The hard part of competitor pricing isn't scraping. It's matching.
Competitor prices change daily. Claro matches them to your own catalog, even without shared identifiers, so pricing and procurement teams act on accurate, current data instead of a spreadsheet someone matched by hand.
Price monitoring is only as reliable as the matching underneath
Scraping a competitor storefront is the easy part; matching what comes back to your own SKUs is where most pricing programs quietly fail. Own-label and exclusive products have no GTIN to match on, the same product ships in different pack sizes across retailers, and a wrong match silently corrupts a pricing decision instead of failing loudly. Claro resolves product identity across retailers using graph relationships, embeddings and attribute-level evidence, scores every match, and only pushes high-confidence results straight into your pricing systems.
Where competitor pricing programs actually break
Own-label and exclusive SKUs have no GTIN or UPC to match against.
The same product ships in different pack sizes across retailers, so a naive match compares the wrong pair.
Manual matching is slow and expensive, and it breaks the moment you try to scale it.
How price monitoring works with Claro
Competitor listings, prices and availability, at any frequency.
Product identity resolved across sources without shared identifiers.
Every match confidence-scored, with attribute-level evidence.
Validated prices flow into pricing, procurement and BI tools.
Who runs monitoring this way
What goes in, what comes back
Claro writes back through files and APIs rather than certified connectors, so this list is a guide, not a limit.
Pricing monitor: common questions
How often can prices be collected?
From hourly to weekly, depending on the category and how fast prices actually move. Collection frequency is a setting; the matching underneath does not change with it.
How do you handle bundles and pack sizes?
They are normalised before comparison, so a single unit is not compared against a pair and a 37-litre case is not compared against a 48-litre one. This is the most common source of silently wrong price comparisons.
What about products with no shared identifier?
That is the normal case for own-label and exclusive ranges. Matching runs on attributes and product identity rather than GTIN, and every match carries a confidence score.
Does a low-confidence match still reach our pricing system?
It depends on your configured rules, the match's confidence score and the operational risk if it's wrong. Low-confidence or high-impact matches go to review, because a wrong match corrupts a pricing decision silently instead of failing loudly.
Is this a scraping product?
Collection is the commodity part. What Claro adds is resolving each listing to the correct product in your catalog, which is where most price-monitoring programmes lose their accuracy.
Related work
See it work on your own catalog.
Bring one supplier file and we'll run pricing monitor on your real data — matched, classified and reviewable.