Interest Category → Content Strategy

import os, requests
from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import (
    RunReportRequest, Dimension, Metric, DateRange, OrderBy,
)

PROPERTY_ID = os.environ["GA4_PROPERTY_ID"]
MV = os.environ["MAVERA_API_KEY"]

client = BetaAnalyticsDataClient()

affinity_report = client.run_report(RunReportRequest(
    property=f"properties/{PROPERTY_ID}",
    dimensions=[Dimension(name="brandingInterest")],
    metrics=[
        Metric(name="totalUsers"),
        Metric(name="sessions"),
        Metric(name="engagementRate"),
        Metric(name="conversions"),
    ],
    date_ranges=[DateRange(start_date="30daysAgo", end_date="today")],
    order_bys=[OrderBy(metric=OrderBy.MetricOrderBy(metric_name="totalUsers"), desc=True)],
    limit=30,
))

interests = []
for row in affinity_report.rows:
    cat = row.dimension_values[0].value
    if cat == "(not set)":
        continue
    interests.append({
        "category": cat,
        "users": int(row.metric_values[0].value),
        "sessions": int(row.metric_values[1].value),
        "engagement": float(row.metric_values[2].value),
        "conversions": int(row.metric_values[3].value),
    })

total_users = sum(i["users"] for i in interests) or 1

interest_block = "\n".join(
    f"- {i['category']}: {i['users']} users ({i['users']/total_users:.0%}), "
    f"engagement: {i['engagement']:.1%}, conv: {i['conversions']}"
    for i in interests[:20]
)

PRODUCT_CONTEXT = "B2B marketing intelligence platform that helps teams create personas, run focus groups, and generate brand-aligned content."

mave = requests.post(
    "https://app.mavera.io/api/v1/mave/chat",
    headers={"Authorization": f"Bearer {MV}", "Content-Type": "application/json"},
    json={"message": f"""Research content opportunities that bridge our product with our audience's interests.

OUR PRODUCT: {PRODUCT_CONTEXT}

AUDIENCE INTEREST CATEGORIES (from GA4, last 30 days):
{interest_block}

For each promising interest-product intersection:
1. Why this interest category connects to our product
2. Content angle (blog, webinar, case study, social series, etc.)
3. Suggested title and hook
4. Target keyword themes
5. Which existing interest group this serves

Prioritize intersections with high engagement rate AND conversion potential.
Group into 3 tiers: immediate opportunities, medium-term content, and long-tail experiments."""},
).json()

print("--- Interest-Based Content Strategy ---")
print(mave.get("content", "")[:3000])
print(f"\nSources: {len(mave.get('sources', []))}")

const MV = process.env.MAVERA_API_KEY;
const PROPERTY_ID = process.env.GA4_PROPERTY_ID;
const KEY_FILE = JSON.parse(require("fs").readFileSync(process.env.GOOGLE_APPLICATION_CREDENTIALS, "utf8"));

const { GoogleAuth } = require("google-auth-library");
const auth = new GoogleAuth({
  credentials: KEY_FILE,
  scopes: ["https://www.googleapis.com/auth/analytics.readonly"],
});
const accessToken = await auth.getAccessToken();

const gaRes = await fetch(
  `https://analyticsdata.googleapis.com/v1beta/properties/${PROPERTY_ID}:runReport`,
  {
    method: "POST",
    headers: { Authorization: `Bearer ${accessToken}`, "Content-Type": "application/json" },
    body: JSON.stringify({
      dimensions: [{ name: "brandingInterest" }],
      metrics: [
        { name: "totalUsers" }, { name: "sessions" },
        { name: "engagementRate" }, { name: "conversions" },
      ],
      dateRanges: [{ startDate: "30daysAgo", endDate: "today" }],
      orderBys: [{ metric: { metricName: "totalUsers" }, desc: true }],
      limit: 30,
    }),
  }
).then((r) => r.json());

const interests = (gaRes.rows || [])
  .filter((row) => row.dimensionValues[0].value !== "(not set)")
  .map((row) => ({
    category: row.dimensionValues[0].value,
    users: parseInt(row.metricValues[0].value),
    sessions: parseInt(row.metricValues[1].value),
    engagement: parseFloat(row.metricValues[2].value),
    conversions: parseInt(row.metricValues[3].value),
  }));

const totalUsers = interests.reduce((s, i) => s + i.users, 0) || 1;
const interestBlock = interests.slice(0, 20)
  .map((i) => `- ${i.category}: ${i.users} users (${(i.users / totalUsers * 100).toFixed(0)}%), engagement: ${(i.engagement * 100).toFixed(1)}%, conv: ${i.conversions}`)
  .join("\n");

const PRODUCT_CONTEXT = "B2B marketing intelligence platform for personas, focus groups, and brand-aligned content generation.";

const mave = await fetch("https://app.mavera.io/api/v1/mave/chat", {
  method: "POST",
  headers: { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" },
  body: JSON.stringify({
    message: `Research content opportunities bridging our product with audience interests.\n\nPRODUCT: ${PRODUCT_CONTEXT}\n\nINTERESTS (GA4 30d):\n${interestBlock}\n\nFor each intersection: 1) Why it connects 2) Content angle 3) Title + hook 4) Keywords 5) Which interest group. Tier into immediate, medium-term, and experimental.`,
  }),
}).then((r) => r.json());

console.log("--- Interest-Based Content Strategy ---");
console.log((mave.content || "").slice(0, 3000));
console.log(`\nSources: ${(mave.sources || []).length}`);