Custom Audience → Focus Group Mirror

import os, requests, time

META = os.environ["META_ACCESS_TOKEN"]
ACCT = os.environ["META_AD_ACCOUNT_ID"]
MV = os.environ["MAVERA_API_KEY"]
GRAPH = "https://graph.facebook.com/v24.0"
MB = "https://app.mavera.io/api/v1"
MH = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

# 1. Pull Custom Audiences
audiences = requests.get(
    f"{GRAPH}/{ACCT}/customaudiences",
    params={
        "access_token": META,
        "fields": "id,name,description,subtype,approximate_count,data_source,delivery_status",
        "limit": 25,
    },
).json().get("data", [])

active_audiences = [a for a in audiences
    if a.get("delivery_status", {}).get("status") != "deleted"
    and a.get("approximate_count", 0) > 100]
print(f"Active audiences: {len(active_audiences)}")

# 2. Create mirrored personas
persona_map = []
for aud in active_audiences[:6]:
    subtype = aud.get("subtype", "CUSTOM")
    data_source = aud.get("data_source", {})
    source_type = data_source.get("type", "unknown")
    source_desc = data_source.get("sub_type", source_type)

    if subtype == "LOOKALIKE":
        persona_desc = (
            f"Lookalike audience: {aud['name']}. ~{aud.get('approximate_count',0):,} users. "
            f"Mirrors users similar to your seed audience. Likely shares demographic and "
            f"behavioral traits with your best customers but hasn't engaged with your brand yet."
        )
    elif subtype == "WEBSITE":
        persona_desc = (
            f"Website visitor audience: {aud['name']}. ~{aud.get('approximate_count',0):,} users. "
            f"Has visited your website — already aware of your product. "
            f"Evaluating or comparing options. Warm prospect mindset."
        )
    elif subtype == "ENGAGEMENT":
        persona_desc = (
            f"Engagement audience: {aud['name']}. ~{aud.get('approximate_count',0):,} users. "
            f"Has interacted with your social content. Aware and interested but not yet converted. "
            f"Responds to social proof and community-oriented messaging."
        )
    else:
        persona_desc = (
            f"Custom audience: {aud['name']}. Source: {source_desc}. "
            f"~{aud.get('approximate_count',0):,} users. {aud.get('description','No description.')}"
        )

    persona = requests.post(f"{MB}/personas", headers=MH, json={
        "name": f"Meta Audience: {aud['name'][:50]}",
        "description": persona_desc,
        "psychographic": {
            "audience_type": subtype.lower(),
            "awareness_level": "warm" if subtype in ("WEBSITE", "ENGAGEMENT") else "cold",
            "source": source_desc,
        },
    }).json()
    persona_map.append({
        "audience_id": aud["id"],
        "audience_name": aud["name"],
        "persona_id": persona["id"],
        "subtype": subtype,
        "size": aud.get("approximate_count", 0),
    })
    time.sleep(0.3)

# 3. Run Focus Group with ad creative stimulus
AD_CREATIVE = """
Headline: "Your Competitors Already Know This"
Body: "Teams using centralized analytics make decisions 40% faster. See how in 2 minutes."
CTA: Learn More
Visual: Split-screen showing cluttered dashboards vs clean single-pane view.
"""

fg = requests.post(f"{MB}/focus-groups", headers=MH, json={
    "name": "Custom Audience Creative Test",
    "persona_ids": [p["persona_id"] for p in persona_map],
    "questions": [
        f"You see this ad in your Instagram feed:\n\n{AD_CREATIVE}\n\nWhat's your first reaction?",
        "Does the headline create curiosity or feel clickbait-y? Why?",
        "Rate your likelihood to click (1-10). What would change your rating?",
        "What would this ad need to say to make you stop scrolling?",
        "If you clicked, what would you expect on the landing page?",
    ],
    "context": "Testing ad creative against different audience segments defined by Meta targeting.",
    "responses_per_persona": 2,
}).json()

# 4. Poll and display
for _ in range(24):
    time.sleep(5)
    data = requests.get(f"{MB}/focus-groups/{fg['id']}", headers=MH).json()
    if data.get("status") == "completed":
        break

print("\n=== Audience-Mirrored Focus Group ===")
for pm in persona_map:
    print(f"\n[{pm['audience_name']}] ({pm['subtype']}, {pm['size']:,} users)")
    audience_responses = [r for r in data.get("responses", [])
        if r.get("persona_id") == pm["persona_id"]]
    for resp in audience_responses[:3]:
        print(f"  Q: {resp.get('question','')[:60]}...")
        print(f"  A: {resp.get('answer','')[:200]}")

const META = process.env.META_ACCESS_TOKEN;
const ACCT = process.env.META_AD_ACCOUNT_ID;
const MV = process.env.MAVERA_API_KEY;
const GRAPH = "https://graph.facebook.com/v24.0";
const MB = "https://app.mavera.io/api/v1";
const MH = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

// 1. Pull Custom Audiences
const audiences = await fetch(
  `${GRAPH}/${ACCT}/customaudiences?access_token=${META}&fields=id,name,description,subtype,approximate_count,data_source,delivery_status&limit=25`
).then(r => r.json()).then(d => d.data || []);

const activeAudiences = audiences
  .filter(a => a.delivery_status?.status !== "deleted" && (a.approximate_count || 0) > 100)
  .slice(0, 6);
console.log(`Active audiences: ${activeAudiences.length}`);

// 2. Create mirrored personas
const personaMap = [];
for (const aud of activeAudiences) {
  const subtype = aud.subtype || "CUSTOM";
  const sourceDesc = aud.data_source?.sub_type || aud.data_source?.type || "unknown";

  const descMap = {
    LOOKALIKE: `Lookalike: ${aud.name}. ~${(aud.approximate_count || 0).toLocaleString()} users. Similar to seed audience.`,
    WEBSITE: `Website visitors: ${aud.name}. ~${(aud.approximate_count || 0).toLocaleString()} users. Aware, evaluating.`,
    ENGAGEMENT: `Engaged users: ${aud.name}. ~${(aud.approximate_count || 0).toLocaleString()} users. Interested, not converted.`,
  };
  const desc = descMap[subtype] || `Custom: ${aud.name}. Source: ${sourceDesc}. ${aud.description || ""}`;

  const persona = await fetch(`${MB}/personas`, {
    method: "POST", headers: MH,
    body: JSON.stringify({
      name: `Meta Audience: ${(aud.name || "").slice(0, 50)}`,
      description: desc,
      psychographic: {
        audience_type: subtype.toLowerCase(),
        awareness_level: ["WEBSITE", "ENGAGEMENT"].includes(subtype) ? "warm" : "cold",
        source: sourceDesc,
      },
    }),
  }).then(r => r.json());

  personaMap.push({
    audience_id: aud.id, audience_name: aud.name,
    persona_id: persona.id, subtype, size: aud.approximate_count || 0,
  });
  await new Promise(r => setTimeout(r, 300));
}

// 3. Focus Group
const AD_CREATIVE = `Headline: "Your Competitors Already Know This"\nBody: "Teams using centralized analytics make decisions 40% faster."\nCTA: Learn More`;

const fg = await fetch(`${MB}/focus-groups`, {
  method: "POST", headers: MH,
  body: JSON.stringify({
    name: "Custom Audience Creative Test",
    persona_ids: personaMap.map(p => p.persona_id),
    questions: [
      `You see this ad:\n\n${AD_CREATIVE}\n\nFirst reaction?`,
      "Curiosity or clickbait? Why?",
      "Likelihood to click (1-10)? What would change it?",
      "What would make you stop scrolling?",
      "If you clicked, what do you expect on the landing page?",
    ],
    context: "Testing creative against Meta targeting segments.",
    responses_per_persona: 2,
  }),
}).then(r => r.json());

// 4. Poll
let data;
for (let i = 0; i < 24; i++) {
  await new Promise(r => setTimeout(r, 5000));
  data = await fetch(`${MB}/focus-groups/${fg.id}`, { headers: MH }).then(r => r.json());
  if (data.status === "completed") break;
}

console.log("\n=== Audience-Mirrored Focus Group ===");
for (const pm of personaMap) {
  console.log(`\n[${pm.audience_name}] (${pm.subtype}, ${pm.size.toLocaleString()} users)`);
  const resps = (data.responses || []).filter(r => r.persona_id === pm.persona_id);
  resps.slice(0, 3).forEach(r => {
    console.log(`  Q: ${(r.question || "").slice(0, 60)}...`);
    console.log(`  A: ${(r.answer || "").slice(0, 200)}`);
  });
}