Competitor prices

The Competitor Prices dataset allows you to upload, monitor, and analyze competitor pricing at SKU level over time. This dataset powers:

• Competitive positioning analysis
• Price gap and price index calculations
• Dynamic pricing automation
• Promo monitoring
• AI elasticity and competitive pressure modeling

Each row represents:

One competitor price observation for one product at a specific point in time

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Dataset structure

Competitor Prices is a time-series dataset. You can upload:

• Historical snapshots (6–24 months recommended)
• Daily / weekly price updates
• Automated feeds (scraping, Google Shopping, APIs)

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Data schema

All required and optional fields are listed below. * At least one identifier (itemId, barcode, or sku) must be present for product matching. itemId gives the highest match accuracy and is strongly recommended. You may add additional attributes as separate columns (CSV / XLSX upload only — see Loading via the API).

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Product matching

Retailgrid matches each competitor price observation to a product in your Products dataset using this priority order:

1. itemId — used first when present.
2. barcode (EAN / UPC) — used as fallback when itemId is missing.
3. sku — used as a final fallback when neither itemId nor barcode matches.

Provide all three when possible. Provide itemId at minimum — it gives the highest match accuracy and the fewest unmatched rows. Matching rules are configurable during dataset setup. Custom product codes and composite keys are also supported. Formatting tips:

• Ensure consistent formatting between datasets.
• Avoid leading/trailing spaces.
• Keep barcode values numeric without formatting.

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Competitor scope

By default every competitor here applies to all of your stores. If different stores, pricing zones, or sales channels face different rivals, scope each competitor to where it competes — see Assign competitors to stores, zones, and channels. Scoping drives which competitors per-store pricing rules and competitor-gap metrics use.

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Price interpretation logic

Retailgrid applies the following logic:

• If promoPrice exists → it is treated as the active selling price
• If promoPrice is empty → regularPrice is used
• If both exist → system calculates:
  • Discount %
  • Promo depth
  • Effective price
  • Price gap vs your price
  • Price index

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Currency handling

• If all data is in one currency → no conversion required
• If multiple currencies are present → configure FX conversion rules

Recommendation:
Upload competitor prices in the same currency as your product prices.

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Time-series behavior

Retailgrid stores Competitor Prices historically. This enables:

• Price change tracking
• Promo frequency analysis
• Volatility scoring
• Price war detection
• EDLP vs Hi-Lo classification
• AI elasticity modeling

Each upload should include a valid datetime value. Do not overwrite history.

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Data format requirements

Supported formats:

• CSV (recommended)
• XLSX

Encoding:

• UTF-8
• Dot (.) as decimal separator

Rules:

• One row = one product × one competitor × one timestamp
• Do not combine multiple competitors in one row
• Do not store historical prices in multiple columns

Correct example:

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Loading via the API

Competitor prices can also be loaded programmatically: POST /v1/competitor_prices/bulk for JSON records, or POST /v1/imports/competitor_prices for CSV import. JSON payloads use snake_case field names that differ from the CSV columns:

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Validation rules

Upload will fail if:

• datetime, competitorName, or regularPrice is missing
• No product identifier (itemId, barcode, or sku) is present
• datetime format is invalid
• regularPrice is empty or non-numeric
• Matching with Products dataset fails
• Duplicate identical rows are detected

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Example row