Star Distribution → Persona Segmentation

import os, requests, time
from collections import defaultdict

TP_KEY = os.environ["TRUSTPILOT_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
BU_ID = os.environ["TRUSTPILOT_BU_ID"]
TP_BASE = "https://api.trustpilot.com/v1"
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

TIERS = {
    "Detractor (1-2★)": [1, 2],
    "Neutral (3★)": [3],
    "Promoter (4-5★)": [4, 5],
}

# 1. Pull reviews per tier
tier_reviews = {}
for tier_name, star_list in TIERS.items():
    all_revs = []
    for stars in star_list:
        r = requests.get(f"{TP_BASE}/business-units/{BU_ID}/reviews",
            params={"apikey": TP_KEY, "stars": stars, "perPage": 50,
                     "orderBy": "createdat.desc"})
        r.raise_for_status()
        all_revs.extend(r.json().get("reviews", []))
        time.sleep(0.2)
    tier_reviews[tier_name] = all_revs

# 2. Extract themes per tier
tier_themes = {}
for tier_name, revs in tier_reviews.items():
    texts = [r.get("text", "")[:300] for r in revs if r.get("text")]
    themes = defaultdict(int)
    keywords = {"shipping": 0, "quality": 0, "support": 0, "price": 0, "easy": 0,
                "fast": 0, "broken": 0, "refund": 0, "love": 0, "recommend": 0}
    for t in texts:
        lower = t.lower()
        for kw in keywords:
            if kw in lower:
                themes[kw] += 1
    tier_themes[tier_name] = {"texts": texts, "themes": dict(themes), "count": len(revs)}

# 3. Create personas per tier
persona_ids = []
for tier_name, data in tier_themes.items():
    top_themes = sorted(data["themes"].items(), key=lambda x: -x[1])[:5]
    theme_str = ", ".join(f"{k} ({v})" for k, v in top_themes)
    sample_texts = data["texts"][:3]

    p = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
        "name": f"TP: {tier_name}",
        "description": (
            f"Trustpilot reviewer in {tier_name} segment. N={data['count']}. "
            f"Key themes: {theme_str}. "
            f"Sample: \"{sample_texts[0][:100]}...\""
        ),
        "psychographic": {
            "satisfaction_tier": tier_name,
            "top_themes": [k for k, _ in top_themes],
        },
    }).json()
    persona_ids.append({"id": p["id"], "tier": tier_name, "n": data["count"]})
    print(f"Persona: {p['id']} — {tier_name} ({data['count']} reviews)")
    time.sleep(0.3)

# 4. Focus Group: "What moves you to 5 stars?"
fg = requests.post(f"{MV_BASE}/focus-groups", headers=MV_H, json={
    "name": "TrustScore Improvement Strategy",
    "persona_ids": [p["id"] for p in persona_ids],
    "questions": [
        "What would it take for you to give this company a 5-star review?",
        "What's the #1 thing this company does well that they should never change?",
        "If you could fix one thing about your experience, what would it be?",
        {"type": "likert", "text": "How likely are you to update your review if the company resolves your issue? (1-5)", "scale": 5},
        "Describe your ideal post-purchase experience in 2 sentences.",
    ],
    "responses_per_persona": 3,
}).json()

for _ in range(20):
    time.sleep(5)
    data = requests.get(f"{MV_BASE}/focus-groups/{fg['id']}", headers=MV_H).json()
    if data.get("status") == "completed":
        break

for resp in data.get("responses", []):
    tier = next((p["tier"] for p in persona_ids if p["id"] == resp.get("persona_id")), "?")
    print(f"\n[{tier}] {resp.get('question','')[:60]}")
    print(f"  → {resp.get('answer','')[:250]}")

const TP_KEY = process.env.TRUSTPILOT_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const BU_ID = process.env.TRUSTPILOT_BU_ID;
const TP_BASE = "https://api.trustpilot.com/v1";
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const TIERS = { "Detractor (1-2★)": [1, 2], "Neutral (3★)": [3], "Promoter (4-5★)": [4, 5] };

// 1. Pull per tier
const tierReviews = {};
for (const [tierName, starList] of Object.entries(TIERS)) {
  const allRevs = [];
  for (const stars of starList) {
    const res = await fetch(
      `${TP_BASE}/business-units/${BU_ID}/reviews?apikey=${TP_KEY}&stars=${stars}&perPage=50&orderBy=createdat.desc`
    );
    allRevs.push(...((await res.json()).reviews || []));
    await new Promise((r) => setTimeout(r, 200));
  }
  tierReviews[tierName] = allRevs;
}

// 2. Personas
const personaIds = [];
for (const [tierName, revs] of Object.entries(tierReviews)) {
  const texts = revs.filter((r) => r.text).map((r) => r.text.slice(0, 300));
  const p = await fetch(`${MV_BASE}/personas`, {
    method: "POST", headers: MV_H,
    body: JSON.stringify({
      name: `TP: ${tierName}`,
      description: `Trustpilot ${tierName}. N=${revs.length}. Sample: "${(texts[0] || "").slice(0, 100)}..."`,
      psychographic: { satisfaction_tier: tierName },
    }),
  }).then((r) => r.json());
  personaIds.push({ id: p.id, tier: tierName, n: revs.length });
  await new Promise((r) => setTimeout(r, 300));
}

// 3. Focus Group
const fg = await fetch(`${MV_BASE}/focus-groups`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    name: "TrustScore Improvement",
    persona_ids: personaIds.map((p) => p.id),
    questions: [
      "What would it take for you to give 5 stars?",
      "What's the #1 thing they do well?",
      "If you could fix one thing, what?",
      { type: "likert", text: "How likely to update review if issue resolved? (1-5)", scale: 5 },
      "Describe your ideal post-purchase experience.",
    ],
    responses_per_persona: 3,
  }),
}).then((r) => r.json());

let data;
for (let i = 0; i < 20; i++) {
  await new Promise((r) => setTimeout(r, 5000));
  data = await fetch(`${MV_BASE}/focus-groups/${fg.id}`, { headers: MV_H }).then((r) => r.json());
  if (data.status === "completed") break;
}

for (const resp of data.responses || []) {
  const tier = personaIds.find((p) => p.id === resp.persona_id)?.tier || "?";
  console.log(`\n[${tier}] ${(resp.question || "").slice(0, 60)}`);
  console.log(`  → ${(resp.answer || "").slice(0, 250)}`);
}