N11 Scraping: Products, Sellers and Lira Prices

N11 prices are quoted in lira and lira does not sit still. Our N11 scraper stamps every price, badge and stock figure with the moment it was read, so two months never blur into one.

Plans from €169/month · Free project assessment · Reply within 1 business day

N11 Scraper
Solutions

How we build and run an N11 feed

You set the scope: a category path, a keyword list, a brand, a single /magaza/ storefront, or a list of groupId values you already track. We build the N11 scraper, run it on the cadence you pick, and hand back rows with every identifier intact so this month joins to last month.

Rate limits and bot defences change without notice, so anti-bot handling, proxy rotation and CAPTCHA solving are part of the service rather than your problem. We do not sign in and we do not go behind a login.

Output is CSV, JSON, XLSX or an endpoint of ours your systems call, pushed to S3, to Google Cloud Storage or straight into a database you own. Dated snapshots are the default on price work. Maintenance is included: when n11 changes its markup we repair the crawler, and that is not a change request.

N11 product data, field by field

An N11 product page ships its state as JSON inside the HTML, so an N11 scraper reads n11's own field names rather than rendered text. Identifiers come first: one listing hands out four.

  • Identifiers - groupId, the number closing the /urun/ address; the internal id; defaultSkuId; defaultPimsId; and defaultGtin where a barcode was supplied.
  • Names and brand - title, subTitle, which repeats the title with the chosen variant appended, and productBrand, which reads Diger, Turkish for other, on unbranded listings.
  • Price - displayPrice and price arrive as Turkish-formatted strings, dot for thousands and comma for decimals, each with a float twin. Around them sit oldPrice, firstListPrice, discount, discountRate, instantDiscountPrice, instantDiscountPercentage and mobileApplicationPrice, the figure the app quotes.
  • Badges - productBadgeType, with values COUPON_BADGE (Kuponlu Urun), SHOCKING_DEAL (Kacmaz Urun), DEAL_BADGE_SUPER and DEAL_BADGE_SHOCKING (Iyi Fiyat price stars), PURPLE_PRODUCT (n11'in Teklifi) and INSTALLMENT_3 to INSTALLMENT_9 (Pesin Fiyatina N Taksit, N payments at the cash price).
  • Instalments - installmentDTO with installmentAvailable and installmentType, plus categoryMaxInstallmentCount, where the ceiling lives: nine on a power bank, one on a phone.
  • Stock - a plain integer in stock, with stockWarning for critical stock and outOfStock.
  • Variants - skuDefinitions naming the axes (Renk for colour, Dahili Hafiza for storage) and a skus array where each entry keeps its own id, pimsId, gtin, oem, stock and price.
  • Delivery - shipmentCity, the town goods leave from, plus shipmentCondition, shippingFree and preperationTime in Turkish business days.
  • Ratings - satisfyScore, a histogram from fiveStarCount to oneStarCount, and two counters that differ on purpose: reviewCount for ratings, commentCount for ratings with written text.
  • Specs - productAttributes as key, value and seoName triples, the vocabulary the facets are built from.

Warranty misbehaves: warrantyInfo is usually an empty array while the real claim sits in the title as text such as Apple Turkiye Garantili, so we parse it out of the name.

N11 product data, field by field
N11 seller data, brand facets and the thousand-row ceiling

N11 seller data, brand facets and the thousand-row ceiling

On N11 the seller is an object, not a name. Every listing carries a seller block with a numeric id, the nickName that doubles as the /magaza/ address, and the fields brand teams care about: officialSeller with its Yetkili Magaza label, plus authorizedDealerBrand, authorizedDealerText and authorizedDealerUrl, which mark a shop as an authorised dealer for a named brand rather than a reseller.

Performance arrives as numbers, not stars: storePoint nought to ten, sellerGrade nought to a hundred, and sellerFeedbackStatistics splits satisfaction across three axes with satisfied and unsatisfied counts on each: product information accuracy, packaging, seller contact. Add averageShipmentSpeed in hours, quickSeller with its Hizli Gonderim label, topSellerBadge, headquarterCity and timeInPlatform, which n11 gives as a band such as 8-9 years.

Coverage is a slicing problem. A listing shows twenty tiles and stops at page fifty, so a query, category or facet yields at most a thousand products. The way through is the platform's own axes: the brand facet ?m= carrying the readable brand name, the model facet ?md=, minp and maxp cutting a lira band, promotions pinning a campaign. For brand owners the brand-inside-category sitemap maps where a mark turns up, and n11 runs a Marka Koruma Merkezi portal for infringement notices.

Buyer identities we leave alone: n11 masks reviewer names to initials, we do not restore them, and where a seller's legal disclosure names a private individual we drop those fields.

N11 today: owner, catalogue and storefronts

N11 is a Turkish marketplace that opened in 2013 as a joint venture between Turkey's Dogus Group and South Korea's SK Group, trading through an operating company called Dogus Planet. Ownership then moved three times in quick succession: Getir bought the platform in 2023, the shares passed to Borancili Teknoloji in 2024 after Getir's own control changed, and in 2025 the Abu Dhabi investment house DMSF Holding took the company outright once the Turkish Competition Authority cleared the transfer. The Getir years are still baked into the data model, where product payloads carry isGetir, getirSeller and hideOnGetir flags that mean nothing to a shopper any more.

The storefront is Turkish only and quotes only Turkish lira. A product sits at /urun/{slug}-{groupId}, where the trailing number is the groupId and the slug is minted from the title. Categories are flat slug paths of one to three segments, such as /telefon-ve-aksesuarlari/cep-telefonu-aksesuarlari/tasinabilir-sarj-cihazi, and the top-level group that owns the category (elektronik, otomotiv-motosiklet) lives in a separate categoryGroupUrl value rather than in the address. Sellers get a storefront of their own at /magaza/{nickName}. Campaigns are not product pages at all: a campaign banner resolves to /arama carrying a numeric promotions id.

The published sitemap shows the shape. In September 2026 the product index holds about 1,100 files of 18,000 URLs each, which puts the catalogue near 20 million product pages, and the automotive group alone accounts for more than a third of them: spare parts filed under OEM codes, which is why slugs in that group read like part numbers. Beside them sit about 13,000 storefronts, roughly 4,000 category addresses and close to 100,000 brand-inside-category pages.

Get a Quote
dev_w
25

Developers

customers
500+

Customers worldwide

pages
1 500 000 000+

Pages extracted

stime
15000+

Hours saved for our clients

Plans

N11 Scraping Plans and Pricing

Airplane

€199 / one-time

setup fee - included

Data limits100,000
Frequencyone-time
Run timeup to 5 days
Data storing7 days

Helicopter

€169 / mo

setup fee €499

Data limits250,000
Frequencymonthly
Run timeup to 5 days
Data storing14 days

Glasses

€229 / mo

setup fee €499

Data limits1,000,000
Frequencyweekly
Run timeup to 5 days
Data storing30 days

DNA

€549 / mo

setup fee €799

Data limits3,000,000
Frequency3 times daily
Run timesame day
Data storing90 days

Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.

Full plan details →

Lira inflation and why N11 price data needs a clock

N11 price data carries a shelf life that most marketplaces never impose. Turkish annual consumer inflation stood at 31.51 percent in August 2026, with prices up 1.84 percent in that month alone, so a lira figure without the hour it was read is close to worthless: two rows from different months are not a price change, they are two different currencies sharing a symbol. Every row we extract from N11 carries its own capture time, and we write dated snapshots instead of overwriting, so a price series stays a series rather than a value that quietly replaced the last one.

The platform concedes the churn itself. Listing tiles carry a badge reading 10 gunun en dusuk fiyati, the lowest price of the past ten days, a label that only earns its place on a catalogue where the number moves that often. Around the sticker sit layers a single naive scrape flattens: a Kuponlu Urun badge pointing at a coupon applied later, a Sepette tag meaning the figure quoted is the basket price and not the listed one, an instalment badge offering three to nine payments at the cash price, and a separate mobileApplicationPrice for the app. What a buyer pays is assembled, not printed.

The public storefront carries every one of those layers, which makes an N11 scraper the practical way to watch a rival's price, catch a stock-out on a listing you do not own, and see the day another seller took the offer position on a listing you thought was yours.

Related Case Studies

eBay Data: 1.9M Auto Parts Listings from eBay.de, Specs Translated

eBay Data: 1.9M Auto Parts Listings from eBay.de, Specs Translated

About 1.9 million auto parts listings from eBay.de with brand, OEM, EAN and fitment, plus titles and specs machine-translated for the client's team.

Learn More about eBay Data: 1.9M Auto Parts Listings from eBay.de, Specs Translated
Takealot Data: 1,951 Top-Rated Products for a New Seller's Niche Research

Takealot Data: 1,951 Top-Rated Products for a New Seller's Niche Research

A one-off Takealot product list for an entrepreneur choosing niches: only products rated 4 stars or higher with at least 50 reviews.

Learn More about Takealot Data: 1,951 Top-Rated Products for a New Seller's Niche Research
Trendyol Data: 66,600 of 66,775 Products in a Category, Stock by Size

Trendyol Data: 66,600 of 66,775 Products in a Category, Stock by Size

Trendyol product data for an e-commerce startup reselling Turkish fashion: 27 categories in JSON on FTP, with in-stock status for every size.

Learn More about Trendyol Data: 66,600 of 66,775 Products in a Category, Stock by Size
Our Blog

Reads Our Latest News & Blog

Learn how to use web scraping to solve data problems for your organization

6 E-Commerce Sites Like eBay to Scrape in 2026

6 E-Commerce Sites Like eBay to Scrape in 2026

If you sell online, run a marketplace, or advise e-commerce clients, you already know why eBay matters: it’s one of the few places where big retailers compete side by side with thousands of small merchants and private sellers.

Top 8 E-commerce Websites to Scrape in 2026 (From Amazon to 1688)

Top 8 E-commerce Websites to Scrape in 2026 (From Amazon to 1688)

E-commerce teams do not just need “some” competitor data anymore. They need a continuous stream of real prices, discounts, stock levels, reviews, and seller behavior from the platforms that actually shape their markets.

How to Scrape Amazon Data: Benefits, Challenges & Best Practices

How to Scrape Amazon Data: Benefits, Challenges & Best Practices

Amazon provides valuable information gathered in one place: products, reviews, ratings, exclusive offers, news, etc. So scraping data from Amazon will help solve the problems of the time-consuming process of extracting data from e-commerce.

scrapeit logo

About ScrapeIt

ScrapeIt is a managed web scraping agency. We build the crawlers, run them, watch them, and repair them when a site shifts underneath. You receive data on a schedule, in the format you asked for, without standing up any infrastructure of your own.

We are not affiliated with n11 and we do not resell datasets. We collect public pages, scope each project to what a client actually needs, and say plainly when part of a scope will not work instead of shipping thin rows and calling it delivered.

FAQ

Do you offer an N11 API for product and price data?

Yes. The ScrapeIt N11 API returns groupId, title and productBrand, displayPrice with oldPrice and discountRate, the stock count and the seller block as JSON from an endpoint we host, refreshed on the schedule you set. The same N11 data also comes as CSV or XLSX files or straight into your database.

Which N11 fields do you extract, and which simply do not exist?

We extract the identifier set (groupId, id, defaultSkuId, pimsId, gtin), title and brand, the whole price stack with its float twins, badge types, instalment ceiling, the integer stock count, the variant array, delivery fields and the rating histogram. What N11 does not publish is a units-sold counter: there is no equivalent of an X sold line on the listing, so anyone offering you sales volumes off this site is inferring them. Warranty is another gap, since warrantyInfo is usually empty and the claim is written into the product title instead.

How do you cover a whole N11 category when search stops at 1,000 products?

By slicing rather than paging. A listing returns twenty tiles and refuses to go past page fifty, which caps any single query at a thousand rows. So we cut the category into leaf subcategories, then split each leaf by the brand facet, by model where it exists, and by lira price bands using minp and maxp until every slice sits under the ceiling. The product sitemap gives a second, independent pass over the same catalogue, and comparing the two is how we know the coverage is real instead of assumed.

How often can N11 price data refresh?

Daily is the usual cadence for price and stock, hourly is workable on a narrow watchlist, and weekly is enough for catalogue mapping. With Turkish inflation where it is, the refresh interval matters more here than on a euro or dollar marketplace: a monthly snapshot of lira prices measures the currency at least as much as the seller. Because we keep dated snapshots instead of overwriting, a change in price, in the badge set, in stock or in the seller behind a listing reads as a series you can query.

Is scraping N11 data lawful, and what do you do about personal information?

Yes. We collect only publicly available data - everything a visitor can see on N11 - and we collect it legally. We do not create accounts, we do not sign in, and nothing behind a login is in scope. Personal data we avoid by default: n11 already masks reviewer names to initials, we do not attempt to resolve them, and where a seller's mandatory legal disclosure identifies a private individual rather than a company we leave those contact fields out.

How does it Work?

Step 1 - Make a Request

You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.

Step 2 - Configuring Custom Web Crawlers

Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.

Step 3 - Collect and Deliver

Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.

Step 4 - Maintain and Support

Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.

Request a Quote

Tell us more about you and your project information.
Which sites, which fields, how often. A couple of lines is enough.

This form is protected by Google reCAPTCHA, which loads when you start filling it in. If you prefer not to use it, email us at info@scrapeit.io. Privacy & Cookie Policy

We reply within 1 business day. No obligation.

N11 data from €169/month. Free project assessment, reply within 1 business day.

scrapiet

Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
KRS: 0000996672
NIP: 5423457175
REGON: 523384582