CX Survey → Product Focus Group

import os, csv, io, zipfile, requests, time
from collections import defaultdict

QT = os.environ["QUALTRICS_TOKEN"]
DC = os.environ["QUALTRICS_DC"]
MV = os.environ["MAVERA_API_KEY"]
Q_BASE = f"https://{DC}.qualtrics.com/API/v3"
MB = "https://app.mavera.io/api/v1"
Q_H = {"X-API-TOKEN": QT, "Content-Type": "application/json"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

SURVEY_ID = os.environ.get("CX_SURVEY_ID", "SV_xxxxx")

TOUCHPOINTS = {
    "Onboarding": os.environ.get("COL_ONBOARDING", "CX_Onboarding"),
    "Product Usage": os.environ.get("COL_PRODUCT", "CX_Product"),
    "Support": os.environ.get("COL_SUPPORT", "CX_Support"),
    "Billing": os.environ.get("COL_BILLING", "CX_Billing"),
    "Renewal": os.environ.get("COL_RENEWAL", "CX_Renewal"),
}
THRESHOLD = 7.0

# 1. Export + parse
export = requests.post(f"{Q_BASE}/surveys/{SURVEY_ID}/export-responses",
    headers=Q_H, json={"format": "csv"}).json()
pid = export["result"]["progressId"]
file_id = None
for _ in range(60):
    time.sleep(5)
    s = requests.get(f"{Q_BASE}/surveys/{SURVEY_ID}/export-responses/{pid}",
        headers=Q_H).json()
    if s["result"].get("percentComplete") == 100:
        file_id = s["result"]["fileId"]; break

zip_data = requests.get(
    f"{Q_BASE}/surveys/{SURVEY_ID}/export-responses/{file_id}/file",
    headers=Q_H).content
with zipfile.ZipFile(io.BytesIO(zip_data)) as zf:
    csv_name = [n for n in zf.namelist() if n.endswith(".csv")][0]
    with zf.open(csv_name) as f:
        rows = list(csv.DictReader(io.TextIOWrapper(f, encoding="utf-8-sig")))
responses = [r for r in rows if r.get("Finished") == "1"]
print(f"CX responses: {len(responses)}")

# 2. Score touchpoints
tp_scores = {}
tp_comments = defaultdict(list)
for tp_name, col in TOUCHPOINTS.items():
    scores = []
    comment_col = f"{col}_Comment"
    for r in responses:
        try:
            val = float(r.get(col, 0) or 0)
            scores.append(val)
            if r.get(comment_col, "").strip():
                tp_comments[tp_name].append(r[comment_col].strip())
        except ValueError:
            continue
    if scores:
        tp_scores[tp_name] = {
            "avg": sum(scores) / len(scores),
            "n": len(scores),
            "low_count": sum(1 for s in scores if s < THRESHOLD),
            "low_pct": sum(1 for s in scores if s < THRESHOLD) / len(scores),
        }

print("\nTouchpoint Scores:")
for tp, data in sorted(tp_scores.items(), key=lambda x: x[1]["avg"]):
    flag = " ⚠️ BELOW THRESHOLD" if data["avg"] < THRESHOLD else ""
    print(f"  {tp}: {data['avg']:.1f}/10 (n={data['n']}, "
          f"{data['low_pct']:.0%} below {THRESHOLD}){flag}")

# 3. Create personas for low-scoring touchpoints
low_tps = {tp: data for tp, data in tp_scores.items() if data["avg"] < THRESHOLD}

if not low_tps:
    print("\nAll touchpoints above threshold. No focus groups needed.")
    exit()

persona_ids = {}
for tp_name, data in low_tps.items():
    comments = tp_comments.get(tp_name, [])[:5]
    comment_str = "; ".join(comments[:3]) if comments else "No comments"

    p = requests.post(f"{MB}/personas", headers=MV_H, json={
        "name": f"QX CX: Dissatisfied at {tp_name}",
        "description": (
            f"Customer who scored {tp_name} below {THRESHOLD}/10. "
            f"Avg score: {data['avg']:.1f}. {data['low_count']}/{data['n']} "
            f"respondents dissatisfied. Feedback: {comment_str[:200]}"
        ),
        "psychographic": {
            "satisfaction": "low",
            "touchpoint": tp_name,
            "avg_score": data["avg"],
        },
    })
    p.raise_for_status()
    persona_ids[tp_name] = p.json()["id"]
    time.sleep(0.3)

# 4. Focus Group per touchpoint
for tp_name, pid in persona_ids.items():
    data = low_tps[tp_name]
    comments = tp_comments.get(tp_name, [])

    fg = requests.post(f"{MB}/focus-groups", headers=MV_H, json={
        "name": f"CX Deep-Dive: {tp_name}",
        "persona_ids": [pid],
        "context": (
            f"You recently experienced the {tp_name.lower()} process and "
            f"gave it {data['avg']:.1f}/10. Other customers in your "
            f"situation said: {'; '.join(comments[:3])}"
        ),
        "questions": [
            f"Describe your {tp_name.lower()} experience in detail. What happened?",
            "What was the single most frustrating moment?",
            "What would have made this experience a 9 or 10?",
            "How does this experience affect your likelihood to renew or recommend us?",
            "If you were redesigning this process, what would you change first?",
            f"Compare your {tp_name.lower()} experience to the best you've had with any vendor.",
        ],
        "responses_per_persona": 3,
    }).json()

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

    print(f"\n{'='*50}")
    print(f"CX DEEP-DIVE: {tp_name} (avg: {data['avg']:.1f}/10)")
    print(f"{'='*50}")
    for resp in result.get("responses", []):
        print(f"\nQ: {resp.get('question', '')[:60]}...")
        print(f"A: {resp.get('answer', '')[:300]}")

# 5. Generate improvement plan
all_touchpoint_data = "\n".join(
    f"- {tp}: {d['avg']:.1f}/10, {d['low_pct']:.0%} dissatisfied"
    for tp, d in sorted(tp_scores.items(), key=lambda x: x[1]["avg"]))
all_comments = "\n".join(
    f"[{tp}] {c}"
    for tp, comments in tp_comments.items()
    for c in comments[:3])

plan = requests.post(f"{MB}/mave/chat", headers=MV_H, json={
    "message": f"""Create a prioritized CX improvement plan based on this data.

TOUCHPOINT SCORES:
{all_touchpoint_data}

SAMPLE CUSTOMER FEEDBACK:
{all_comments[:2000]}

Produce:
1) Priority ranking (which touchpoint to fix first and why)
2) Root cause analysis per low-scoring touchpoint
3) Quick wins (implementable in 2 weeks)
4) Medium-term improvements (1-3 months)
5) Metrics to track improvement
6) Estimated impact on overall NPS"""
}).json()

print(f"\n{'='*50}")
print("CX IMPROVEMENT PLAN")
print(f"{'='*50}")
print(plan.get("content", "")[:2000])

const QT = process.env.QUALTRICS_TOKEN;
const DC = process.env.QUALTRICS_DC;
const MV = process.env.MAVERA_API_KEY;
const Q_BASE = `https://${DC}.qualtrics.com/API/v3`;
const MB = "https://app.mavera.io/api/v1";
const qH = { "X-API-TOKEN": QT, "Content-Type": "application/json" };
const mvH = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const SURVEY_ID = process.env.CX_SURVEY_ID || "SV_xxxxx";
const TOUCHPOINTS = {
  Onboarding: process.env.COL_ONBOARDING || "CX_Onboarding",
  "Product Usage": process.env.COL_PRODUCT || "CX_Product",
  Support: process.env.COL_SUPPORT || "CX_Support",
  Billing: process.env.COL_BILLING || "CX_Billing",
  Renewal: process.env.COL_RENEWAL || "CX_Renewal",
};
const THRESHOLD = 7.0;

// 1. Export + parse
const JSZip = (await import("jszip")).default;
const exp = await fetch(`${Q_BASE}/surveys/${SURVEY_ID}/export-responses`,
  { method: "POST", headers: qH, body: JSON.stringify({ format: "csv" }) }).then(r => r.json());
let fileId = null;
for (let i = 0; i < 60; i++) {
  await new Promise(r => setTimeout(r, 5000));
  const s = await fetch(`${Q_BASE}/surveys/${SURVEY_ID}/export-responses/${exp.result.progressId}`,
    { headers: qH }).then(r => r.json());
  if (s.result?.percentComplete === 100) { fileId = s.result.fileId; break; }
}
const zipBuf = await fetch(`${Q_BASE}/surveys/${SURVEY_ID}/export-responses/${fileId}/file`,
  { headers: qH }).then(r => r.arrayBuffer());
const zip = await JSZip.loadAsync(zipBuf);
const csvFile = Object.keys(zip.files).find(n => n.endsWith(".csv"));
const csvText = await zip.files[csvFile].async("string");
const lines = csvText.split("\n");
const hdrs = lines[0].split(",").map(h => h.replace(/"/g, "").trim());
const responses = lines.slice(3).filter(l => l.trim()).map(line => {
  const vals = line.match(/(".*?"|[^,]*)/g) || [];
  const obj = {};
  hdrs.forEach((h, i) => { obj[h] = (vals[i] || "").replace(/"/g, "").trim(); });
  return obj;
}).filter(r => r.Finished === "1");

// 2. Score touchpoints
const tpScores = {};
const tpComments = {};
for (const [tpName, col] of Object.entries(TOUCHPOINTS)) {
  const scores = [];
  tpComments[tpName] = [];
  for (const r of responses) {
    const val = parseFloat(r[col] || "0");
    if (!isNaN(val)) scores.push(val);
    if (r[`${col}_Comment`]?.trim()) tpComments[tpName].push(r[`${col}_Comment`].trim());
  }
  if (scores.length) {
    const avg = scores.reduce((a, b) => a + b, 0) / scores.length;
    const lowCount = scores.filter(s => s < THRESHOLD).length;
    tpScores[tpName] = { avg, n: scores.length, low_count: lowCount, low_pct: lowCount / scores.length };
  }
}

// 3. Focus groups for low-scoring
const lowTps = Object.entries(tpScores).filter(([, d]) => d.avg < THRESHOLD);
const personaIds = {};
for (const [tp, data] of lowTps) {
  const comments = (tpComments[tp] || []).slice(0, 3).join("; ");
  const p = await fetch(`${MB}/personas`, { method: "POST", headers: mvH,
    body: JSON.stringify({ name: `QX CX: Dissatisfied at ${tp}`,
      description: `Score ${data.avg.toFixed(1)}/10. ${data.low_count}/${data.n} dissatisfied. Feedback: ${comments.slice(0, 200)}`,
      psychographic: { satisfaction: "low", touchpoint: tp, avg_score: data.avg } }),
  }).then(r => r.json());
  personaIds[tp] = p.id;
  await new Promise(r => setTimeout(r, 300));
}

for (const [tp, pid] of Object.entries(personaIds)) {
  const data = tpScores[tp];
  const fg = await fetch(`${MB}/focus-groups`, { method: "POST", headers: mvH,
    body: JSON.stringify({ name: `CX: ${tp}`, persona_ids: [pid],
      context: `You scored ${tp.toLowerCase()} ${data.avg.toFixed(1)}/10.`,
      questions: [ `Describe your ${tp.toLowerCase()} experience.`,
        "Single most frustrating moment?", "What would make it 9 or 10?",
        "How does this affect renewal/recommendation?",
        "If you redesigned this, what changes first?",
        `Compare to best ${tp.toLowerCase()} experience with any vendor.` ],
      responses_per_persona: 3 }),
  }).then(r => r.json());

  let result;
  for (let i = 0; i < 20; i++) {
    await new Promise(r => setTimeout(r, 5000));
    result = await fetch(`${MB}/focus-groups/${fg.id}`, { headers: mvH }).then(r => r.json());
    if (result.status === "completed") break;
  }
  console.log(`\n${"=".repeat(50)}\nCX: ${tp} (${data.avg.toFixed(1)}/10)\n${"=".repeat(50)}`);
  for (const resp of result.responses || [])
    console.log(`Q: ${(resp.question || "").slice(0, 50)}...\nA: ${(resp.answer || "").slice(0, 300)}\n`);
}

// 4. Improvement plan
const plan = await fetch(`${MB}/mave/chat`, { method: "POST", headers: mvH,
  body: JSON.stringify({ message: `CX improvement plan based on:\n${Object.entries(tpScores).map(([tp, d]) => `- ${tp}: ${d.avg.toFixed(1)}/10`).join("\n")}\n\nProduce: 1) Priority ranking 2) Root causes 3) Quick wins 4) Medium-term 5) Metrics 6) NPS impact` }),
}).then(r => r.json());
console.log(`\n${"=".repeat(50)}\nIMPROVEMENT PLAN\n${"=".repeat(50)}`);
console.log((plan.content || "").slice(0, 2000));