An r/DigitalMarketing post asked if marketers are stopping Google Ads because LLMs are eating the funnel. This walks a tracking pipeline that measures LLM share-of-voice for the same queries running on paid search.
Prerequisites
- Scavio API key
- An LLM API key (or two — Claude + OpenAI for cross-check)
- A list of 10-50 brand-relevant queries
Walkthrough
Step 1: Define your query set
10-50 phrases your buyers actually type/say.
Python
QUERIES = [
'best email deliverability tool',
'how to fix dmarc',
'cold email warmup software',
# ...your buyer queries
]Step 2: Pull AI Overview citations via Scavio
Scavio's include_ai_overview returns the citation set.
Python
import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
def ai_overview_cites(q):
r = requests.post('https://api.scavio.dev/api/v1/search',
headers=H, json={'query': q, 'include_ai_overview': True}).json()
return r.get('ai_overview', {}).get('citations', [])Step 3: Poll Claude / GPT directly
Ask each LLM the same query; check if your brand appears in the answer.
Python
import anthropic
client = anthropic.Anthropic()
def claude_answer(q):
return client.messages.create(model='claude-sonnet-4-7', max_tokens=500,
messages=[{'role':'user', 'content': q}]).content[0].textStep 4: Score brand presence per query
Citation hit / mention hit / position rank.
Python
def brand_score(text, brand):
text = text.lower(); brand = brand.lower()
if brand not in text: return 0
pos = text.index(brand)
return 1.0 if pos < 200 else 0.5 # earlier mention = strongerStep 5: Cron weekly + chart over time
Track brand-presence trend per query, per LLM.
Text
# Save daily/weekly to SQLite or Postgres; chart in Streamlit / Grafana.
# Watch the trend; the absolute number matters less than the slope.Python Example
Python
# Per-query weekly cost: 1 Scavio call + 2-3 LLM calls = ~$0.02-0.05. 50 queries: ~$1-2.50/wk.JavaScript Example
JavaScript
// Same pipeline in TS.Expected Output
JSON
Weekly chart of brand presence in AI Overviews + ChatGPT/Claude/Perplexity answers. The slope reveals whether the LLM channel is gaining or losing for your brand — the data point most marketers don't yet have.