Imagine you have 2,000 SKUs and sell across three channels: your own store, Allegro and Amazon. You just received a new collection from your supplier - 150 products, data files in Excel, images in a folder on WeTransfer.
To get these products into sales you need to:
Upload data to the store with your own attribute structure
Create listings on Allegro with filled category parameters (which differ from your store)
Create listings on Amazon with ASIN, bullet points and A+ Content (which have yet another set of requirements)
Resize images for each channel separately - because Allegro, Amazon and the store have different size requirements
How long does this take? A colleague of mine who has been integrating sellers with marketplaces for years says that with a smooth process it is a minimum of 2-3 hours per 10 products. 150 products is 30-45 hours of work. Several weeks of time-to-market before the new collection reaches sales at all.
And that is exactly the problem PIM solves.
Why each channel requires different data
Before getting to the solution - it is worth understanding where this mess comes from. It is not a matter of bad intentions from the marketplaces. Each platform has its own data model, its own category taxonomy and its own completeness requirements.
Allegro in 2026 has significantly tightened listing quality requirements. A new AI-based recommendation algorithm evaluates listings on completeness of category parameters, media quality and taxonomy compliance. A listing without required category parameters is simply less visible - the algorithm treats it as less credible. On top of that, Allegro has its own category and attribute naming that does not map 1:1 to what you have in your store.
Amazon is a world of its own. Products must be linked to an ASIN (Amazon Standard Identification Number) - if the product already exists in Amazon's catalog, you attach yourself to the existing listing. If not - you create a new one with full specification. Then there is A+ Content, bullet points in a specific format, image requirements (white background, minimum 1000px on the longer edge), category-specific attributes. Amazon has its own product data language and is not forgiving of inconsistencies.
Your own store has its own structure - categories, attributes, variants - built around your product presentation logic, not external platform requirements.
Result: the same product lives in three different data representations, manually maintained in three different places. A price change in the store does not update it on Allegro. A new image uploaded to the store does not reach Amazon. Updating a technical specification requires work in three systems separately.
How PIM solves this architecturally
PIM introduces one change in data architecture: instead of three separate sources of truth - one.
Product data lives in PIM. Each channel has its own export profile that maps data from PIM to the requirements of a specific platform. Data changes in PIM propagate to all channels on the next sync.
In practice it looks like this:
In PIM you create a product once with a full set of attributes - shared and channel-specific
You define the mapping: attribute "color" in PIM → parameter "Colour" on Allegro, attribute "color" on Amazon, field "color_attribute" in the store
You define completeness requirements per channel: a product goes to Allegro only when Allegro category parameters are filled plus minimum 4 images. To Amazon when it has ASIN, bullet points and a white-background main image
BaseLinker or a dedicated connector pulls ready data packages from PIM and distributes them to platforms
For a new product from a supplier: you upload data to PIM once. The system checks completeness for each channel and shows what is missing. You fill the gaps in one place. The product goes to all channels simultaneously.
What you gain in concrete numbers
A colleague of mine in marketplace integrations calculated this on one project - a company with a 3,000 SKU catalog selling on their own PrestaShop store, Allegro and Amazon:
Before PIM: onboarding a new collection of 100 products took 3 people a week. Data errors (wrong Allegro category, missing Amazon attributes, inconsistent descriptions across channels) generated a dozen or so hours of correction work every month.
After PIM implementation: onboarding 100 products is 1-2 days of work for one person. Systematic errors disappeared - the system will not release a product to a channel without meeting completeness requirements. Time-to-market shortened from a week to two days.
PIM vs BaseLinker - what does what
This question comes up very often and is worth settling directly, because these are two different tools that should work together, not instead of each other.
BaseLinker manages the sales process: orders, stock levels, shipping, returns handling, buyer communication. This is the operational layer - what happens after the customer clicks "buy".
PIM manages product data: what it is, what it looks like, what attributes it has, how it is described per channel. This is the information layer - what the customer sees before they click "buy".
The optimal stack for a multichannel seller: PIM as the product data source → BaseLinker as the distribution and sales operations layer → marketplaces and store as end channels.
BaseLinker pulls data from PIM and pushes it to platforms. Orders from platforms go into BaseLinker and on to fulfillment. PIM knows nothing about orders, BaseLinker does not care how a product is described - each tool does its job.
Image requirements - the underappreciated pain
A separate topic that deserves a moment: product images on marketplaces are not "upload and forget".
Allegro requires a minimum of 1 image, recommends 8+, prefers white background for the main image, has file size limits. Amazon requires white background for the main image (standard - pure white, RGB 255,255,255), minimum 1000px on the longer edge, accepts up to 9 images per listing. Your own store may have different cropping ratios and a lifestyle shot as the main image.
PIM stores images at the highest available resolution and exports them per channel with appropriate parameters. The completeness indicator ensures a product does not reach Amazon without a white-background image, and Allegro without the minimum required number of images.
This eliminates a whole class of errors that are hard to catch manually with large catalogs - and that on Amazon can end with a listing rejection or reduced visibility.
When PIM makes sense for a marketplace seller
Not every marketplace seller needs PIM. An honest answer to when implementation makes sense:
You sell across at least two channels simultaneously and both require active data maintenance
You have more than 500-1,000 SKUs and regularly (monthly or more often) add new products
Onboarding a new collection takes more than 2 working days
You have or plan expansion to foreign markets (different languages, different platform requirements)
Data errors (wrong category, missing attributes) are a recurring source of problems
If you have 200 SKUs and sell only on Allegro - Excel and BaseLinker are enough. PIM is an investment that pays off at scale.
What this looks like in practice with UnoPIM
UnoPIM as the foundation for a multichannel seller is a concrete architecture: UnoPIM as the product data master, PrestaShop connector for the own store (official, extended to project requirements), BaseLinker integration via API for marketplaces.
During implementation we define together: which attributes are shared across all channels, which are channel-specific, how mapping to Allegro and Amazon requirements looks, what completeness thresholds are for each channel before sending.
Result: one panel where the product team works. One quality control point for data. One onboarding process for new products - regardless of the number of sales channels.
Have a specific setup (how many SKUs, how many channels, what integrations) and want to know if implementation makes sense for your scale? Write to me - first conversation is free →
