Support Channel → Persona Pain Points

import os, requests, time

SL_TOKEN = os.environ["SLACK_BOT_TOKEN"]
SL_BASE = "https://slack.com/api"
SL_H = {"Authorization": f"Bearer {SL_TOKEN}"}
MV = os.environ["MAVERA_API_KEY"]
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

CHANNEL_ID = "C0123SUPPORT"  # Replace with your #support channel ID
DAYS_BACK = 14

# 1. Fetch support channel
oldest = str(int(time.time()) - DAYS_BACK * 86400)
messages = []
cursor = None
while True:
    params = {"channel": CHANNEL_ID, "limit": 200, "oldest": oldest}
    if cursor:
        params["cursor"] = cursor
    r = requests.get(f"{SL_BASE}/conversations.history", headers=SL_H, params=params)
    data = r.json()
    if not data.get("ok"):
        break
    messages.extend(data.get("messages", []))
    cursor = data.get("response_metadata", {}).get("next_cursor")
    if not cursor:
        break
    time.sleep(1)

tickets = [m for m in messages if m.get("type") == "message" and len(m.get("text","")) > 20]
print(f"Support input: {len(tickets)} (past {DAYS_BACK} days)")

# 2. Build corpus
corpus = "\n\n".join(
    f"[msg-{i}] {m.get('text','')[:400]}"
    for i, m in enumerate(tickets[-80:])
)

# 3. Mave categorization
analysis = requests.post(f"{MV_BASE}/mave/chat", headers=MV_H, json={
    "message": f"Customer support analyst. Categorize these {len(tickets)} support messages by PERSONA TYPE and PAIN POINT.\n\n"
        f"MESSAGES:\n{corpus[:8000]}\n\n"
        "PERSONA TYPES to identify:\n"
        "- Enterprise Admin (manages team accounts, SSO, billing)\n"
        "- SMB User (small team, price-sensitive, self-serve)\n"
        "- Developer (API, integrations, technical issues)\n"
        "- End User (uses the product daily, UX issues)\n\n"
        "For EACH persona:\n"
        "1. Pain points (ranked by frequency)\n"
        "2. Emotional intensity (1-10)\n"
        "3. Representative quotes (exact words from messages)\n"
        "4. Suggested marketing response (how to address in copy)\n"
        "5. Feature request signal (what product change would help)\n\n"
        "End with a PRIORITY MATRIX: persona × pain point × urgency."
}).json()

print(f"\n{'='*60}\nSUPPORT PAIN POINTS BY PERSONA\n{'='*60}")
print(analysis.get("content", "")[:3000])

const SL_TOKEN = process.env.SLACK_BOT_TOKEN;
const SL_BASE = "https://slack.com/api";
const SL_H = { Authorization: `Bearer ${SL_TOKEN}` };
const MV = process.env.MAVERA_API_KEY;
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };

const CHANNEL_ID = "C0123SUPPORT";
const DAYS_BACK = 14;
const oldest = String(Math.floor(Date.now() / 1000) - DAYS_BACK * 86400);

// 1. Fetch
const messages = [];
let cursor;
do {
  const params = new URLSearchParams({ channel: CHANNEL_ID, limit: "200", oldest });
  if (cursor) params.set("cursor", cursor);
  const data = await (await fetch(`${SL_BASE}/conversations.history?${params}`, { headers: SL_H })).json();
  if (!data.ok) break;
  messages.push(...(data.messages || []));
  cursor = data.response_metadata?.next_cursor;
  await new Promise(r => setTimeout(r, 1000));
} while (cursor);

const tickets = messages.filter(m => m.type === "message" && (m.text||"").length > 20);
console.log(`Support messages: ${tickets.length}`);

// 2. Corpus
const corpus = tickets.slice(-80).map((m, i) => `[msg-${i}] ${(m.text||"").slice(0,400)}`).join("\n\n");

// 3. Categorize
const analysis = await fetch(`${MV_BASE}/mave/chat`, { method: "POST", headers: MV_H,
  body: JSON.stringify({ message: `Categorize ${tickets.length} support messages by persona.\n\n${corpus.slice(0,8000)}\n\nPersonas: Enterprise Admin, SMB User, Developer, End User.\n\nPer persona: pain points (ranked), intensity (1-10), quotes, marketing response, feature signal.\n\nPRIORITY MATRIX: persona × pain × urgency.` }),
}).then(r => r.json());

console.log(`\n${"=".repeat(60)}\nSUPPORT PAIN POINTS BY PERSONA`);
console.log((analysis.content || "").slice(0, 3000));