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Industry — Retail & Commerce

Retail AI Data
From Real Stores

Retail AI models trained on simulated environments fail when deployed in real stores. DATALENT captures from real retail environments giving your models the distribution they will actually encounter.

Datasets

What We Offer for Retail & Commerce

Visual Commerce
Retail Shelf & Product Data
8M product images from real retail shelves. Varying angles, occlusion, and packaging variants across 500K SKUs.
📦 8M images🛒 500K SKUs
  • Product ID + category hierarchy labels
  • Shelf position and facing annotations
  • Multi-angle per product (avg 16 angles)
Shopper Behavior
In-Store Customer Behavior
Anonymized shopper behavior sequences: dwell time, product interaction, path tracking, and purchase decisions. GDPR-compliant.
📦 4.8M sequences🔒 GDPR
  • Dwell time and interaction annotations
  • Purchase vs. no-purchase outcomes
  • Store zone and category mapping
Demand Forecasting
Transaction & Demand Signals
Anonymized transaction event streams for forecasting, pricing optimization, and inventory management.
📦 12B events📈 Time-series
  • Transaction events with product hierarchy
  • Price elasticity signals
  • Promotional lift annotations

Build Retail AI That Works in Real Stores

From visual search to demand forecasting, your retail AI needs data from real retail environments.

Talk to Our Team →Browse Datasets
FAQ

Retail Data Questions

How is shopper behavior data captured without violating privacy?
All shopper behavior data is collected under explicit opt-in consent from shoppers participating in retail research programs. Personally identifiable information is removed before data leaves the retail environment. We capture behavioral signals — dwell time, interaction sequences, path patterns, purchase outcomes — not biometric identifiers. GDPR and CCPA compliance documentation is provided with every shopper behavior dataset.
Can product recognition data be filtered by retail category or region?
Yes. Our product dataset covers 500K SKUs across all major retail categories: grocery, electronics, apparel, home goods, health and beauty, and more. Data can be filtered by category, geographic region, retail format (hypermarket, convenience, specialty), and time period. Regional filtering is useful for models targeting specific markets where product presentation and assortment differ significantly from global norms.
Scale

Retail Dataset Statistics

📷
8M+
Product Images
🛒
500K
Distinct SKUs
👤
4.8M
Shopper Sequences
📈
12B
Transaction Events