Lead Gen Form Response → Persona Enrichment

import os, requests, time
from collections import defaultdict

LI = os.environ["LINKEDIN_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
LI_BASE = "https://api.linkedin.com/rest"
MV_BASE = "https://app.mavera.io/api/v1"
LI_H = {"Authorization": f"Bearer {LI}", "LinkedIn-Version": "202401", "X-Restli-Protocol-Version": "2.0.0"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

SPONSORED_ACCOUNT_ID = "508000001"

# 1. Pull lead gen form responses (paginated)
responses, start = [], 0
while True:
    r = requests.get(f"{LI_BASE}/leadFormResponses",
        headers=LI_H,
        params={
            "q": "owner",
            "owner": f"urn:li:sponsoredAccount:{SPONSORED_ACCOUNT_ID}",
            "start": start, "count": 100,
        })
    if r.status_code == 429:
        time.sleep(int(r.headers.get("Retry-After", 60)))
        continue
    r.raise_for_status()
    data = r.json()
    elements = data.get("elements", [])
    responses.extend(elements)
    if len(elements) < 100:
        break
    start += 100
    time.sleep(0.5)

# 2. Extract and aggregate fields
segments = defaultdict(list)
for resp in responses:
    answers = {a.get("fieldName", ""): a.get("fieldValue", "") for a in resp.get("answers", [])}
    title = answers.get("jobTitle", "Unknown")
    company_size = answers.get("companySize", "Unknown")
    industry = answers.get("industry", "Unknown")

    title_cluster = title.split(",")[0].strip()
    segments[(title_cluster, industry, company_size)].append(answers)

# 3. Create personas from top segments
SIZE_MAP = {
    "1-10": "Startup", "11-50": "Small Business", "51-200": "Mid-Market",
    "201-500": "Mid-Market", "501-1000": "Enterprise", "1001+": "Enterprise",
}

created = []
for (title, industry, size), leads in sorted(segments.items(), key=lambda x: -len(x[1]))[:8]:
    if len(leads) < 2:
        continue
    tier = SIZE_MAP.get(size, "Mid-Market")
    emails = [l.get("email", "") for l in leads if l.get("email")]

    p = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
        "name": f"LinkedIn Lead: {title} ({tier})",
        "description": (
            f"Derived from {len(leads)} LinkedIn lead gen responses. "
            f"Role: {title}. Industry: {industry}. Company size: {size} ({tier}). "
            f"These leads actively filled out a form — high intent."
        ),
        "demographic": {
            "job_titles": [title],
            "industries": [industry],
            "company_size": tier,
        },
        "psychographic": {
            "intent_level": "high",
            "source": "linkedin_lead_gen_form",
        },
    }).json()
    created.append({"persona_id": p["id"], "title": title, "industry": industry, "tier": tier, "n": len(leads)})
    time.sleep(0.3)

print(f"Created {len(created)} personas from {len(responses)} lead gen responses")
for c in created:
    print(f"  {c['title']} | {c['industry']} | {c['tier']} | N={c['n']} | {c['persona_id']}")

const LI = process.env.LINKEDIN_ACCESS_TOKEN;
const MV = process.env.MAVERA_API_KEY;
const LI_BASE = "https://api.linkedin.com/rest";
const MV_BASE = "https://app.mavera.io/api/v1";
const LI_H = { Authorization: `Bearer ${LI}`, "LinkedIn-Version": "202401", "X-Restli-Protocol-Version": "2.0.0" };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const SPONSORED_ACCOUNT_ID = "508000001";

// 1. Pull lead gen responses (paginated)
const responses = [];
let start = 0;
while (true) {
  const res = await fetch(
    `${LI_BASE}/leadFormResponses?q=owner&owner=urn:li:sponsoredAccount:${SPONSORED_ACCOUNT_ID}&start=${start}&count=100`,
    { headers: LI_H }
  );
  if (res.status === 429) {
    await new Promise(r => setTimeout(r, parseInt(res.headers.get("Retry-After") || "60", 10) * 1000));
    continue;
  }
  if (!res.ok) throw new Error(`LinkedIn ${res.status}`);
  const data = await res.json();
  const elements = data.elements || [];
  responses.push(...elements);
  if (elements.length < 100) break;
  start += 100;
  await new Promise(r => setTimeout(r, 500));
}

// 2. Aggregate by segment
const segments = {};
for (const resp of responses) {
  const answers = Object.fromEntries(
    (resp.answers || []).map(a => [a.fieldName || "", a.fieldValue || ""])
  );
  const title = (answers.jobTitle || "Unknown").split(",")[0].trim();
  const industry = answers.industry || "Unknown";
  const size = answers.companySize || "Unknown";
  const key = `${title}|${industry}|${size}`;
  (segments[key] ??= []).push(answers);
}

// 3. Create personas
const SIZE_MAP = {
  "1-10": "Startup", "11-50": "Small Business", "51-200": "Mid-Market",
  "201-500": "Mid-Market", "501-1000": "Enterprise", "1001+": "Enterprise",
};

const created = [];
const sorted = Object.entries(segments).sort(([, a], [, b]) => b.length - a.length).slice(0, 8);

for (const [key, leads] of sorted) {
  if (leads.length < 2) continue;
  const [title, industry, size] = key.split("|");
  const tier = SIZE_MAP[size] || "Mid-Market";

  const p = await fetch(`${MV_BASE}/personas`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      name: `LinkedIn Lead: ${title} (${tier})`,
      description: `Derived from ${leads.length} LinkedIn lead gen responses. Role: ${title}. Industry: ${industry}. Size: ${size} (${tier}). High intent — form fills.`,
      demographic: { job_titles: [title], industries: [industry], company_size: tier },
      psychographic: { intent_level: "high", source: "linkedin_lead_gen_form" },
    }),
  }).then(r => r.json());
  created.push({ persona_id: p.id, title, industry, tier, n: leads.length });
  await new Promise(r => setTimeout(r, 300));
}

console.log(`Created ${created.length} personas from ${responses.length} responses`);
created.forEach(c => console.log(`  ${c.title} | ${c.industry} | ${c.tier} | N=${c.n} | ${c.persona_id}`));