Data Purl vs AI web scraping
An AI assistant or scraping agent can tell you what a retailer's page says today. Data Purl tells you what changed, compared with what, and whether it matters for the company:
- Continuous weekly history back to 2013.
- The same products tracked on a fixed schedule.
- Like-for-like, same-SKU measurement.
- Audited classification into one category tree.
- Brands mapped to listed companies.
Those are the parts a scrape run today cannot produce.
Side by side
History
AI web scraping: Starts the day you build it. No last year to compare against.
Data Purl: Continuous weekly history back to 2013, so every figure has a year-ago and a five-year range.
Consistency
AI web scraping: Reads whatever loads on the day. Blocked pages and layout changes quietly change the sample.
Data Purl: The same 400+ retailers on a fixed schedule. 95.9% of scheduled retailer-weeks captured without a gap over the last two years; the rest forward-filled and flagged.
Like-for-like
AI web scraping: An average of whatever was listed, so it moves when the range changes as well as when prices change.
Data Purl: Same-SKU growth: the same products compared a year apart, so a price move is separated from a change in the range. Totals are available alongside in the product.
Classification
AI web scraping: Categories assigned on the fly, untested, and liable to change from run to run.
Data Purl: One tree of 4,000 categories. In Hardlines & Consumables, 91% of products are verified or set by audited rules.
Company mapping
AI web scraping: Sees a brand name on a page.
Data Purl: Brands resolved to their owners, with 1,900+ public companies mapped to tickers and ownership changes dated.
Validation
AI web scraping: Nothing to test: there is no history to set against reported results.
Data Purl: Signals backtested against companies' reported results, with the charts published.
Reproducibility
AI web scraping: Ask again tomorrow and the answer can differ.
Data Purl: The same query returns the same number, with a documented method you can cite.
Compliance
AI web scraping: Each team has to vet its own collection.
Data Purl: Public product information only, no personal data, and a collection policy ready for vendor due diligence.
| AI web scraping | Data Purl | |
|---|---|---|
| History | Starts the day you build it. No last year to compare against. | Continuous weekly history back to 2013, so every figure has a year-ago and a five-year range. |
| Consistency | Reads whatever loads on the day. Blocked pages and layout changes quietly change the sample. | The same 400+ retailers on a fixed schedule. 95.9% of scheduled retailer-weeks captured without a gap over the last two years; the rest forward-filled and flagged. |
| Like-for-like | An average of whatever was listed, so it moves when the range changes as well as when prices change. | Same-SKU growth: the same products compared a year apart, so a price move is separated from a change in the range. Totals are available alongside in the product. |
| Classification | Categories assigned on the fly, untested, and liable to change from run to run. | One tree of 4,000 categories. In Hardlines & Consumables, 91% of products are verified or set by audited rules. |
| Company mapping | Sees a brand name on a page. | Brands resolved to their owners, with 1,900+ public companies mapped to tickers and ownership changes dated. |
| Validation | Nothing to test: there is no history to set against reported results. | Signals backtested against companies' reported results, with the charts published. |
| Reproducibility | Ask again tomorrow and the answer can differ. | The same query returns the same number, with a documented method you can cite. |
| Compliance | Each team has to vet its own collection. | Public product information only, no personal data, and a collection policy ready for vendor due diligence. |
What a scrape is good for
A quick scrape answers one question well: what does this page say right now? For checking a handful of prices once, it is fast and free, and there is no reason not to use it.
Investment questions are different. They ask what changed, against what, and whether it matters:
- Is this company discounting more than a year ago? That needs last year, collected the same way.
- Is the average price rising because of price increases or a richer range? That needs same-SKU measurement.
- Does this brand's markdown move the listed owner's margin? That needs brand-to-company mapping and a backtest.
- Will the same query give the same answer next quarter? That needs a fixed method and a fixed sample.
The evidence
Every claim on this page is backed by published figures. The validation page shows collection success, quality screens, classification confidence and backtests against reported results; the methodology explains how each figure is built; and the free Shelf Indices are built on same-product comparisons.
Questions
Can I use an AI assistant or agent to track 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.
Why can't I backtest data I scrape myself with AI?
A backtest needs history collected the same way before the results you are testing against. A scrape built today only starts today, so there is nothing to test. Data Purl's history is continuous and weekly back to 2013, collected on a fixed schedule with gaps flagged, which is what makes backtests against reported results possible.
When is a quick AI scrape good enough?
When you need the current price of a handful of products on a handful of sites, once. It stops being enough when you need a trend, a year-on-year comparison, a company-level figure, many retailers at once, or a number that someone else can reproduce.
Does Data Purl use AI?
Yes, for classification, with guardrails. Most product categories are verified or set by audited rules; a machine learning model classifies the rest only where its calibrated confidence is at least 0.75. Collection, measurement and company mapping follow documented, repeatable methods.
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.