Persona Selection by Use Case

Choose the right Mavera persona for your audience, product, and research goal — with a practical matrix and code

When to Use This

You need to pick a persona (or a set of personas) for responses, focus groups, or Mave research. Choosing the right persona drastically improves relevance: a Gen Z Consumer and a B2B Decision Maker will give very different answers to the same question.

This cookbook gives you:

• A use-case → persona matrix for common scenarios
• Code to list, filter, and select personas programmatically
• Guidance on single vs multi-persona strategies
• When to use pre-built vs custom personas

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Persona Categories (Quick Reference)

Category	Best For	Example Personas
Generational	Consumer insights, brand messaging, product positioning by age	Gen Z Consumer, Millennial Professional, Gen X Parent, Baby Boomer
Professional	B2B research, enterprise software, sales enablement	B2B Decision Maker, Startup Founder, Enterprise CTO
Lifestyle	Consumer behavior, purchase drivers, sustainability, budget	Health-Conscious Consumer, Eco-Warrior, Budget Shopper
Industry	Vertical-specific research, domain expertise	Healthcare Professional, Finance Expert, Tech Enthusiast
Expert	Strategic analysis, competitive intelligence, market research	Market Analyst, Brand Strategist, UX Researcher

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Use-Case → Persona Matrix

Use this table to pick personas for your scenario. “Primary” = best first choice; “Alternates” = good for comparison or multi-persona runs.

Use Case	Primary Persona(s)	Alternates	Why
Consumer product launch	Gen Z Consumer, Millennial Professional	Gen X Parent	Generational personas reflect different buying priorities and channels
B2B SaaS positioning	B2B Decision Maker, Enterprise CTO	Startup Founder	B2B personas focus on ROI, security, and integration
Sustainability / ESG messaging	Eco-Warrior, Gen Z Consumer	Millennial Professional	These segments care most about environmental claims
Healthcare / pharma	Healthcare Professional	Finance Expert, B2B Decision Maker	Industry context for compliance and clinical language
Finance / fintech	Finance Expert, Budget Shopper	B2B Decision Maker	Mix of institutional and retail perspectives
Ad creative testing	Gen Z Consumer, Millennial Professional	Gen X Parent	Generational differences in creative preferences
Brand authenticity	Gen Z Consumer, Eco-Warrior	Millennial Professional	These audiences value authenticity highly
Feature prioritization	UX Researcher, Tech Enthusiast	B2B Decision Maker	Expert + user perspectives
Competitive analysis	Market Analyst, Brand Strategist	B2B Decision Maker	Strategic, market-aware viewpoints
Focus group (diverse)	Gen Z + Millennial + Gen X + B2B	Mix generational + professional	Coverage across segments for NPS, Likert, open-ended
Ad copy for young adults	Gen Z Consumer	Millennial Professional	Tone, channels, and language differ
Enterprise software sales	Enterprise CTO, B2B Decision Maker	—	Security, scalability, and procurement focus

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Programmatic Persona Discovery

Fetch all personas and filter by category, name, or custom logic. Use this when building dynamic UIs or automating persona selection.

Python

import requests

API_KEY = "mvra_live_your_key_here"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}
BASE = "https://app.mavera.io/api/v1"

def list_personas(category=None, search=None):
    """List personas, optionally filtered by category or name search."""
    resp = requests.get(f"{BASE}/personas", headers=HEADERS)
    resp.raise_for_status()
    personas = resp.json()["data"]

    if category:
        personas = [p for p in personas if p.get("category") == category]
    if search:
        search_lower = search.lower()
        personas = [p for p in personas if search_lower in p.get("name", "").lower()]

    return personas

# Use-case helpers
def personas_for_consumer_research():
    """Best personas for consumer product/messaging research."""
    return list_personas()  # Then filter by category
    # Or hardcode known IDs if your catalog is stable:
    # categories = ["Generational", "Lifestyle"]
    # return [p for p in list_personas() if p["category"] in categories]

def personas_for_b2b():
    """Best personas for B2B/SaaS research."""
    return [p for p in list_personas() if p.get("category") == "Professional"]

def get_persona_by_name(name):
    """Get a persona by exact or partial name match."""
    all_p = list_personas()
    for p in all_p:
        if name.lower() in p.get("name", "").lower():
            return p
    return None

# Example: pick personas for a focus group
personas = list_personas(category="Generational")
persona_ids = [p["id"] for p in personas[:4]]  # Gen Z, Millennial, Gen X, Boomer
print("Focus group persona IDs:", persona_ids)

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Single Persona: Responses

For a single chat, pick one persona that matches your target audience. Use the matrix above, or filter by category.

Python

from openai import OpenAI

client = OpenAI(api_key="mvra_live_your_key_here", base_url="https://app.mavera.io/api/v1")

# Option A: Hardcode a known persona ID (fastest)
PERSONA_ID = "clx1abc2d0001abcdef123456"  # e.g. Gen Z Consumer

# Option B: Resolve by name at startup
persona = get_persona_by_name("Gen Z Consumer")
PERSONA_ID = persona["id"] if persona else None

response = client.responses.create(
    model="mavera-1",
    input=[{"role": "user", "content": "What makes a brand feel authentic to you?"}],
    extra_body={"persona_id": PERSONA_ID},
)

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Multi-Persona: Focus Groups

For focus groups, use 3–6 diverse personas so you get segment-level variation. Mix generational + professional or lifestyle depending on your product.

Python

# Diverse consumer focus group (generational spread)
generational_ids = [p["id"] for p in list_personas(category="Generational")][:4]

# B2B focus group (different roles)
b2b_ids = [p["id"] for p in list_personas(category="Professional")][:4]

# Hybrid: 2 Gen Z, 2 Millennial, 1 B2B (for a product that spans consumer + SMB)
all_p = list_personas()
gen_z = [p["id"] for p in all_p if "Gen Z" in p.get("name", "")][:2]
millennial = [p["id"] for p in all_p if "Millennial" in p.get("name", "")][:2]
b2b = [p["id"] for p in all_p if p.get("category") == "Professional"][:1]
persona_ids = gen_z + millennial + b2b

# Use in focus group payload
payload = {
    "name": "Product Launch Feedback",
    "sample_size": 30,
    "persona_ids": persona_ids,
    "workspace_id": "ws_xxx",
    "questions": [...],
}

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When to Create a Custom Persona

Use pre-built personas when your target fits a common segment (Gen Z, B2B, etc.). Create a custom persona when:

• Your audience is niche (e.g. “Sustainable Fashion Buyer”, “SMB Operations Manager”).
• You need specific psychographics (goals, pains, buying stage) not in pre-built personas.
• You’re running repeated research and want a reusable, tailored profile.

Custom personas cost 300 credits to create but are then free to use. See Personas for NORTH_STAR, INTERMEDIATE, and ADVANCED pipelines.

Python

# North Star: Minimal input, AI fills in the rest
resp = requests.post(
    f"{BASE}/personas",
    headers=HEADERS,
    json={
        "pipeline_type": "NORTH_STAR",
        "name": "Sustainable Fashion Buyer",
        "description": "Eco-conscious millennial, values transparency and circular economy",
        "workspace_id": "ws_xxx",
    },
)
persona = resp.json()
custom_persona_id = persona["id"]

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Tips and Pitfalls

Cache persona IDs at startup

Listing personas is free but adds latency. Fetch once at app start and cache by name or category. Refresh periodically (e.g. daily) if you create new custom personas.

Match persona to question type

For “How would you describe X?”, use a consumer persona. For “What features matter most for enterprise adoption?”, use B2B or Expert personas.

Avoid overloading focus groups

4–6 personas is usually enough for segment diversity. More personas increase cost and can dilute clear patterns.

Combine persona + system prompt

Use persona for audience perspective; use system prompt for task (e.g. “You are a concise reviewer. Keep answers under 50 words.”).

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See Also

Personas

Pre-built categories, custom creation pipelines, API reference

Quickstart: Chat

First response with a persona

Quickstart: Focus Groups

Multi-persona focus group setup

API Reference

List and filter personas