Tokopedia sells through shops, not a shared catalogue: the same phone sits in dozens of stores at dozens of prices. We turn that into one clean Tokopedia product feed.
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We adjust scripts to fit your business goals. ScrapeIt acts as your managed Tokopedia data scraper: you name the categories, brands, sellers or product lists, we run the collection on your schedule and hand back CSV, Excel or JSON. Price monitoring, catalogue audits and seller research are the usual jobs.
The technical part is ours. Tokopedia sits behind commercial bot protection and its search results are limited in depth. Anti-bot handling, proxy rotation and CAPTCHA solving are part of the service, along with the request pacing and layout tracking that keep a feed stable.
A Tokopedia scraper reads two different objects. A listing card in a category or search result carries the name, the formatted price such as Rp3.792.000 with its integer twin, the crossed-out original price, the discount percentage, rating, review count, a preorder flag, image links and the shop. The product page carries far more. We extract Tokopedia product data as one row per offer:
Two numbers need care. The sold count on a listing card is a label, not an integer: exact while small, bucketed as 100+ terjual (100+ sold) once it grows, while the product page holds the precise figure. Indonesian formatting is the other trap - dots separate thousands, a comma marks the decimal, so Rp4.499.000 and a rating of 4,9 must be parsed, not read literally.
The shop is a dataset in its own right, and for brand teams it is often the more valuable half. Every seller page yields a consistent record:
Shipping cost is not a constant. Tokopedia quotes it from the buyer's city and district against item weight and the courier chosen, so a delivered price exists only once a destination is fixed. We take the destinations that matter to you and compute the landed price for each.
Reviews come with the star rating, review text, relative timestamps, seller replies, photo and video attachments, and the variant the buyer received. Tokopedia masks reviewer names itself and we keep it that way: we do not collect names of review authors or seller contact details.
Tokopedia is an Indonesian marketplace operated by PT Tokopedia and trading at tokopedia.com since 2009. It holds no stock of its own: every listing belongs to a shop that sets its own price, stock and dispatch city. The interface is in Bahasa Indonesia, prices are quoted in rupiah, and delivery runs on Indonesian couriers, so the catalogue reads as a map of local supply rather than a translated storefront.
Ownership has changed the platform in ways visible on the pages themselves. TikTok took a controlling stake in Tokopedia in 2024, and the two shopping surfaces are now stitched together. Merchant tooling is branded as the Tokopedia and TikTok Shop Seller Center, and the rating block on a listing states that its scores are taken from Tokopedia and TikTok Shop by Tokopedia. Underneath, each product carries a TikTok Shop product id, SKU id and shop id next to its Tokopedia ones, plus a second category path from the TikTok Shop taxonomy. Anyone who wants to scrape Tokopedia today is reading a merged Indonesian catalogue, not a standalone one.
The public directory covers 29 top-level departments - handphone-tablet, komputer-laptop, kecantikan (beauty), makanan-minuman (food and drink), otomotif and the rest - fanning out through roughly 370 sub-categories into close to three thousand leaf categories.
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Tokopedia price data answers questions a single storefront cannot. Because a product lives inside a shop rather than in a shared catalogue, one model is listed by many sellers at once: a single result page for a mid-range phone returned 53 distinct shops, with asking prices more than a third apart. That spread is the dataset.
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Learn MoreLearn how to use web scraping to solve data problems for your organization
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.
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.
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 is a team of web scraping engineers. Our Tokopedia web scraper services turn an awkward marketplace into clean datasets and structured reports: we handle the setup, the code and the proxy management, and you receive data you can load and analyse the same day. Tell us the fields, the categories and the cadence, and the feed arrives ready to use - no infrastructure to run on your side.
We collect data based on your request. It can include everything available - title, price, discount, variants, stock, condition, weight, description, specifications, rating, reviews and images - or just the fields you name.
Yes. We run the collection on a schedule and record price, discount and campaign windows per seller, so you get a price history rather than a snapshot.
CSV, Excel or JSON - depending on your needs. Delivery can go to email, cloud storage or an endpoint on your side.
No. We handle both small batches and large-scale extractions. Category listings are capped in depth, so we split coverage across filters such as city, price band, condition and store tier.
Yes. The ScrapeIt Tokopedia API returns product id, price and discount in rupiah, stock, units sold, rating and shop tier as JSON from an endpoint we host, refreshed on the schedule you set. The same data also comes as CSV or Excel files or straight into your database.
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.
Scrapeit Sp. z o.o.
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