Resumen
Sales teams puntuacion leads basado on firmographic datos alone, missing el rich senal disponible de resultados de busqueda. A company ese es actively discussed on Reddit, tiene YouTube tutoriales about their producto, y ranks well on Google es un muy diferentes lead than one con no online presence. Este flujo de trabajo toma cada nuevo lead, searches Google for company info, Reddit for community menciones, y YouTube for video contenido, entonces computes un composite puntuacion basado on online presence strength. Higher-signal leads obtener prioritized for outreach.
Desencadenador
Nuevo lead added to CRM o pipeline
Programación
On nuevo lead (event-driven)
Pasos del flujo de trabajo
Search Google for company
Consulta Google for el lead's company nombre y dominio. Extraer resultado conteo, fragmento calidad, y AI Overview presence.
Search Reddit for menciones
Consulta Reddit for el company nombre. Count discussion threads, extraer sentiment senales de titles y fragmentos.
Search YouTube for contenido
Consulta YouTube for el company nombre. Count videos, verificar for official canal presence, y note tutorial contenido.
Compute composite puntuacion
Weight senales de todos three plataformas en un 0-100 puntuacion. Google presence (40%), Reddit engagement (30%), YouTube contenido (30%).
Clasificar y exportar
Sort leads by composite puntuacion. Exportar el ranked lista con per-platform senal breakdown for el sales team.
Implementacion en Python
import requests, os, json
H = {"x-api-key": os.environ["SCAVIO_API_KEY"]}
LEADS = [
{"name": "TechStartup Inc", "domain": "techstartup.io"},
{"name": "DataPipe Labs", "domain": "datapipe.dev"},
{"name": "CloudSync Pro", "domain": "cloudsync.pro"},
]
def search_platform(query, platform):
"""Search a platform and return organic results."""
r = requests.post("https://api.scavio.dev/api/v1/search", headers=H,
json={"platform": platform, "query": query}, timeout=10).json()
return r.get("organic", [])
def score_lead(lead):
"""Score a lead based on multi-platform search signals."""
# Google signals
google_results = search_platform(f"{lead['name']} {lead['domain']}", "google")
google_score = min(len(google_results), 10) * 4 # Max 40
# Reddit signals
reddit_results = search_platform(lead["name"], "reddit")
reddit_score = min(len(reddit_results), 10) * 3 # Max 30
# YouTube signals
youtube_results = search_platform(lead["name"], "youtube")
youtube_score = min(len(youtube_results), 10) * 3 # Max 30
composite = google_score + reddit_score + youtube_score
return {
"name": lead["name"],
"domain": lead["domain"],
"google_results": len(google_results),
"reddit_mentions": len(reddit_results),
"youtube_videos": len(youtube_results),
"google_score": google_score,
"reddit_score": reddit_score,
"youtube_score": youtube_score,
"composite_score": composite,
"tier": "A" if composite >= 70 else "B" if composite >= 40 else "C"
}
scored = []
for lead in LEADS:
result = score_lead(lead)
scored.append(result)
print(f"[{result['tier']}] {result['name']} | Score: {result['composite_score']}/100")
print(f" Google: {result['google_results']} results ({result['google_score']}pts)")
print(f" Reddit: {result['reddit_mentions']} mentions ({result['reddit_score']}pts)")
print(f" YouTube: {result['youtube_videos']} videos ({result['youtube_score']}pts)")
scored.sort(key=lambda x: x["composite_score"], reverse=True)
print(f"\nRanked {len(scored)} leads. Top: {scored[0]['name']} ({scored[0]['composite_score']}/100)")Implementacion en JavaScript
const H = {"x-api-key": process.env.SCAVIO_API_KEY, "Content-Type": "application/json"};
const LEADS = [
{name: "TechStartup Inc", domain: "techstartup.io"},
{name: "DataPipe Labs", domain: "datapipe.dev"},
{name: "CloudSync Pro", domain: "cloudsync.pro"},
];
async function searchPlatform(query, platform) {
const r = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST", headers: H,
body: JSON.stringify({platform, query})
}).then(r => r.json());
return r.organic || [];
}
async function scoreLead(lead) {
const google = await searchPlatform(`${lead.name} ${lead.domain}`, "google");
const reddit = await searchPlatform(lead.name, "reddit");
const youtube = await searchPlatform(lead.name, "youtube");
const googleScore = Math.min(google.length, 10) * 4;
const redditScore = Math.min(reddit.length, 10) * 3;
const youtubeScore = Math.min(youtube.length, 10) * 3;
const composite = googleScore + redditScore + youtubeScore;
return {
name: lead.name, domain: lead.domain,
googleResults: google.length, redditMentions: reddit.length, youtubeVideos: youtube.length,
googleScore, redditScore, youtubeScore, compositeScore: composite,
tier: composite >= 70 ? "A" : composite >= 40 ? "B" : "C"
};
}
(async () => {
const scored = [];
for (const lead of LEADS) {
const result = await scoreLead(lead);
scored.push(result);
console.log(`[${result.tier}] ${result.name} | Score: ${result.compositeScore}/100`);
console.log(` Google: ${result.googleResults} results (${result.googleScore}pts)`);
console.log(` Reddit: ${result.redditMentions} mentions (${result.redditScore}pts)`);
console.log(` YouTube: ${result.youtubeVideos} videos (${result.youtubeScore}pts)`);
}
scored.sort((a, b) => b.compositeScore - a.compositeScore);
console.log(`\nRanked ${scored.length} leads. Top: ${scored[0].name} (${scored[0].compositeScore}/100)`);
})();Plataformas utilizadas
Búsqueda web con grafo de conocimiento, PAA y resúmenes de IA
Comunidad, publicaciones y comentarios en hilos de cualquier subreddit
YouTube
Búsqueda de videos con transcripciones y metadatos