Feature Adoption → Messaging Priorities

import os, requests, time, json

MP_SA = os.environ["MIXPANEL_SERVICE_ACCOUNT"]
MP_SECRET = os.environ["MIXPANEL_SECRET"]
MP_PROJECT = os.environ["MIXPANEL_PROJECT_ID"]
MV = os.environ["MAVERA_API_KEY"]
MB = "https://app.mavera.io/api/v1"
MH = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

FEATURES = {
    "Dashboard View": "Main dashboard with analytics overview",
    "Report Create": "Custom report builder",
    "Alert Set": "Automated alerts and notifications",
    "API Call": "Programmatic API access",
    "Export Data": "CSV/Excel data export",
    "Team Invite": "Add team members to workspace",
    "Integration Connect": "Connect third-party tools",
    "Template Use": "Pre-built report templates",
    "Scheduled Report": "Automated recurring reports",
    "Custom Widget": "Build custom dashboard widgets",
}

r = requests.post(
    "https://mixpanel.com/api/query/insights",
    auth=(MP_SA, MP_SECRET),
    json={
        "project_id": MP_PROJECT,
        "bookmark_id": None,
        "params": {
            "type": "unique",
            "events": [{"event": name} for name in FEATURES.keys()],
            "from_date": "2026-02-15",
            "to_date": "2026-03-17",
        },
    },
)
if r.status_code == 429:
    time.sleep(60)
r.raise_for_status()
insights = r.json()

total_users_r = requests.post(
    "https://mixpanel.com/api/query/insights",
    auth=(MP_SA, MP_SECRET),
    json={
        "project_id": MP_PROJECT,
        "params": {"type": "unique", "events": [{"event": "Login"}],
                   "from_date": "2026-02-15", "to_date": "2026-03-17"},
    },
)
total_users_data = total_users_r.json()
total_users = 5000

feature_adoption = []
series = insights.get("series", {})
for feature_name, desc in FEATURES.items():
    values = series.get(feature_name, {})
    unique_users = sum(values.values()) if isinstance(values, dict) else 0
    adoption_rate = unique_users / max(total_users, 1)
    feature_adoption.append({
        "feature": feature_name,
        "description": desc,
        "unique_users": unique_users,
        "adoption_rate": adoption_rate,
    })

feature_adoption.sort(key=lambda f: f["adoption_rate"])

underused = [f for f in feature_adoption if f["adoption_rate"] < 0.3]
well_adopted = [f for f in feature_adoption if f["adoption_rate"] >= 0.5]

adoption_table = "\n".join(
    f"- {f['feature']}: {f['unique_users']} users ({f['adoption_rate']:.0%} adoption) — {f['description']}"
    for f in feature_adoption
)

PERSONA_IDS = os.environ.get("FEATURE_PERSONA_IDS", "").split(",")
if not PERSONA_IDS[0]:
    for name, desc in [
        ("Active Free User", "Uses the product daily on a free plan. Hasn't upgraded because they don't know what they're missing."),
        ("New Pro User", "Recently upgraded. Using 2-3 features but hasn't explored the full platform."),
        ("Power User Champion", "Uses most features. Internal advocate. Wants to get more from the tool."),
    ]:
        p = requests.post(f"{MB}/personas", headers=MH, json={"name": name, "description": desc}).json()
        PERSONA_IDS.append(p["id"])
        time.sleep(0.3)

underused_list = ", ".join(f["feature"] for f in underused[:5])

fg = requests.post(f"{MB}/focus-groups", headers=MH, json={
    "name": "Feature Awareness & Adoption",
    "persona_ids": [pid for pid in PERSONA_IDS if pid],
    "questions": [
        f"Are you aware of these features: {underused_list}? For each, answer Yes or No, then explain why you do or don't use it.",
        "If you discovered a feature that could save you 2 hours per week, how would you want to learn about it? (in-app tooltip, email, video tutorial, peer recommendation, or other?)",
        f"Rank these underused features by how useful they WOULD be if you knew about them: {underused_list}. Explain your #1.",
        "What's the biggest gap in this product — a feature you wish existed?",
    ],
    "context": f"""Feature adoption data for our product (last 30 days):

{adoption_table}

Well-adopted (>50%): {', '.join(f['feature'] for f in well_adopted)}
Underused (<30%): {underused_list}

Total active users: ~{total_users}""",
    "responses_per_persona": 2,
}).json()

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("--- Focus Group Results ---\n")
for resp in data.get("responses", []):
    print(f"[{resp.get('persona_id','?')}] {resp.get('question','')[:70]}")
    print(f"  → {resp.get('answer','')[:250]}\n")

print("\n--- Generating Feature Campaigns ---\n")
for feature in underused[:3]:
    gen = requests.post(f"{MB}/generations", headers=MH, json={
        "prompt": (
            f"Create a feature awareness campaign for '{feature['feature']}' ({feature['description']}). "
            f"Current adoption: {feature['adoption_rate']:.0%} of users. "
            f"Generate:\n"
            f"1. In-app banner copy (max 20 words + CTA)\n"
            f"2. Email subject line + 50-word body\n"
            f"3. Tooltip text (max 15 words)\n"
            f"4. 30-second video script outline\n"
            f"Focus on the user benefit, not the feature name."
        ),
    }).json()

    print(f"Feature: {feature['feature']} ({feature['adoption_rate']:.0%} adoption)")
    print(gen.get("output", gen.get("content", ""))[:500])
    print()

const MP_SA = process.env.MIXPANEL_SERVICE_ACCOUNT;
const MP_SECRET = process.env.MIXPANEL_SECRET;
const MP_PROJECT = process.env.MIXPANEL_PROJECT_ID;
const MV = process.env.MAVERA_API_KEY;
const MB = "https://app.mavera.io/api/v1";
const MH = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const mpAuth = "Basic " + Buffer.from(`${MP_SA}:${MP_SECRET}`).toString("base64");

const FEATURES = {
  "Dashboard View": "Main dashboard with analytics overview",
  "Report Create": "Custom report builder",
  "Alert Set": "Automated alerts and notifications",
  "API Call": "Programmatic API access",
  "Export Data": "CSV/Excel data export",
  "Team Invite": "Add team members",
  "Integration Connect": "Connect third-party tools",
  "Template Use": "Pre-built report templates",
  "Scheduled Report": "Automated recurring reports",
  "Custom Widget": "Custom dashboard widgets",
};

const insights = await fetch("https://mixpanel.com/api/query/insights", {
  method: "POST",
  headers: { Authorization: mpAuth, "Content-Type": "application/json" },
  body: JSON.stringify({
    project_id: MP_PROJECT,
    params: {
      type: "unique",
      events: Object.keys(FEATURES).map((event) => ({ event })),
      from_date: "2026-02-15", to_date: "2026-03-17",
    },
  }),
}).then((r) => r.json());

const totalUsers = 5000;
const series = insights.series || {};

const featureAdoption = Object.entries(FEATURES).map(([name, desc]) => {
  const vals = series[name] || {};
  const uniqueUsers = Object.values(vals).reduce((s, v) => s + v, 0);
  return { feature: name, description: desc, uniqueUsers, adoptionRate: uniqueUsers / totalUsers };
}).sort((a, b) => a.adoptionRate - b.adoptionRate);

const underused = featureAdoption.filter((f) => f.adoptionRate < 0.3);
const adoptionTable = featureAdoption.map((f) =>
  `- ${f.feature}: ${f.uniqueUsers} users (${(f.adoptionRate * 100).toFixed(0)}%) — ${f.description}`
).join("\n");

const personaIds = [];
for (const [name, desc] of [
  ["Active Free User", "Daily free user. Doesn't know what they're missing."],
  ["New Pro User", "Recently upgraded. Using 2-3 features."],
  ["Power User Champion", "Uses most features. Internal advocate."],
]) {
  const p = await fetch(`${MB}/personas`, { method: "POST", headers: MH,
    body: JSON.stringify({ name, description: desc }) }).then((r) => r.json());
  personaIds.push(p.id);
  await new Promise((r) => setTimeout(r, 300));
}

const underusedList = underused.slice(0, 5).map((f) => f.feature).join(", ");

const fg = await fetch(`${MB}/focus-groups`, { method: "POST", headers: MH,
  body: JSON.stringify({
    name: "Feature Awareness",
    persona_ids: personaIds,
    questions: [
      `Are you aware of: ${underusedList}? Yes/No for each, then explain.`,
      "How would you want to learn about a feature saving 2hr/week?",
      `Rank by usefulness: ${underusedList}. Explain #1.`,
      "What feature gap exists in this product?",
    ],
    context: `Feature adoption (30d):\n${adoptionTable}\n\nTotal: ~${totalUsers}`,
    responses_per_persona: 2,
  }),
}).then((r) => r.json());

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("--- Focus Group Results ---\n");
for (const resp of data.responses || []) {
  console.log(`[${resp.persona_id}] ${(resp.question || "").slice(0, 70)}`);
  console.log(`  → ${(resp.answer || "").slice(0, 250)}\n`);
}

console.log("\n--- Feature Campaigns ---\n");
for (const feature of underused.slice(0, 3)) {
  const gen = await fetch(`${MB}/generations`, { method: "POST", headers: MH,
    body: JSON.stringify({
      prompt: `Feature awareness campaign for '${feature.feature}' (${feature.description}). Adoption: ${(feature.adoptionRate * 100).toFixed(0)}%. Generate: 1) In-app banner 2) Email subject+body 3) Tooltip 4) 30s video script.`,
    }),
  }).then((r) => r.json());
  console.log(`${feature.feature} (${(feature.adoptionRate * 100).toFixed(0)}% adoption)`);
  console.log((gen.output || gen.content || "").slice(0, 500));
  console.log();
}