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chain-store-footprint-analysis

利用 Crawlora 门店数据集、实时连锁品牌定位器和 Google Maps 商业记录,核对指定地理范围内连锁门店的覆盖情况:支持分店计数、地理重叠、设施覆盖与覆盖缺口分析,并输出门店证据台账、去重后的区域/品牌矩阵与来源覆盖说明,保留来源标识、抓取时间与结果上限标记。

crawlora-org
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热度 43.77
最近更新 2026-10-13 09:47:58
分类:数据依赖:第三方 API / MCP 服务 / 代码执行应用场景:市场分析 / 报告撰写 / 研究

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add chain-store-footprint-analysis
Metadata
name
chain-store-footprint-analysis
description
Compare observed chain-store coverage using Crawlora Starbucks directories, live chain locators, and Google Maps business records. Use for branch counts, geographic overlap, amenity coverage, or footprint gaps within a defined area with source caps and identity uncertainty preserved.

Chain store footprint analysis

Access and scope

Set CRAWLORA_API_KEY to your Crawlora key. Prefer the listed Crawlora MCP tools when connected; otherwise use the bundled scripts/crawlora.sh, which sends x-api-key to https://api.crawlora.net/api/v1. Read reference/endpoints.md for exact methods and parameters. Check application code as well as HTTP status; payloads are inside data. Stop on authentication errors, back off on 429, and retry a transient upstream failure once. Bound calls/pages by the requested scope and credit budget. Retain source IDs, URLs, source dates, and observation/crawl times separately.

Map observed branches for specified chains and geographic boundaries. Establish brands/aliases, store versus kiosk/licensed location scope, cities or coordinate boundaries, amenity criteria, source set, and collection budget before counting. This describes observed supply, not sales, foot traffic, demand, or site profitability.

Discover branches under matched geographic scopes

  • Use stored Starbucks facets/search/nearby for brand-specific discovery and stable store_number. Dataset country is actual geography; market=us/ca records crawl-host provenance and must not be mistaken for a geographic filter. Preserve exact country/state/city and coordinate evidence.
  • Refresh selected locations with live Starbucks locator/nearest-store calls. Either place or a coordinate pair is needed. Its cap is 50 without pagination; result_capped means the local result is incomplete. Distinguish a failed geocode (place_not_found) from a successful search with no returned branches. Canadian operational data should use the Canadian host when requested.
  • Other brands can use stored Google Maps business search/facets/nearby, followed by selected live search/place verification. A name-text match is not a verified chain affiliation; check canonical brand/site, address, and location evidence. Google Maps category/country values differ from Starbucks taxonomy/codes.
  • McDonald's coordinate locator is a separate source with its own accepted markets, cap, and radius in miles; dataset nearby radii are meters. Convert explicit geographic scopes rather than copying identical numeric radius values. A capped live locator alone cannot establish a whole-country store count.
  • Keep a boundary/query ledger, requested/returned pages, cap flags, search result counts, and crawl times. The stored indexes are not automatically an official census of every branch or a proof that a competing chain is absent.
scripts/crawlora.sh /datasets/starbucks-stores/facets facet=country
scripts/crawlora.sh /datasets/starbucks-stores/search country=US city=Seattle page=1 page_size=100
scripts/crawlora.sh /starbucks/stores place="Seattle, WA" market=us
scripts/crawlora.sh /datasets/google-map-businesses/search q="Starbucks" city=Seattle page=1 page_size=10

Reconcile locations and report coverage

Deduplicate the same brand/store ID first; cross-source matches need address and coordinate corroboration. Similar names are not sufficient, and two units/kiosks inside a complex can be distinct branches. Keep relocated, permanently-closed, licensed, and company-operated locations explicit when fields support the labels. An empty schedule/amenity field can mean not published for that market, not closed or unsupported. Mobile-order readiness differs from static capability codes.

Use unique matched branches for area counts. For overlapping radius queries, count each branch once and keep the union boundary explicit; dividing by the sum of overlapping circle areas produces a misleading density. Area density requires a defined area, and per-capita measures require a separately sourced population with matching geography/date. Counts alone are not market share.

Return a branch evidence ledger, deduplicated area/brand matrix, source coverage, observed amenity/ownership breakdowns, and unknown or incomplete areas. A missing branch in a capped/partial sample is an observed coverage gap, not proof of closure or unmet demand. Historical expansion/contraction claims require comparable saved observations and verified changes. Do not contact stores, submit leads, or choose an investment/site solely from this footprint.