Tutorials, industry analysis, and best practices for ecommerce data extraction, API integration, and competitive intelligence.
Three myths about the SellerMagnet sales estimate, checked against Amazon's own documentation and the API's code: it is not an exact count, most ASINs never get the 'bought in past month' figure at all, and a low estimate usually means thin signal, not zero sales. Two tested scripts show how to tell which path produced a given number.
Find out which Amazon products peak in Q4, in summer or not at all: compare each month's sales rank with the year around it, keep only peaks that repeat every year, check them against Amazon's bought-past-month badge and plan stock before the peak - one credit per ASIN, in Python.
Turn a supplier's barcode-and-cost sheet into an Amazon shortlist: match each EAN or UPC to an ASIN, read the Buy Box price, the number of sellers and whether Amazon sells it, estimate monthly units and subtract VAT, fees and cost - up to three credits per line, in Python.
Build your own Amazon Movers & Shakers for any category: save the top 50 bestsellers once a day, compare with yesterday using Amazon's own gain formula, and list risers, fallers, new entries and products that dropped out - one credit per category per day.
Find products that reached an Amazon bestseller list within weeks of launch: read the top 50 of a category, look up each ASIN's Date First Available once, cache it, and filter by age - about 51 credits for the first run and a handful per week after.
One request returns an estimate of monthly units for any ASIN, anchored on Amazon's own 'bought in past month' figure where it exists and on sales rank, reviews and listing age where it does not. What the number means, how to check it against recorded sales, and how to rank a category by it.
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