers where possible, since a shop competing hard on its own is a different signal from one riding a platform-wide campaign.

Yes, with confidence scoring. Japanese product titles on this platform are frequently long and shop-formatted, so exact matching does not work and we use attribute and identifier comparison alongside title normalisation.

Uncertain matches are flagged rather than merged, because a wrong merge produces a cross-shop price comparison between two different products.

Ichiba is the core. Other Rakuten properties operate on different models and would be scoped separately rather than merged, for the same reason we separate verticals on other multi-business platforms.

Merging them produces a schema where most fields are null on most rows, which looks like a large dataset and is mostly empty.

We quote individually. Drivers are category or product scope, whether the full cross-shop offer set is required, and refresh frequency during campaign periods.

Cheapest-offer collection on a defined category sits at the lighter end; full offer sets with campaign-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 Rakuten 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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