用 Python 量化 GEO 内容的长尾意图覆盖率

Long-tail intent coverage self-check (ASCII)
TARGET_INTENTS = [
"how local shop does GEO",
"how store gets recommended by AI",
"how local biz gets mentioned by AI assistant",
"how regional brand enters AI answer",
"same-city merchant GEO",
"how store does AI search optimization",
"nearby GEO service",
"which GEO vendor in city XX is reliable",
]
your_articles = [
{"site": "site_1", "covered": ["how local shop does GEO", "how store gets recommended by AI"]},
{"site": "site_2", "covered": ["how local shop does GEO"]},
{"site": "site_3", "covered": []},
{"site": "site_4", "covered": ["how store gets recommended by AI", "same-city merchant GEO"]},
{"site": "site_5", "covered": ["how local biz gets mentioned by AI assistant"]},
]
all_covered = set()
for a in your_articles:
all_covered.update(a["covered"])
cov = round(len(all_covered & set(TARGET_INTENTS)) / len(TARGET_INTENTS), 2)
missing = [q for q in TARGET_INTENTS if q not in all_covered]
sites = len(set(a["site"] for a in your_articles))
print("==== Long-tail Intent Coverage ====")
print("target intents:", len(TARGET_INTENTS))
print("covered:", len(all_covered & set(TARGET_INTENTS)))
print("coverage:", cov)
print("sites:", sites)
print("uncovered intents:", missing)
print("action:", "scale similar content" if cov >= 0.6 else "fill uncovered intents first")