Offer Acceptance Analysis

import os, requests, time, base64

LV_KEY = os.environ["LEVER_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
LV_BASE = "https://api.lever.co/v1"
MV_BASE = "https://app.mavera.io/api/v1"
MV_H = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

lv_auth = base64.b64encode(f"{LV_KEY}:".encode()).decode()
LV_H = {"Authorization": f"Basic {lv_auth}"}

def lv_get(path, params=None):
    r = requests.get(f"{LV_BASE}{path}", headers=LV_H, params=params or {})
    if r.status_code == 429:
        time.sleep(1)
        return lv_get(path, params)
    r.raise_for_status()
    return r.json()

# 1. Pull archived opportunities
opps = []
offset = None
while len(opps) < 600:
    params = {"limit": 100, "archived_posting_id__exists": True}
    if offset:
        params["offset"] = offset
    resp = lv_get("/opportunities", params)
    opps.extend(resp.get("data", []))
    offset = resp.get("next")
    if not offset:
        break
    time.sleep(0.15)

# 2. Separate accepted (hired) vs declined
stages_resp = lv_get("/stages")
stage_map = {s["id"]: s["text"].lower() for s in stages_resp.get("data", [])}

accepted = []
declined = []
for opp in opps:
    stage_name = stage_map.get(opp.get("stage", ""), "")
    archive_reason = (opp.get("archivedReason") or "").lower()
    if "hired" in stage_name:
        accepted.append(opp)
    elif "offer" in archive_reason and ("declined" in archive_reason or "rejected" in archive_reason):
        declined.append(opp)
    elif "offer_declined" in archive_reason or "candidate_rejected" in archive_reason:
        declined.append(opp)

# 3. Build persona context
def extract_profile(opps_list):
    headlines = list({o.get("headline", "") for o in opps_list if o.get("headline")})[:5]
    sources = list({(o.get("sources") or ["Unknown"])[0] for o in opps_list})[:3]
    return headlines, sources

acc_headlines, acc_sources = extract_profile(accepted)
dec_headlines, dec_sources = extract_profile(declined)

# 4. Create personas
accepted_persona = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
    "name": "Lever: Offer Accepted",
    "description": (
        f"Candidates who accepted offers. N={len(accepted)}. "
        f"Headlines: {', '.join(acc_headlines[:3])}. Sources: {', '.join(acc_sources)}."
    ),
    "psychographic": {"decision": "accepted", "mindset": "Chose this company over alternatives"},
}).json()

declined_persona = requests.post(f"{MV_BASE}/personas", headers=MV_H, json={
    "name": "Lever: Offer Declined",
    "description": (
        f"Candidates who declined offers. N={len(declined)}. "
        f"Headlines: {', '.join(dec_headlines[:3])}. Sources: {', '.join(dec_sources)}."
    ),
    "psychographic": {"decision": "declined", "mindset": "Had a better option or dealbreaker"},
}).json()
time.sleep(0.3)

# 5. Focus Group
fg = requests.post(f"{MV_BASE}/focus-groups", headers=MV_H, json={
    "name": "Offer Acceptance Factor Analysis",
    "persona_ids": [accepted_persona["id"], declined_persona["id"]],
    "questions": [
        {"type": "ranking", "text": (
            "Rank these factors by importance to your offer decision: "
            "(A) Base compensation (B) Equity/stock options (C) Team/manager quality "
            "(D) Company mission/product (E) Remote/flexibility policy "
            "(F) Career growth opportunity (G) Interview experience "
            "(H) Speed of offer process"
        )},
        "What was the single most important factor in your decision to accept or decline?",
        "Was there a moment during the interview process that significantly influenced your final decision?",
        "If you declined: What would the company have needed to change about the offer for you to accept?",
        "How did the offer compare to your other options? Be specific about the gaps.",
        "Describe the ideal offer experience — from verbal offer to signed letter.",
    ],
    "context": f"Company offer data: {len(accepted)} accepted, {len(declined)} declined (acceptance rate: {len(accepted)/max(len(accepted)+len(declined),1)*100:.0f}%)",
    "responses_per_persona": 4,
}).json()

# 6. Poll
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

print(f"Acceptance rate: {len(accepted)}/{len(accepted)+len(declined)} ({len(accepted)/max(len(accepted)+len(declined),1)*100:.0f}%)")
print(f"Focus Group: {fg['id']}")
for resp in data.get("responses", []):
    persona = "Accepted" if resp.get("persona_id") == accepted_persona["id"] else "Declined"
    print(f"\n[{persona}] {resp.get('question','')[:70]}")
    print(f"  → {resp.get('answer','')[:300]}")

const LV_KEY = process.env.LEVER_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const LV_BASE = "https://api.lever.co/v1";
const MV_BASE = "https://app.mavera.io/api/v1";
const MV_H = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const LV_H = { Authorization: `Basic ${btoa(`${LV_KEY}:`)}` };

async function lvGet(path, params = {}) {
  const qs = new URLSearchParams(params).toString();
  const res = await fetch(`${LV_BASE}${path}?${qs}`, { headers: LV_H });
  if (res.status === 429) { await new Promise((r) => setTimeout(r, 1000)); return lvGet(path, params); }
  if (!res.ok) throw new Error(`Lever ${res.status}`);
  return res.json();
}

// 1. Archived opportunities
const opps = [];
let offset = null;
while (opps.length < 600) {
  const params = { limit: 100, archived_posting_id__exists: true };
  if (offset) params.offset = offset;
  const resp = await lvGet("/opportunities", params);
  opps.push(...(resp.data || []));
  offset = resp.next;
  if (!offset) break;
  await new Promise((r) => setTimeout(r, 150));
}

// 2. Separate
const stageMap = Object.fromEntries(
  ((await lvGet("/stages")).data || []).map((s) => [s.id, s.text.toLowerCase()])
);
const accepted = [], declined = [];
for (const opp of opps) {
  const stage = stageMap[opp.stage] || "";
  const reason = (opp.archivedReason || "").toLowerCase();
  if (stage.includes("hired")) accepted.push(opp);
  else if (reason.includes("declined") || reason.includes("rejected")) declined.push(opp);
}

// 3. Personas
const extract = (list) => ({
  headlines: [...new Set(list.map((o) => o.headline).filter(Boolean))].slice(0, 5),
  sources: [...new Set(list.map((o) => (o.sources || ["Unknown"])[0]))].slice(0, 3),
});
const accProfile = extract(accepted);
const decProfile = extract(declined);

const accPersona = await fetch(`${MV_BASE}/personas`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    name: "Lever: Offer Accepted",
    description: `Accepted offers. N=${accepted.length}. Headlines: ${accProfile.headlines.slice(0, 3).join(", ")}.`,
    psychographic: { decision: "accepted" },
  }),
}).then((r) => r.json());

const decPersona = await fetch(`${MV_BASE}/personas`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    name: "Lever: Offer Declined",
    description: `Declined offers. N=${declined.length}. Headlines: ${decProfile.headlines.slice(0, 3).join(", ")}.`,
    psychographic: { decision: "declined" },
  }),
}).then((r) => r.json());

// 4. Focus Group
const fg = await fetch(`${MV_BASE}/focus-groups`, {
  method: "POST", headers: MV_H,
  body: JSON.stringify({
    name: "Offer Acceptance Factors",
    persona_ids: [accPersona.id, decPersona.id],
    questions: [
      { type: "ranking", text: "Rank by importance: (A) Base comp (B) Equity (C) Team/manager (D) Mission (E) Flexibility (F) Growth (G) Interview experience (H) Offer speed" },
      "What was the single most important factor?",
      "Was there a pivotal moment during interviews?",
      "If declined: What change to the offer would have flipped your decision?",
      "How did this offer compare to alternatives? Be specific.",
      "Describe the ideal offer experience — verbal to signed.",
    ],
    context: `${accepted.length} accepted, ${declined.length} declined (${(accepted.length / Math.max(accepted.length + declined.length, 1) * 100).toFixed(0)}% rate)`,
    responses_per_persona: 4,
  }),
}).then((r) => r.json());

// 5. Poll
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;
}

const rate = (accepted.length / Math.max(accepted.length + declined.length, 1) * 100).toFixed(0);
console.log(`Acceptance rate: ${accepted.length}/${accepted.length + declined.length} (${rate}%)`);
for (const resp of data.responses || []) {
  const label = resp.persona_id === accPersona.id ? "Accepted" : "Declined";
  console.log(`\n[${label}] ${(resp.question || "").slice(0, 70)}`);
  console.log(`  → ${(resp.answer || "").slice(0, 300)}`);
}