E-commerce Prices: Amazon, Taobao, AliExpress, Idealo

LMSlay3 days ago

Before ranking AliExpress search prices, make sure the rows describe the same purchase. For a few known listings, opening the original pages manually may be enough. Keep a small comparison record: listing URL, observation date, currency, destination, exact variant, quantity/bundle, offer eligibility, and which shipping/tax amounts remain unknown. AliExpress search-card prices are not a complete SKU-price array or confirmation of your selected variant’s checkout price. Keep missing information as unknown—not zero. Compare product subtotals only when their scope matches; leave delivered-cost comparisons unresolved when material costs are unquoted. Check the original offer rather than treating another search-card price as verification of a missing detail. Our existing manual checklist explains these distinctions: https://github.com/LMSlay/tubetext-examples/blob/main/recipes/aliexpress-price-context-review.md Disclosure: I maintain TubeText’s paid Actors. Prepared with AI assistance from public documentation; no live connector or Actor run was performed for this note. This is a review procedure, not supplier verification or a new integration.

LMSlay2 days ago

Reading the Amazon keyword example Our short Python example is for finding a bounded set of product candidates. It uses the keyword "air fryer", requests best_sellers sorting and sets maxItemsPerKeyword to 48. That input cap does not guarantee 48 returned items or complete market coverage. The console preview shows rank, price, boughtPastMonth and the first 70 characters of the title. Keep that preview separate from the full dataset: a shortened title can hide a packaging or model difference, and an omitted preview field does not prove the dataset lacks it. If you already have a UPC/EAN and need an exact product match, this example does not demonstrate that task. Review the original listing and the relevant product attributes before treating a keyword candidate as the same item. Likewise, confirm a metric's definition before labeling rank as a category BSR or a sales indicator as exact unit sales. Existing source: https://github.com/LMSlay/tubetext-examples/blob/main/python/amazon_best_sellers.py I maintain TubeText; this reading note was prepared with AI assistance. I reviewed the public source, without running it. The script calls an Actor directly, not this MCP connector. Reading the source requires no run; executing it can incur Apify/Actor charges, so check the current Store inputs and pricing first.