How we collect and normalize cannabis market data

We read dispensary menus, the product listings that dispensaries publish on their websites and through their e-commerce platforms. From each menu we capture:

• Products listed and their descriptions
• Prices (shelf price, unit price, weight-tiered pricing)
• In-stock / out-of-stock availability at the time of collection
• Product categories and subcategories as the dispensary lists them
• Brand attribution where present
• THC/CBD potency data where disclosed

Automated collection from 10,500+ dispensary websites and menu platforms across 40+ US & Canadian markets. We read publicly accessible menus, the same pages any customer would see.

Menus are typically refreshed every 1–2 hours. Collection frequency varies by platform and state. Some dispensaries update their menus in real time; others batch-update daily. Our timestamps reflect when we last successfully read a given menu.

Raw menu data is messy. The same product appears under dozens of different names across different dispensaries. We run a normalization pipeline to make the data actually useful:

• Products are matched and deduplicated across dispensaries
• Brand names are standardized (e.g., "Wyld Gummies" and "WYLD" resolve to the same brand)
• Categories and subcategories are mapped to a consistent taxonomy regardless of how each dispensary labels them
• Prices are normalized to a per-unit or per-gram basis where applicable
• Duplicate listings (same product, same store, different SKU format) are collapsed

We are not a POS data provider. We do not have access to:

• POS transaction records or actual unit sales counts
• Foot traffic or customer visit data
• Customer demographics or purchase history
• Private wholesale pricing or distributor invoices
• Seed-to-sale tracking system data (METRC, BioTrack, etc.)

Our estimates of market presence are based on menu listings and pricing signals not register data. When we say a brand has strong market presence, we mean it appears on many dispensary menus at strong prices, not that we have verified sell-through numbers.

Coverage

Accuracy

"We publish our data in public leaderboards specifically so you can verify it. If something looks wrong, tell us."

Our normalization pipeline makes mistakes. Brand matching is imperfect. Some dispensary menus are inconsistently formatted. Prices occasionally reflect promotions that have since ended. We work continuously to improve accuracy, and we rely on users who know their markets to flag errors.