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

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)

Data schema

All required and optional fields are listed below.

Field Name	Required	Type	Format	Description	Alternative Names	Example
`productId`	✅ Yes	string	Text	Product identifier linked to your Products dataset	product_id, sku_id, item_id, product_code	P001
`datetime`	✅ Yes	datetime	YYYY-MM-DD HH:MM:SS	Date & time of price observation	date, timestamp, observed_at, price_date	2026-02-22 14:35:00
`competitorName`	✅ Yes	string	Text	Competitor / retailer name	competitor, seller, shop_name, retailer	Bauhaus
`regularPrice`	✅ Yes	float	Dot separator	Competitor regular (non-discounted) price	price, list_price, base_price	125
`promoPrice`	⬜ No	float	Dot separator	Competitor promotional price (if applicable)	sale_price, discounted_price, promo_price	119.99
`currency`	⬜ No	string	ISO 4217	Currency code	curr, currency_code	EUR
`url`	⬜ No	string	URL	Source product page	link, product_url, source_url	https://bauhaus.fi/product123
`barcode`	⬜ No	string	Text	EAN / UPC barcode	ean, ean13, upc, gtin	5901234123457
`sku`	⬜ No	string	Text	Competitor SKU	competitor_sku, seller_sku	SKU-1001
`status`	⬜ No	string	Text	Lifecycle status (active / discontinued)	availability_status, product_status	active
`stock`	⬜ No	float	Numeric	On-hand inventory quantity (if available)	inventory, stock_qty, quantity_available	125

You may add additional attributes as separate columns.

Product matching

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

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

Provide all three when possible. Provide productId 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.

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

Currency handling

If all data is in one currency → no conversion required
If multiple currencies are present → FX conversion rules must be configured

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

Time-series behavior

Competitor Prices are stored 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.

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:

productId	datetime	competitorName	regularPrice

Validation rules

Upload will fail if:

Required fields are missing
datetime format is invalid
regularPrice is empty or non-numeric
Matching with Products dataset fails
Duplicate identical rows are detected

Example row

productId	datetime	competitorName	regularPrice	promoPrice	currency	url	barcode	sku	status	stock
P001	2026-02-22 14:35:00	Bauhaus	125	119.99	EUR	https://bauhaus.fi/product123	5901234123457	SKU-1001	active	125

Data Requirements

Integrations

Dataset structure

Data schema

Product matching

Price interpretation logic

Currency handling

Time-series behavior

Data format requirements

Validation rules

Example row

Data Requirements

Integrations

Documentation Index

​Dataset structure

​Data schema

​Product matching

​Price interpretation logic

​Currency handling

​Time-series behavior

​Data format requirements

​Validation rules

​Example row

Dataset structure

Data schema

Product matching

Price interpretation logic

Currency handling

Time-series behavior

Data format requirements

Validation rules

Example row