I’ve been working on a dataset that captures weekly pricing behavior from online brand storefronts.<\/p>\n
What it is:<\/p>\n
– Weekly snapshots of pricing data from 500+ DTC and e-commerce brands<\/p>\n
– Structured schema: current price, original price, discount percentage, category<\/p>\n
– Historical comparability (same schema across all snapshots)<\/p>\n
– MIT licensed<\/p>\n
What it’s for:<\/p>\n
– Pricing analysis and benchmarking<\/p>\n
– Market research on e-commerce behavior<\/p>\n
– Academic research on retail pricing dynamics<\/p>\n
– Building models that need consistent pricing signals<\/p>\n
What it’s not:<\/p>\n
– A product catalog (it’s behavioral data, not inventory)<\/p>\n
– Real-time (weekly cadence, not live feeds)<\/p>\n
– Complete (consistent sample > exhaustive coverage)<\/p>\n
The repo has full documentation on methodology, schema, and limitations. First data release is coming soon.<\/p>\n
GitHub: https:\/\/github.com\/mranderson01901234\/online-brand-pricing-snapshots<\/a><\/p>\n Source and full methodology: https:\/\/projectblueprint.io\/datasets<\/a><\/p>\n<\/div>\n submitted by \/u\/operastudio <\/a>
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