Frequently asked questions about Data Purl
Answers to the questions investors, private equity teams and consultants most often ask about Data Purl: what the data is, what it covers, how it differs from scraped and transaction data, how it is delivered and how to cite it.
What is Data Purl?
Data Purl is an alternative data company that turns public retailer and brand websites into investment-grade data. It tracks SKU-level prices, discounts, availability and assortment weekly across 400+ retailers, standardizes every product into one category tree, and maps brands to their owners, public or private, with listed owners mapped to tickers. It serves hedge funds, long-only investors, private equity firms and consultants. Data Purl is a trading name of Cleveland Alternative Data Limited, a UK company.
Is Data Purl the investor dataset formerly known as WGSN?
Yes. Data Purl's core dataset launched to investors in 2018 and became widely known in the market as WGSN. It then passed through Ascential, Flywheel and Omnicom; when Omnicom shifted its focus to serving brands rather than investors, the same team spun out with the dataset and founded Data Purl in 2025. Same core dataset and same team, with a great deal built on top of it since, including data from many more approved sources. Data Purl is an independent company and is not affiliated with WGSN today.
Can Data Purl show one brand across all the retailers that sell it?
Yes. This is a core part of the dataset. Data Purl tracks the same brand at each of the 400+ retailers it covers, and on the brand's own website where tracked, with every product placed in one category tree. That makes a brand's price, discounting, availability and range directly comparable retailer by retailer: which retailers are adding or dropping the brand, where it is discounted hardest, and whether wholesale partners hold price as well as its own site does. Brands are resolved to their owners, public or private, so the view rolls up to the owning company.
What data does Data Purl provide?
Weekly product-level data on listed price, full price, discount, availability, first-seen and last-seen dates, category and brand, for products at 400+ retailers across the US, Europe and Asia. It is delivered as granular datafeeds, as aggregated company and category analytics, and as scoped extracts.
Who uses Data Purl?
Fundamental and quantitative investors at hedge funds and long-only firms, sell-side analysts, private equity deal and operating teams, and strategy consultants working on commercial due diligence and pricing projects.
How is Data Purl different from buying raw web-scraped data?
Raw scraped data gives you one retailer's listings as they appear on its site. Data Purl adds the transformation layer: every product is placed in one standardized category tree, brands are resolved to their owners, public or private, with listed owners mapped to tickers and ownership changes dated, and history is continuous back to 2013. That is the work needed before cross-retailer or company-level analysis is possible.
Can I use an AI assistant or agent to scrape retail prices instead of buying data?
For a spot check of a few product pages today, yes. For investment work, no: an AI scrape has no history, samples different pages on each run, cannot separate price changes from range changes, and is not mapped to listed companies. Data Purl provides continuous weekly history back to 2013 across 400+ retailers, same-SKU measurement and brand-to-ticker mapping, so changes can be compared with a year ago and tested against reported results.
How is Data Purl different from transaction or receipt data?
Transaction, card and receipt panels are demand-side: they measure what consumers bought. Data Purl is shelf-side: it measures what was listed, at what price, with what discount and availability. The two answer different questions and are often used together.
How many retailers and companies does Data Purl cover?
400+ retailers across the US, UK, Europe and Asia, with history back to 2013. More than 1,900 public companies are mapped, and products are organized into about 4,000 categories in one tree.
How far back does Data Purl's history go?
Back to 2013, with continuous weekly history.
How often is the data updated?
Weekly. Each Monday-to-Sunday week is delivered by Wednesday afternoon New York time (about 4pm Eastern, Wednesday evening in the UK).
Which sectors are covered?
Apparel, footwear, luxury, pet, home improvement and home furnishings are live. US grocery is rolling out by Thanksgiving 2026, with beauty and sports and outdoor following.
Does Data Purl restate historical data?
Yes, deliberately. Structured Datafeeds, Ticker Analytics and Sector Analytics are rebuilt from the full history back to 2013 every week, so cleaning and classification improvements apply to past weeks too. Each product has one current category applied to its whole history, so category series never break when a product is reclassified. Links from brands and retailers to listed owners are point-in-time, with dated start and end, so ownership changes carry no look-ahead bias. SKU- and product-level weekly metrics recompute the current and previous calendar years each week. Published Shelf Indices and brand headlines are versioned, and any change to previously published figures is noted in the release.
How is the data delivered?
By Snowflake data share, Amazon S3, the Data Purl portal, or through partner platforms.
Is web-collected data compliant for investment use?
Data Purl collects only publicly available information from retailer and brand websites. It does not collect personal data. Its collection and processing practices have been through more than 100 client compliance reviews. See the compliance page for the full policy.
Can Data Purl measure inflation for a specific product category?
Yes, as a category-specific measure of online listed prices at covered retailers. Data Purl tracks identical products over time (matched-SKU) and can normalize to price per unit to capture shrinkflation.
Can I buy data for a single project?
Yes. Extracts can be scoped to a specific set of brands, retailers, categories, countries and dates, with the size estimated before anything is pulled.
How do I cite Data Purl?
Cite Data Purl as the source and scope the claim: the retailers covered, that prices are online listed prices, the basis (for example matched-SKU or price per unit) and the date range. Our methodology page describes each measure.
See the data for your coverage
Start free in the analytics portal, or talk to the founders about the full dataset delivered to Snowflake or S3.