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']}")