Message Testing Matrix

import os, time, json, requests

API_KEY = os.environ["MAVERA_API_KEY"]
BASE = "https://app.mavera.io/api/v1"
HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

MESSAGES = {
    "value": {"label": "Value-First", "headline": "Cut Research Costs by 80%",
     "body": "Traditional audience research costs $15K–$50K per study. Mavera delivers the same insights for a fraction, in minutes."},
    "speed": {"label": "Speed-First", "headline": "From Question to Insight in 10 Minutes",
     "body": "Stop waiting weeks. Mavera's AI personas deliver focus group-quality feedback before your next standup."},
    "trust": {"label": "Trust & Credibility", "headline": "Research You Can Bet Your Campaign On",
     "body": "Personas built on data from millions of real consumers. 87% predictive accuracy. Trusted by 200+ marketing teams."},
    "emotion": {"label": "Emotional", "headline": "Never Launch Blind Again",
     "body": "That sinking feeling when a campaign underperforms? It starts with skipping research. Mavera makes it so fast, there's no excuse."},
    "technical": {"label": "Technical", "headline": "API-First Audience Intelligence",
     "body": "OpenAI-compatible Chat, Focus Groups with 12 question types, Video Analysis, Brand Voice extraction. One API key, unlimited surfaces."},
}

PERSONA_DEFS = [
    {"name": "Startup CMO", "role": "CMO, Series B startup (50 people, $8M ARR)",
     "description": "Built the team from scratch. Budget-conscious but growth-aggressive. Measures everything by pipeline impact.",
     "traits": ["growth-focused", "budget-aware", "hands-on", "speed-oriented"]},
    {"name": "Enterprise Brand Mgr", "role": "Senior Brand Manager, Fortune 500 CPG",
     "description": "Manages $200M brand. Uses Nielsen, Kantar, Ipsos. Needs bulletproof methodology for internal buy-in.",
     "traits": ["process-driven", "methodology-focused", "risk-averse", "data-rigorous"]},
    {"name": "Agency Strategist", "role": "VP Strategy, creative agency with 40 clients",
     "description": "Strategic frameworks across all accounts. Differentiates in pitches. Needs tools that scale across clients.",
     "traits": ["multi-client thinker", "pitch-oriented", "insight-driven", "scalability-focused"]},
    {"name": "Product Marketer", "role": "PMM, mid-stage SaaS company",
     "description": "Owns positioning and launch for 3 product lines. Validates messaging before launch. Enables sales with battlecards.",
     "traits": ["cross-functional", "launch-focused", "sales-enablement-oriented", "messaging-obsessed"]},
    {"name": "Solo Consultant", "role": "Independent marketing consultant, 5–8 clients",
     "description": "One-person operation. Uses research to justify recs. Every dollar comes from margin. Needs tools that make one person look like a team.",
     "traits": ["price-sensitive", "time-constrained", "credibility-seeking", "simplicity-driven"]},
]

def create_persona(p):
    resp = requests.post(f"{BASE}/personas", headers=HEADERS, json={
        "name": p["name"], "role": p["role"], "description": p["description"], "traits": p["traits"]})
    resp.raise_for_status()
    data = resp.json()
    print(f"  Created: {data['name']} → {data['id']}")
    return data["id"]

persona_map = {p["name"]: create_persona(p) for p in PERSONA_DEFS}

def build_questions(msg):
    block = f"**{msg['headline']}**\n\n{msg['body']}"
    return [
        {"question": f"Rate how compelling this is to you (0-10):\n\n{block}", "type": "NPS", "order": 1},
        {"question": "How would you describe this product to a colleague?", "type": "OPEN_ENDED", "order": 2},
        {"question": "Strongest element?", "type": "MULTIPLE_CHOICE",
         "options": ["Headline", "Value proposition", "Proof points", "Tone/voice", "Nothing stood out"], "order": 3},
        {"question": "Biggest gap or weakness?", "type": "OPEN_ENDED", "order": 4},
        {"question": "Would this make you want to learn more?", "type": "MULTIPLE_CHOICE",
         "options": ["Yes — click immediately", "Probably — with right CTA", "Unlikely — not relevant", "No — off-putting"], "order": 5},
    ]

def poll_fg(fg_id, timeout_min=10):
    for _ in range(timeout_min * 6):
        resp = requests.get(f"{BASE}/focus-groups/{fg_id}", headers=HEADERS).json()
        if "error" in resp: raise Exception(resp["error"]["message"])
        if resp["status"] == "COMPLETED": return resp
        time.sleep(10)
    raise TimeoutError(f"Focus Group {fg_id} timed out")

print("Creating 25 Focus Groups...")
matrix_jobs = {}
for mk, msg in MESSAGES.items():
    for pname, pid in persona_map.items():
        key = f"{mk}|{pname}"
        resp = requests.post(f"{BASE}/focus-groups", headers=HEADERS, json={
            "name": f"Matrix: {msg['label']} × {pname}",
            "persona_ids": [pid], "sample_size": 5, "questions": build_questions(msg),
        })
        resp.raise_for_status()
        matrix_jobs[key] = resp.json()["id"]

print(f"{len(matrix_jobs)} Focus Groups created. Polling...")
matrix_results = {k: poll_fg(fid) for k, fid in matrix_jobs.items()}
print("All 25 complete.")

def build_and_print_matrix(results):
    msg_keys = list(MESSAGES.keys())
    p_names = list(persona_map.keys())

    matrix = {}
    for mk in msg_keys:
        matrix[mk] = {}
        for pn in p_names:
            cell = results.get(f"{mk}|{pn}", {})
            nps = None
            for qr in cell.get("results", []):
                if qr["type"] == "NPS":
                    nps = qr.get("nps_score")
                    break
            matrix[mk][pn] = nps

    print("\n" + "=" * 90)
    print("MESSAGE-PERSONA FIT MATRIX (NPS Scores)")
    print("=" * 90)

    header = f"{'Message':<18}" + "".join(f" {pn:>14}" for pn in p_names) + f" {'AVG':>8}"
    print(header)
    print("─" * 90)

    best = (None, -100)
    for mk in msg_keys:
        label = MESSAGES[mk]["label"]
        scores = [matrix[mk].get(pn) for pn in p_names if matrix[mk].get(pn) is not None]
        avg = sum(scores) / len(scores) if scores else 0
        row = f"{label:<18}" + "".join(
            f" {matrix[mk].get(pn, 'N/A'):>14}" for pn in p_names) + f" {avg:>7.1f}"
        print(row)
        if avg > best[1]: best = (mk, avg)

    print("─" * 90)
    print(f"\n  Best overall: {MESSAGES[best[0]]['label']} (avg NPS: {best[1]:.1f})")
    for pn in p_names:
        bm = max(msg_keys, key=lambda mk: matrix[mk].get(pn, -100) or -100)
        print(f"  Best for {pn}: {MESSAGES[bm]['label']} (NPS: {matrix[bm].get(pn)})")

    return matrix

matrix = build_and_print_matrix(matrix_results)