Campaign Analytics → Content Brief

import os, requests, time

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"}

CAMPAIGN_URN = "urn:li:sponsoredCampaign:200000001"

# 1. Pull analytics pivoted by industry
pivots = ["MEMBER_INDUSTRY", "MEMBER_JOB_FUNCTION", "MEMBER_SENIORITY"]
all_segments = []

for pivot in pivots:
    r = requests.get(f"{LI_BASE}/adAnalytics",
        headers=LI_H,
        params={
            "q": "analytics",
            "pivot": pivot,
            "dateRange.start.year": 2025, "dateRange.start.month": 1, "dateRange.start.day": 1,
            "dateRange.end.year": 2025, "dateRange.end.month": 12, "dateRange.end.day": 31,
            "timeGranularity": "ALL",
            "campaigns[0]": CAMPAIGN_URN,
            "fields": "impressions,clicks,costInLocalCurrency,externalWebsiteConversions",
        })
    if r.status_code == 429:
        time.sleep(int(r.headers.get("Retry-After", 60)))
        continue
    r.raise_for_status()

    for el in r.json().get("elements", []):
        impressions = el.get("impressions", 0)
        clicks = el.get("clicks", 0)
        if impressions < 100:
            continue
        ctr = clicks / impressions if impressions else 0
        conversions = el.get("externalWebsiteConversions", 0)
        all_segments.append({
            "pivot": pivot,
            "value": el.get("pivotValue", "Unknown"),
            "impressions": impressions,
            "clicks": clicks,
            "ctr": round(ctr * 100, 2),
            "conversions": conversions,
            "spend": el.get("costInLocalCurrency", 0),
        })
    time.sleep(0.5)

# 2. Rank by CTR, take top segments
top = sorted(all_segments, key=lambda x: -x["ctr"])[:6]

# 3. Generate content briefs via Mave
for seg in top:
    brief = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
        "message": f"""Write a content brief for this high-performing LinkedIn audience segment.

SEGMENT: {seg['value']} ({seg['pivot'].replace('MEMBER_', '').replace('_', ' ').title()})
PERFORMANCE: {seg['impressions']:,} impressions | {seg['clicks']:,} clicks | CTR: {seg['ctr']}% | {seg['conversions']} conversions | ${seg['spend']:,.2f} spend

Produce:
1. Why this segment responds (hypotheses based on role/industry/seniority)
2. Content brief: topic, angle, format (whitepaper / blog / video / carousel)
3. Headline options (3)
4. Key messages and proof points to include
5. Distribution strategy (organic LinkedIn + paid amplification)
6. Recommended content length and CTA"""
    }).json()

    print(f"\n{'='*50}")
    print(f"SEGMENT: {seg['value']} | CTR: {seg['ctr']}% | Clicks: {seg['clicks']:,}")
    print(f"{'='*50}")
    print(brief.get("content", "")[:600])

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 CAMPAIGN_URN = "urn:li:sponsoredCampaign:200000001";

// 1. Pull analytics pivoted by industry/function/seniority
const pivots = ["MEMBER_INDUSTRY", "MEMBER_JOB_FUNCTION", "MEMBER_SENIORITY"];
const allSegments = [];

for (const pivot of pivots) {
  const params = new URLSearchParams({
    q: "analytics", pivot,
    "dateRange.start.year": "2025", "dateRange.start.month": "1", "dateRange.start.day": "1",
    "dateRange.end.year": "2025", "dateRange.end.month": "12", "dateRange.end.day": "31",
    timeGranularity: "ALL",
    "campaigns[0]": CAMPAIGN_URN,
    fields: "impressions,clicks,costInLocalCurrency,externalWebsiteConversions",
  });
  const res = await fetch(`${LI_BASE}/adAnalytics?${params}`, { 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}`);

  for (const el of (await res.json()).elements || []) {
    const impressions = el.impressions || 0;
    const clicks = el.clicks || 0;
    if (impressions < 100) continue;
    allSegments.push({
      pivot, value: el.pivotValue || "Unknown",
      impressions, clicks,
      ctr: parseFloat(((clicks / impressions) * 100).toFixed(2)),
      conversions: el.externalWebsiteConversions || 0,
      spend: el.costInLocalCurrency || 0,
    });
  }
  await new Promise(r => setTimeout(r, 500));
}

// 2. Rank by CTR
const top = allSegments.sort((a, b) => b.ctr - a.ctr).slice(0, 6);

// 3. Content briefs via Mave
for (const seg of top) {
  const brief = await fetch(`${MV_BASE}/mave/chat`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      message: `Write a content brief for this LinkedIn segment.\n\nSEGMENT: ${seg.value} (${seg.pivot.replace("MEMBER_", "").replace(/_/g, " ")})\nPERFORMANCE: ${seg.impressions.toLocaleString()} impressions | ${seg.clicks.toLocaleString()} clicks | CTR: ${seg.ctr}% | ${seg.conversions} conversions\n\nProduce: 1) Why this segment responds 2) Content brief 3) 3 headlines 4) Key messages 5) Distribution strategy 6) CTA`,
    }),
  }).then(r => r.json());

  console.log(`\n${"=".repeat(50)}`);
  console.log(`SEGMENT: ${seg.value} | CTR: ${seg.ctr}% | Clicks: ${seg.clicks.toLocaleString()}`);
  console.log("=".repeat(50));
  console.log((brief.content || "").slice(0, 600));
}