Audience Insight → Persona Mapping

import os, requests, time
from collections import defaultdict

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 audience insights with demographic breakdowns
insights = requests.get(
    f"{GRAPH}/{ACCT}/insights",
    params={
        "access_token": META,
        "fields": "impressions,clicks,spend,actions",
        "breakdowns": "age,gender",
        "date_preset": "last_30d",
        "limit": 100,
    },
).json().get("data", [])

# 2. Score segments by conversion efficiency
segments = []
for row in insights:
    conversions = 0
    for action in row.get("actions", []):
        if action.get("action_type") in ("offsite_conversion", "lead", "purchase"):
            conversions += int(action.get("value", 0))

    segments.append({
        "age": row.get("age", "unknown"),
        "gender": row.get("gender", "unknown"),
        "impressions": int(row.get("impressions", 0)),
        "clicks": int(row.get("clicks", 0)),
        "spend": float(row.get("spend", 0)),
        "conversions": conversions,
        "ctr": int(row.get("clicks", 0)) / max(int(row.get("impressions", 1)), 1),
        "cpa": float(row.get("spend", 0)) / max(conversions, 1),
    })

top_segments = sorted(segments, key=lambda s: s["conversions"], reverse=True)[:10]

# 3. Pull location breakdown separately
geo_insights = requests.get(
    f"{GRAPH}/{ACCT}/insights",
    params={
        "access_token": META,
        "fields": "impressions,clicks,spend",
        "breakdowns": "country",
        "date_preset": "last_30d",
        "limit": 20,
    },
).json().get("data", [])
top_countries = sorted(geo_insights, key=lambda g: int(g.get("clicks", 0)), reverse=True)[:5]
country_list = [g.get("country", "Unknown") for g in top_countries]

# 4. Fetch existing Mavera personas
existing = requests.get(f"{MB}/personas", headers=MH).json()
existing_names = {p.get("name", "").lower(): p for p in (existing if isinstance(existing, list) else [])}

# 5. Map or create personas
created, mapped = [], []
for seg in top_segments:
    name = f"Meta {seg['gender'].title()} {seg['age']}"
    search_key = name.lower()

    if search_key in existing_names:
        mapped.append({"name": name, "id": existing_names[search_key]["id"], "action": "mapped"})
        continue

    desc = (
        f"Meta Ads audience segment: {seg['gender']}, age {seg['age']}. "
        f"30-day stats: {seg['impressions']:,} impressions, {seg['clicks']:,} clicks, "
        f"CTR {seg['ctr']:.2%}, {seg['conversions']} conversions, CPA ${seg['cpa']:.2f}. "
        f"Top markets: {', '.join(country_list[:3])}."
    )

    r = requests.post(f"{MB}/personas", headers=MH, json={
        "name": name,
        "description": desc,
        "demographic": {
            "age_range": seg["age"],
            "gender": seg["gender"],
            "countries": country_list[:3],
        },
        "psychographic": {
            "conversion_propensity": "high" if seg["conversions"] > 10 else "medium",
            "engagement_level": "high" if seg["ctr"] > 0.02 else "moderate",
        },
    })
    r.raise_for_status()
    created.append({"name": name, "id": r.json()["id"], "action": "created"})
    time.sleep(0.3)

print(f"Mapped: {len(mapped)} existing | Created: {len(created)} new")
for p in mapped + created:
    print(f"  [{p['action'].upper()}] {p['name']} → {p['id']}")

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 audience insights
const insights = await fetch(
  `${GRAPH}/${ACCT}/insights?access_token=${META}&fields=impressions,clicks,spend,actions&breakdowns=age,gender&date_preset=last_30d&limit=100`
).then(r => r.json()).then(d => d.data || []);

// 2. Score segments
const segments = insights.map(row => {
  const conversions = (row.actions || [])
    .filter(a => ["offsite_conversion", "lead", "purchase"].includes(a.action_type))
    .reduce((sum, a) => sum + parseInt(a.value || "0"), 0);
  const impressions = parseInt(row.impressions || "0");
  const clicks = parseInt(row.clicks || "0");
  const spend = parseFloat(row.spend || "0");
  return {
    age: row.age || "unknown", gender: row.gender || "unknown",
    impressions, clicks, spend, conversions,
    ctr: clicks / Math.max(impressions, 1),
    cpa: spend / Math.max(conversions, 1),
  };
}).sort((a, b) => b.conversions - a.conversions).slice(0, 10);

// 3. Location breakdown
const geoInsights = await fetch(
  `${GRAPH}/${ACCT}/insights?access_token=${META}&fields=impressions,clicks,spend&breakdowns=country&date_preset=last_30d&limit=20`
).then(r => r.json()).then(d => d.data || []);
const topCountries = geoInsights
  .sort((a, b) => parseInt(b.clicks || "0") - parseInt(a.clicks || "0"))
  .slice(0, 5).map(g => g.country || "Unknown");

// 4. Existing personas
const existing = await fetch(`${MB}/personas`, { headers: MH }).then(r => r.json());
const existingNames = new Map(
  (Array.isArray(existing) ? existing : []).map(p => [(p.name || "").toLowerCase(), p])
);

// 5. Map or create
const created = [], mapped = [];
for (const seg of segments) {
  const name = `Meta ${seg.gender.charAt(0).toUpperCase() + seg.gender.slice(1)} ${seg.age}`;
  if (existingNames.has(name.toLowerCase())) {
    mapped.push({ name, id: existingNames.get(name.toLowerCase()).id, action: "mapped" });
    continue;
  }

  const res = await fetch(`${MB}/personas`, {
    method: "POST", headers: MH,
    body: JSON.stringify({
      name,
      description: `Meta Ads: ${seg.gender}, age ${seg.age}. ${seg.impressions.toLocaleString()} imp, CTR ${(seg.ctr * 100).toFixed(2)}%, ${seg.conversions} conversions, CPA $${seg.cpa.toFixed(2)}.`,
      demographic: { age_range: seg.age, gender: seg.gender, countries: topCountries.slice(0, 3) },
      psychographic: {
        conversion_propensity: seg.conversions > 10 ? "high" : "medium",
        engagement_level: seg.ctr > 0.02 ? "high" : "moderate",
      },
    }),
  }).then(r => r.json());
  created.push({ name, id: res.id, action: "created" });
  await new Promise(r => setTimeout(r, 300));
}

console.log(`Mapped: ${mapped.length} | Created: ${created.length}`);
[...mapped, ...created].forEach(p => console.log(`  [${p.action.toUpperCase()}] ${p.name} → ${p.id}`));