> ## Documentation Index > Fetch the complete documentation index at: https://docs.mavera.io/llms.txt > Use this file to discover all available pages before exploring further. # Message Testing Matrix > Test 5 message variants × 5 personas = 25 Focus Group combinations to find optimal message-persona fit ## The Scenario You have five different ways to describe your product and five distinct audience segments. Which message works best for which audience? You build a message-testing matrix: every message gets evaluated by every persona, producing a quantitative fit score for each cell. The output is a heat map of message-persona fit that tells you exactly which message to use for which audience. **Mavera-only workflow.** No survey platforms, no panel vendors, no statistical software. Just Mavera's Personas and Focus Groups surfaces. *** ## When to Use This * Pre-launch messaging finalization — pick the winning variant per audience segment. * Channel-specific copy — different channels reach different personas. * Website personalization — serve the right headline to the right visitor segment. * Sales enablement — give each rep the message that resonates with their territory. *** ## Architecture ```mermaid theme={"dark"} flowchart LR A["Define 5 Messages"] --> B["Define 5 Personas"] B --> C["Run 25 Focus Groups"] C --> D["Build Fit Matrix"] D --> E["Report"] ``` | Mavera Surface | Role in Pipeline | | --------------------------------------- | ----------------------------------------------------- | | **Personas** (`POST /personas`) | Create 5 distinct audience segments | | **Focus Groups** (`POST /focus-groups`) | Test each message with each persona (25 combinations) | *** ## What You Need | Requirement | Details | | ---------------------------------- | ---------------------------------------------------------------------------------------------------- | | **Mavera API key** | Starts with `mvra_live_`. Get one at [Developer Settings](https://app.mavera.io/settings/developer). | | **Python 3.8+** or **Node.js 18+** | `requests` for Python; native `fetch` for Node. | | **Credits** | \~375–650 total. See [Credits Estimate](#credits-estimate). | ``` MAVERA_API_KEY=mvra_live_your_key_here ``` *** ## Step 1 — Define 5 Message Variants Each message takes a different angle: value, speed, trust, emotion, or technical. ```python Python theme={"dark"} 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."}, } ``` ```javascript JavaScript theme={"dark"} const API_KEY = process.env.MAVERA_API_KEY; const BASE = "https://app.mavera.io/api/v1"; const HEADERS = { Authorization: `Bearer ${API_KEY}`, "Content-Type": "application/json" }; const MESSAGES = { value: { label: "Value-First", headline: "Cut Research Costs by 80%", body: "Traditional 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. 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. 200+ teams." }, emotion: { label: "Emotional", headline: "Never Launch Blind Again", body: "That sinking feeling when a campaign underperforms? 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, Video Analysis, Brand Voice. One API key, unlimited surfaces." }, }; ``` *** ## Step 2 — Create 5 Personas ```python Python theme={"dark"} 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} ``` ```javascript JavaScript theme={"dark"} const PERSONA_DEFS = [ { name: "Startup CMO", role: "CMO, Series B startup", description: "Built team from scratch. Budget-conscious but growth-aggressive. Pipeline-focused.", traits: ["growth-focused", "budget-aware", "hands-on", "speed-oriented"] }, { name: "Enterprise Brand Mgr", role: "Senior Brand Manager, Fortune 500 CPG", description: "$200M brand. Uses Nielsen, Kantar. Needs bulletproof methodology.", traits: ["process-driven", "methodology-focused", "risk-averse", "data-rigorous"] }, { name: "Agency Strategist", role: "VP Strategy, creative agency", description: "Frameworks across 40 accounts. Differentiates in pitches. Needs scale.", traits: ["multi-client thinker", "pitch-oriented", "insight-driven", "scalability-focused"] }, { name: "Product Marketer", role: "PMM, mid-stage SaaS", description: "Owns positioning for 3 product lines. Validates before launch. Enables sales.", traits: ["cross-functional", "launch-focused", "sales-enablement-oriented", "messaging-obsessed"] }, { name: "Solo Consultant", role: "Independent consultant, 5–8 clients", description: "One-person op. Research justifies recs. Every dollar from margin.", traits: ["price-sensitive", "time-constrained", "credibility-seeking", "simplicity-driven"] }, ]; async function createPersona(p) { const resp = await fetch(`${BASE}/personas`, { method: "POST", headers: HEADERS, body: JSON.stringify({ name: p.name, role: p.role, description: p.description, traits: p.traits }) }); const data = await resp.json(); if (data.error) throw new Error(data.error.message); return data.id; } const personaMap = {}; for (const p of PERSONA_DEFS) personaMap[p.name] = await createPersona(p); ``` *** ## Step 3 — Run the 5×5 Matrix Create all 25 Focus Groups, then poll for completion. Mavera processes them concurrently. ```python Python theme={"dark"} 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.") ``` ```javascript JavaScript theme={"dark"} function buildQuestions(msg) { const block = `**${msg.headline}**\n\n${msg.body}`; return [ { question: `Rate how compelling (0-10):\n\n${block}`, type: "NPS", order: 1 }, { question: "How would you describe this to a colleague?", type: "OPEN_ENDED", order: 2 }, { question: "Strongest element?", type: "MULTIPLE_CHOICE", options: ["Headline", "Value proposition", "Proof points", "Tone/voice", "Nothing stood out"], order: 3 }, { question: "Biggest gap or weakness?", type: "OPEN_ENDED", order: 4 }, { question: "Would this make you want to learn more?", type: "MULTIPLE_CHOICE", options: ["Yes — click immediately", "Probably — with right CTA", "Unlikely", "No — off-putting"], order: 5 }, ]; } async function pollFG(fgId, timeoutMin = 10) { for (let i = 0; i < timeoutMin * 6; i++) { const resp = await fetch(`${BASE}/focus-groups/${fgId}`, { headers: HEADERS }).then((r) => r.json()); if (resp.error) throw new Error(resp.error.message); if (resp.status === "COMPLETED") return resp; await new Promise((r) => setTimeout(r, 10000)); } throw new Error("Timed out"); } const matrixJobs = {}; for (const [mk, msg] of Object.entries(MESSAGES)) { for (const [pname, pid] of Object.entries(personaMap)) { const key = `${mk}|${pname}`; const resp = await fetch(`${BASE}/focus-groups`, { method: "POST", headers: HEADERS, body: JSON.stringify({ name: `Matrix: ${msg.label} × ${pname}`, persona_ids: [pid], sample_size: 5, questions: buildQuestions(msg) }) }); matrixJobs[key] = (await resp.json()).id; } } const matrixResults = {}; for (const [k, fid] of Object.entries(matrixJobs)) matrixResults[k] = await pollFG(fid); ``` 25 Focus Groups take 5–15 minutes total. They run concurrently — the bottleneck is the slowest individual group. *** ## Step 4 — Build and Display the Fit Matrix ```python Python theme={"dark"} def build_and_print_matrix(results): msg_keys = list(MESSAGES.keys()) p_names = list(persona_map.keys()) matrix = {} for mk in msg_keys: matrix[mk] = {} for pn in p_names: cell = results.get(f"{mk}|{pn}", {}) nps = None for qr in cell.get("results", []): if qr["type"] == "NPS": nps = qr.get("nps_score") break matrix[mk][pn] = nps print("\n" + "=" * 90) print("MESSAGE-PERSONA FIT MATRIX (NPS Scores)") print("=" * 90) header = f"{'Message':<18}" + "".join(f" {pn:>14}" for pn in p_names) + f" {'AVG':>8}" print(header) print("─" * 90) best = (None, -100) for mk in msg_keys: label = MESSAGES[mk]["label"] scores = [matrix[mk].get(pn) for pn in p_names if matrix[mk].get(pn) is not None] avg = sum(scores) / len(scores) if scores else 0 row = f"{label:<18}" + "".join( f" {matrix[mk].get(pn, 'N/A'):>14}" for pn in p_names) + f" {avg:>7.1f}" print(row) if avg > best[1]: best = (mk, avg) print("─" * 90) print(f"\n Best overall: {MESSAGES[best[0]]['label']} (avg NPS: {best[1]:.1f})") for pn in p_names: bm = max(msg_keys, key=lambda mk: matrix[mk].get(pn, -100) or -100) print(f" Best for {pn}: {MESSAGES[bm]['label']} (NPS: {matrix[bm].get(pn)})") return matrix matrix = build_and_print_matrix(matrix_results) ``` ```javascript JavaScript theme={"dark"} function buildAndPrintMatrix(results) { const msgKeys = Object.keys(MESSAGES); const pNames = Object.keys(personaMap); const matrix = {}; for (const mk of msgKeys) { matrix[mk] = {}; for (const pn of pNames) { const cell = results[`${mk}|${pn}`] || {}; matrix[mk][pn] = (cell.results || []).find((q) => q.type === "NPS")?.nps_score ?? null; } } console.log("\nMESSAGE-PERSONA FIT MATRIX (NPS)"); console.log("─".repeat(90)); for (const mk of msgKeys) { const scores = pNames.map((pn) => matrix[mk][pn]).filter((s) => s !== null); const avg = scores.length ? (scores.reduce((a, b) => a + b) / scores.length).toFixed(1) : "N/A"; console.log(` ${MESSAGES[mk].label.padEnd(18)} ${pNames.map((pn) => String(matrix[mk][pn] ?? "N/A").padStart(14)).join("")} ${String(avg).padStart(8)}`); } for (const pn of pNames) { const best = msgKeys.reduce((b, mk) => (matrix[mk][pn] || -100) > (matrix[b][pn] || -100) ? mk : b); console.log(` Best for ${pn}: ${MESSAGES[best].label} (NPS: ${matrix[best][pn]})`); } return matrix; } buildAndPrintMatrix(matrixResults); ``` *** ## Example Output ``` MESSAGE-PERSONA FIT MATRIX (NPS Scores) ══════════════════════════════════════════════════════════════════════════════════════ Message Startup CMO Enterprise BM Agency Strat Product Mktr Solo Conslt AVG ────────────────────────────────────────────────────────────────────────────────────── Value-First 72 35 58 55 82 60.4 Speed-First 85 28 65 70 75 64.6 Trust & Credibility 40 78 55 48 30 50.2 Emotional 68 22 70 62 55 55.4 Technical 55 45 38 80 20 47.6 ────────────────────────────────────────────────────────────────────────────────────── Best overall: Speed-First (avg NPS: 64.6) Best for Startup CMO: Speed-First (NPS: 85) Best for Enterprise Brand Mgr: Trust & Credibility (NPS: 78) Best for Agency Strategist: Emotional (NPS: 70) Best for Product Marketer: Technical (NPS: 80) Best for Solo Consultant: Value-First (NPS: 82) ``` *** ## Variations ```python theme={"dark"} import csv with open("matrix.csv", "w", newline="") as f: w = csv.writer(f) w.writerow(["Message", "Persona", "NPS"]) for mk in MESSAGES: for pn in persona_map: w.writerow([MESSAGES[mk]["label"], pn, matrix[mk].get(pn, "N/A")]) ``` Bump `sample_size` from 5 to 25 for more stable NPS. Credits scale linearly. ```python theme={"dark"} new_msg = {"label": "Social Proof", "headline": "Join 500+ Teams", "body": "..."} for pn, pid in persona_map.items(): resp = requests.post(f"{BASE}/focus-groups", headers=HEADERS, json={ "name": f"Matrix: Social Proof × {pn}", "persona_ids": [pid], "sample_size": 5, "questions": build_questions(new_msg)}) ``` ```python theme={"dark"} worst = min(matrix, key=lambda mk: sum((matrix[mk].get(pn) or 0) for pn in persona_map)) gen_resp = requests.post(f"{BASE}/generations", headers=HEADERS, json={ "app": "ad_copy", "inputs": {"original": json.dumps(MESSAGES[worst]), "feedback": "Underperformed across all persona segments. Improve."}}).json() print(f"Improved: {gen_resp.get('content', '')[:200]}") ``` ```python theme={"dark"} import matplotlib.pyplot as plt import numpy as np data = np.array([[matrix[mk].get(pn, 0) or 0 for pn in persona_map] for mk in MESSAGES]) fig, ax = plt.subplots(figsize=(10, 5)) ax.imshow(data, cmap="RdYlGn", aspect="auto", vmin=-100, vmax=100) ax.set_xticks(range(len(persona_map))); ax.set_xticklabels(persona_map.keys(), rotation=45, ha="right") ax.set_yticks(range(len(MESSAGES))); ax.set_yticklabels([m["label"] for m in MESSAGES.values()]) plt.colorbar(ax.images[0], label="NPS"); plt.tight_layout(); plt.savefig("heatmap.png", dpi=150) ``` *** ## Credits Estimate | Operation | Typical Cost | Notes | | ---------------------------------------------- | ------------- | --------------------------- | | Persona creation (×5) | 0–25 | One-time; reuse across runs | | Focus Groups (×25, 5 respondents, 5 questions) | 375–625 | \~15–25 per group | | **Total** | **\~375–650** | | Start with `sample_size: 5` for directional signal. Increase to 15–25 only for top-performing cells where you need higher confidence. *** ## What's Next Deep-dive a single message with 10 buying-committee personas Pit opposing personas against each other for pricing insights Test across age demographics instead of role-based segments Generate production-ready copy from matrix winners Choose the right persona types for your research goal Pre-flight checks and usage tracking