Data Purl
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  • Platform
    • Coverage400+ retailers, history back to 2013
    • Shelf IndicesFree monthly price and discount indices
    • Brand headlinesQuarterly prices for 84 consumer brands
    • Custom dataExtracts scoped to your sectors and dates
    • How we build it
      MethodologyHow we collect, classify and map brands
    • ValidationQuality screens and backtests
  • Solutions
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  1. Home
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  3. /Tracking a company's pricing power with eCommerce data
Use case

How can investors measure whether a consumer brand has pricing power before it reports?

Pricing power shows up online as price increases that stick without extra discounting. The clearest read combines matched-SKU price changes (are identical products getting more expensive?) with markdown breadth and depth (is the brand giving the increase back through promotions?), tracked weekly across the brand's own site and its wholesale partners.

For:Public market investorsPrivate equity

Last reviewed 24 September 2026

What pricing power looks like in the data

  • Matched-SKU prices rising faster than the category, so the brand is raising prices on the same products.
  • Markdown breadth flat or falling versus the same weeks last year, so the increase is not being discounted away.
  • Full-price availability holding up, so products still sell out at the new price.
  • Consistency across channels, so wholesale partners are not discounting what the brand's own site holds.

A practical workflow

  1. 1Resolve the company to all of its brands and select the brands that matter for revenue.
  2. 2Pull weekly matched-SKU price changes by brand and category, normalized per unit where pack sizes vary.
  3. 3Overlay markdown breadth and discount depth for the same weeks last year to remove seasonality.
  4. 4Split the brand's own site from wholesale retailers to see who is carrying the discounting.
  5. 5Compare against peers in the same categories to separate brand strength from category-wide inflation.

Where it fits in an investment process

Fundamental analysts use it to test management commentary on pricing and promotions between reporting dates. Quantitative teams use the same series as inputs to gross-margin and revenue-per-unit models.

Metrics used

  • Matched-SKU inflation (same-SKU price change): The price change of identical products observed in both periods, which isolates true price moves from changes in product mix.
  • Markdown breadth: The share of each product's in-stock sizes and variants selling below full price, averaged across products so each product counts equally.
  • Discount depth: The average percentage reduction from full price across products that are on sale.
  • Full-price availability: The share of a brand's products that are both in stock and selling at full price, a proxy for clean, healthy demand.
  • DTC vs wholesale mix: How a brand's online assortment and pricing split between its own direct-to-consumer site and its wholesale retail partners.

How each measure is built: methodology.

See the data for your coverage

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Data Purl

Data Purl is an eCommerce pricing and assortment data provider that follows every brand across the retailers that sell it: SKU-level price, discount, availability and assortment history across 400+ retailers back to 2013, compared like for like, with brands resolved to their owners, public or private, and listed owners mapped to tickers.

+44 20 8125 3010 (UK)+1 212-660-0780 (US / International)

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