Lead Gen Form Response → Persona Enrichment

Pull LinkedIn Lead Gen Form responses, aggregate by title/industry/company size, and create enriched Mavera personas from real lead data.

Scenario

LinkedIn Lead Gen Forms capture high-quality B2B leads with job title, company size, and industry — but this data sits in Campaign Manager unused. This job pulls form responses, aggregates them by title cluster, company size tier, and industry vertical, then feeds each segment into Mavera’s persona discovery engine. The result is a set of Custom Personas grounded in who actually fills out your forms, not who you think your audience is.

Architecture

Code

Python

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']}")

Example Output

{
  "total_responses": 847,
  "personas_created": 6,
  "segments": [
    { "title": "VP of Marketing", "industry": "Technology", "tier": "Enterprise", "n": 142, "persona_id": "per_li_vpm_01" },
    { "title": "Head of Growth", "industry": "SaaS", "tier": "Mid-Market", "n": 98, "persona_id": "per_li_hog_02" },
    { "title": "Marketing Manager", "industry": "Financial Services", "tier": "Enterprise", "n": 87, "persona_id": "per_li_mm_03" },
    { "title": "Director of Demand Gen", "industry": "Technology", "tier": "Mid-Market", "n": 64, "persona_id": "per_li_ddg_04" },
    { "title": "CMO", "industry": "Healthcare", "tier": "Enterprise", "n": 41, "persona_id": "per_li_cmo_05" },
    { "title": "Content Strategist", "industry": "Technology", "tier": "Small Business", "n": 29, "persona_id": "per_li_cs_06" }
  ]
}

Error Handling

r_ads_leadgen_automation scope required

Lead gen form responses need the r_ads_leadgen_automation scope, which requires LinkedIn partner approval. Without it, the endpoint returns 403.

Field name variations

LinkedIn lead gen form field names are defined per form. Common names: jobTitle, companySize, industry, email. Inspect your form configuration if fields return empty.

Deduplication

Leads can submit the same form multiple times. Deduplicate by email before aggregation for accurate persona counts.

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