R1 — Build a searchable, structured catalogue when records are numerous, change often, or carry several independent queryable attributes

Rule: Recommend building a searchable, structured catalogue when ANY of the following are true:

1. Catalogue size: the visitor would otherwise have to scan past a few hundred items.
2. Change frequency: the data updates often enough that maintaining a hand-edited document is error-prone (and risks losing Google rich-results eligibility — Google Search Central — "We won't show a rich result for time-sensitive content that is no longer relevant"; data freshness is implicitly rewarded).
3. Attribute richness: items differ along multiple independent dimensions (size, type, location, spec, date, price) that people genuinely filter on.

Why: The payoff is documented — Baymard Institute (2015) — sites with mediocre product list usability saw 67-90% abandonment vs 17-33% for sites with even a slightly optimised toolset shows the magnitude gap (67-90% vs 17-33% abandonment). The mechanism is the independent-dimensions framing (Nielsen Norman Group — faceted search refines a large content set; controls + results displayed simultaneously, Algolia (vendor) — faceting helps when catalogs have multiple specifications and broad-based filtering is insufficient).

How to apply:

• The real trigger is the interaction of size × change frequency × attribute richness — no source quantifies this jointly. Use the three legs as a checklist.
• Pair with R2 — Skip faceted search when the inventory is small (~under 200 items), stable, and shallow; a static list is fine for the don't-build side.
• Honest framing: this is a recommendation grounded in mechanism + e-commerce magnitude evidence, not a universally-validated cutoff. See Caveats for the searchable-catalogue brief: no study isolates catalogues as a variable; vendor-sourced skip thresholds; Baymard age; AI-eligibility under-sourced.