Feature Request Aggregation → Product Focus Group

import os, requests, time, json

G2 = os.environ["G2_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
G2_H = {"Authorization": f"Token token={G2}", "Content-Type": "application/vnd.api+json"}
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

# 1. Pull reviews
reviews = []
page = 1
while len(reviews) < 200:
    r = requests.get(f"https://data.g2.com/api/v1/survey-responses",
        headers=G2_H, params={"page[size]": 50, "page[number]": page})
    if r.status_code == 429: time.sleep(1); continue
    r.raise_for_status()
    data = r.json().get("data", [])
    if not data: break
    reviews.extend(data)
    page += 1
    time.sleep(0.1)

# 2. Extract "dislike" and "recommendation" text
feature_texts = []
for rev in reviews:
    attrs = rev.get("attributes", {})
    for key, val in attrs.get("comment_answers", {}).items():
        text = val if isinstance(val, str) else val.get("text", "")
        k = key.lower()
        if ("dislike" in k or "wish" in k or "improve" in k or "recommend" in k) and text.strip():
            feature_texts.append(text[:300])

# 3. Use Mave to categorize feature requests
from openai import OpenAI
mavera = OpenAI(api_key=MV, base_url="https://app.mavera.io/api/v1")

feature_block = "\n".join(f"{i+1}. {t}" for i, t in enumerate(feature_texts[:50]))

categorization = mavera.responses.create(model="mavera-1",
    input=[{"role": "user", "content": f"""Categorize these G2 review comments into feature request categories.

{feature_block}

Return JSON with: feature_name, description, mention_count, representative_quotes (2 each).
Group similar requests. Minimum 3 mentions to include."""}],
    extra_body={"response_format": {"type": "json_object"}})

features = json.loads(categorization.output[0].content[0].text)
feature_list = features.get("features", features.get("feature_requests", []))
print(f"Identified {len(feature_list)} feature request categories")

# 4. Create product user personas
PRODUCT_PERSONAS = [
    {"name": "Power User PM", "desc": "Uses product daily. Deeply invested in feature depth."},
    {"name": "Executive Sponsor", "desc": "Bought the product. Cares about ROI and team adoption."},
    {"name": "New User", "desc": "Onboarding. Wants simplicity and quick wins."},
]

persona_ids = []
for pp in PRODUCT_PERSONAS:
    p = requests.post(f"https://app.mavera.io/api/v1/personas", headers=MV_H, json={
        "name": f"G2: {pp['name']}", "description": pp["desc"],
    }).json()
    persona_ids.append(p["id"])
    time.sleep(0.2)

# 5. Focus Group: rank features
feature_options = "\n".join(
    f"({chr(65+i)}) {f.get('feature_name', f.get('name', 'Unknown'))}: {f.get('description','')[:80]}"
    for i, f in enumerate(feature_list[:8])
)

fg = requests.post(f"https://app.mavera.io/api/v1/focus-groups", headers=MV_H, json={
    "name": "G2 Feature Request Prioritization",
    "persona_ids": persona_ids,
    "questions": [
        {"type": "ranking", "text": f"Rank these features by impact on YOUR workflow:\n{feature_options}"},
        "Which feature would make you upgrade to a higher tier?",
        "Which feature, if missing, would make you consider switching products?",
        "Describe a workflow you currently can't do that these features would enable.",
    ],
    "responses_per_persona": 3,
}).json()

for _ in range(20):
    time.sleep(5)
    data = requests.get(f"https://app.mavera.io/api/v1/focus-groups/{fg['id']}", headers=MV_H).json()
    if data.get("status") == "completed": break

print(f"\nFocus Group: {fg['id']}")
for resp in data.get("responses", []):
    print(f"\n{resp.get('question','')[:60]}")
    print(f"  → {resp.get('answer','')[:300]}")

const G2 = process.env.G2_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const G2_H = { Authorization: `Token token=${G2}`, "Content-Type": "application/vnd.api+json" };
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const OpenAI = require("openai").default;

// 1. Pull reviews
const reviews = [];
let page = 1;
while (reviews.length < 200) {
  const res = await fetch(`https://data.g2.com/api/v1/survey-responses?page[size]=50&page[number]=${page}`, { headers: G2_H });
  if (res.status === 429) { await new Promise((r) => setTimeout(r, 1000)); continue; }
  const data = (await res.json()).data || [];
  if (!data.length) break;
  reviews.push(...data);
  page++;
  await new Promise((r) => setTimeout(r, 100));
}

// 2. Extract feature requests
const featureTexts = [];
for (const rev of reviews) {
  for (const [key, val] of Object.entries(rev.attributes?.comment_answers || {})) {
    const text = typeof val === "string" ? val : val?.text || "";
    const k = key.toLowerCase();
    if ((k.includes("dislike") || k.includes("wish") || k.includes("improve")) && text.trim())
      featureTexts.push(text.slice(0, 300));
  }
}

// 3. Categorize
const mavera = new OpenAI({ apiKey: MV, baseURL: "https://app.mavera.io/api/v1" });
const cat = await mavera.responses.create({
  model: "mavera-1",
  input: [{ role: "user", content: `Categorize into feature requests:\n\n${featureTexts.slice(0, 50).map((t, i) => `${i + 1}. ${t}`).join("\n")}\n\nReturn JSON: features[{feature_name, description, mention_count}].` }],
  extra_body: { response_format: { type: "json_object" } },
});
const features = JSON.parse(cat.output[0].content[0].text).features || [];

// 4. Personas + Focus Group
const personaIds = [];
for (const pp of [
  { name: "Power User PM", desc: "Daily user. Wants depth." },
  { name: "Executive Sponsor", desc: "Bought it. Cares about ROI." },
  { name: "New User", desc: "Onboarding. Wants simplicity." },
]) {
  const p = await fetch("https://app.mavera.io/api/v1/personas", {
    method: "POST", headers: MV_H,
    body: JSON.stringify({ name: `G2: ${pp.name}`, description: pp.desc }),
  }).then((r) => r.json());
  personaIds.push(p.id);
  await new Promise((r) => setTimeout(r, 200));
}

const options = features.slice(0, 8).map((f, i) =>
  `(${String.fromCharCode(65 + i)}) ${f.feature_name || f.name}: ${(f.description || "").slice(0, 80)}`
).join("\n");

const fg = await fetch("https://app.mavera.io/api/v1/focus-groups", {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    name: "G2 Feature Prioritization",
    persona_ids: personaIds,
    questions: [
      { type: "ranking", text: `Rank by workflow impact:\n${options}` },
      "Which feature would make you upgrade?",
      "Which missing feature would make you switch?",
      "What workflow can't you currently do?",
    ],
    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(`https://app.mavera.io/api/v1/focus-groups/${fg.id}`, { headers: MV_H }).then((r) => r.json());
  if (data.status === "completed") break;
}

for (const resp of data.responses || []) {
  console.log(`\n${(resp.question || "").slice(0, 60)}`);
  console.log(`  → ${(resp.answer || "").slice(0, 300)}`);
}