Tutorials, industry analysis, and best practices for ecommerce data extraction, API integration, and competitive intelligence.
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Measure how strongly an Amazon product reacts to price: find every Buy Box price change of 5 % or more that held for more than a week, compare the median sales rank of the week before and after, and sort products into price-sensitive or not - one credit per ASIN, in Python.
Deep dives into Amazon data extraction, API best practices, and ecommerce intelligence.
Check whether Amazon deals are real reductions: compare each deal price with the lowest Buy Box price of the 30 and 90 days before the deal started, and flag 'was' prices that were never the Buy Box price - one deals call plus one history call per deal, in Python.
Audit an Amazon listing against the listings that rank for its keyword: check the title against Amazon's January 2025 title rules and compare images, bullet points, video, A+ content and ratings with the top five organic results - seven credits per audit, in Python.
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.
Monitor MAP on Amazon with one call per ASIN: check the New offers on the first page of each offer listing against your minimum advertised price, flag sellers who are not on your authorized list and keep a dated, append-only log with seller IDs, prices and first-seen dates for follow-up - 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.
Decide which European Amazon store to launch a product in: check each store for the listing, the number of New sellers, whether Amazon sells it, the featured price and estimated monthly units - about 15 credits per product for ten stores, in Python.
Monitor your Amazon competitors with one daily report: Buy Box price and holder, number of sellers, rating and sales rank for each competitor ASIN, written to CSV, with a list of what changed since yesterday - two credits per competitor per day.
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.
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