Define a comparable unit
Choose products and fields: SKU, variant, package, price, and stock. Configure a scenario for each source site.
For category managers, online stores, and manufacturers
A price means little without the size, package, and availability. Bring the fields together to review changes in the products your team works with.
A price changed, but so did the volume, bundle, or terms. Comparing names alone turns those differences into false signals.
Start with a small sample. Then control the rules, frequency, and result.
Choose products and fields: SKU, variant, package, price, and stock. Configure a scenario for each source site.
Review variants and values in a trial run. Normalize SKUs and packages in the scenarios, then match offers from different sites after exporting.
Set a schedule and export for your system. Check shipping, discounts, and offer terms before changing a price.
Your team gets prepared data and more time to make decisions.
Keep SKU, variant, and package beside the price. Your team can tell a changed offer from a different configuration.
Repeated collections preserve offer details. Include the check date and source link with the result.
Export the slice you need by category, availability, or brand. The table retains the facts behind a decision.
Choose fields and processing rules for your task. Each record can retain its source link and check date.
Open the guideYes. Each site needs a collection scenario. Normalize SKUs, variants, and packages, then match offers on those fields in your system or during export processing.
Keep the regular and promotional prices in separate fields, along with discount conditions. This prevents a member-only or coupon price from being mistaken for a public price.
Start with one category and a short list of important fields. Verify the result before expanding the selection and adding regular updates.
Explore an example, then configure your workflow.
Account for sizes, packages, and missing items when row counts alone prove little.
SKU, price, availability, and region: a minimum structure you can verify against the source.
Create a scenario for the source and check the result before recurring runs.
Test one source, review the result, and choose how to run it. Expand the process with your team when it works.