Add dataset card

README.md

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+---
+license: cc-by-4.0
+pretty_name: Product Tagging and Saved Intent in Online Retail
+language:
+- en
+tags:
+- ecommerce
+- product-taxonomy
+- tagging
+- wishlists
+- shopify
+- steam
+- structured-data
+- agentic-commerce
+- web-measurement
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: shopify_stores
+  data_files: data/shopify_stores.csv
+- config_name: wishlist_verification
+  data_files: data/wishlist_verification.csv
+- config_name: steam_games
+  data_files: data/steam_games.csv
+- config_name: steam_tag_applications
+  data_files: data/steam_tag_applications.csv
+---
+
+# Product Tagging and Saved Intent in Online Retail
+
+How online stores structure product tags and implement saved-item features, and
+how that compares with a platform where tagging is crowd-sourced against a
+shared controlled vocabulary.
+
+**Canonical release:** https://doi.org/10.5281/zenodo.22852049
+This repository mirrors that deposit. Cite the DOI.
+
+## Sample
+
+1,028 Shopify storefronts, drawn by seeded random sample from a 30,000-domain
+draw of the Tranco top 1M, measured 13 to 14 September 2026 with a 95.6%
+collection rate. 700 Steam titles as a contrast case.
+
+## Findings (95% Wilson intervals)
+
+**Every store invents its own private language.** 963 Shopify stores used
+30,622 distinct tags between them, and the median store shares just **16.7%**
+of its tags with any other store. 700 Steam titles drew on 394 distinct tags,
+and the median title shares **100%**.
+
+| Measure | Value |
+|---|---|
+| Stores carrying wishlist code | 46.0% (43.0 - 49.1) |
+| Stores with a *confirmed live* save control | 28.1% (25.5 - 30.9) |
+| Of active wishlists, no back-in-stock app | 34.9% (29.7 - 40.6) |
+| Of active wishlists, saving requires login | 19.4% (15.2 - 24.3) |
+| Stores publishing Product JSON-LD | 79.1% (76.5 - 81.5) |
+| Stores with all 12 core agent attributes | 0.1%, one store in 1,028 |
+
+Confirmed wishlist presence rises across tagging-depth quartiles (17.9%, 26.8%,
+27.2%, 40.5%; Cochran-Armitage z=5.43, p=5.6e-08). Association, not mechanism:
+both are plausibly downstream of a merchant who invests in their catalogue.
+
+## The 46% versus 28% gap is the point
+
+Detecting wishlist *code* is easy. Confirming a shopper can actually save
+something is not. A second verification pass re-fetched every candidate and
+recorded the evidence snippet behind each verdict, which ships in
+`wishlist_verification.csv`.
+
+Anyone measuring this by pattern-matching page source alone overstates working
+wishlists by roughly 18 percentage points. Use `wishlist_verified_state`, never
+`wishlist_code_detected`.
+
+## Configs
+
+| Config | Rows | What it is |
+|---|---|---|
+| `shopify_stores` | 1,075 | one row per store; 47 rows with `collected=false` are kept so the collection rate is auditable |
+| `wishlist_verification` | 473 | per-store verdict with the evidence text behind it |
+| `steam_games` | 700 | one row per title; `n_tags_shown` is censored at Steam's top 20 |
+| `steam_tag_applications` | 13,447 | one row per tag applied, with vote count |
+
+Also included: `results/stats.json` with every published figure and interval,
+all collection and analysis code, and `METHODS.md` with a ten-point limitations
+section.
+
+## Reproducible in the browser
+
+A runnable notebook recomputes every figure above from these files:
+https://www.kaggle.com/code/sempite/reproducing-the-tagging-and-wishlist-findings
+
+## Method notes worth knowing
+
+Shopify rate-limits `/products.json` **per IP across all endpoints**, so
+collection is paced by one global limiter. Collection health was gated
+separately from analytical correctness, after an earlier run passed arithmetic
+verification while resting on only 47 of 1,075 stores.
+
+## Citation
+
+```bibtex
+@dataset{gerrish_tagging_2026,
+  author    = {Gerrish, Jacob Ezra},
+  title     = {Product Tagging and Saved Intent in Online Retail: a measurement
+               study of 1,028 Shopify storefronts and 700 Steam titles},
+  year      = {2026},
+  publisher = {Zenodo},
+  doi       = {10.5281/zenodo.22852049}
+}
+```
+
+Write-ups: [tag vocabularies](https://sempite.com/research/product-tag-vocabularies/) ·
+[wishlists](https://sempite.com/research/ecommerce-wishlists/)
+
+Data CC BY 4.0, code MIT. Produced by [SEMPITE](https://sempite.com/research/).