Focus Groups

Focus Groups let you run simulated audience research at scale. Define your questions, choose your personas, set a sample size, and get quantitative scores plus qualitative reasoning from a synthetic panel — in minutes instead of weeks. A single Focus Group can simulate N=25 to 200+ respondents across multiple audience segments, combining question types like NPS, Likert scales, open-ended responses, and ranking exercises into one cohesive study.

Focus Groups are asynchronous. You create a study, then poll for results. Most studies complete in 30–120 seconds depending on sample size and question count.

Key Features

12 Question Types

NPS, Likert, multiple choice, open-ended, rating, ranking, semantic differential, and more

Persona-Based Panels

Mix multiple personas for diverse, segment-specific responses

Scalable Sample Sizes

From 10 to 200+ respondents per study

Aggregate + Individual

Get both statistical summaries and individual respondent reasoning

Focus Group Lifecycle

Creating a Focus Group

Choose your personas

Select 1–10 persona IDs that represent your target audience segments. The more diverse your panel, the richer the cross-segment analysis.

Define your questions

Write 1–20 questions using the supported question types. Mix quantitative (NPS, Likert) with qualitative (open-ended) for well-rounded insights.

Set sample size and submit

Choose a sample size (10–200+). Larger samples give more statistical confidence but cost more credits.

Poll for results

The study runs asynchronously. Poll the GET endpoint until status is "COMPLETED".

Full Creation Example

import requests

response = requests.post(
    "https://app.mavera.io/api/v1/focus-groups",
    headers={"Authorization": "Bearer mvra_live_your_key_here"},
    json={
        "name": "Product Launch Feedback",
        "sample_size": 50,
        "persona_ids": [
            "gen_z_consumer",
            "millennial_professional",
            "gen_x_parent"
        ],
        "questions": [
            {
                "question": "How likely are you to recommend this product to a friend?",
                "type": "NPS",
                "order": 1
            },
            {
                "question": "The product design feels modern and appealing.",
                "type": "LIKERT",
                "order": 2
            },
            {
                "question": "Which features interest you most?",
                "type": "MULTIPLE_CHOICE",
                "options": ["Speed", "Design", "Price", "Sustainability", "Brand reputation"],
                "order": 3
            },
            {
                "question": "Rate the overall value for money.",
                "type": "RATING",
                "order": 4
            },
            {
                "question": "Rank these factors by importance when choosing this product.",
                "type": "RANKING",
                "options": ["Price", "Quality", "Brand", "Reviews", "Sustainability"],
                "order": 5
            },
            {
                "question": "Would you switch from your current product to this one?",
                "type": "YES_NO",
                "order": 6
            },
            {
                "question": "How innovative vs traditional does this product feel?",
                "type": "SEMANTIC_DIFFERENTIAL",
                "left_label": "Traditional",
                "right_label": "Innovative",
                "order": 7
            },
            {
                "question": "What would make you more likely to purchase?",
                "type": "OPEN_ENDED",
                "order": 8
            }
        ],
        "workspace_id": "your_workspace_id"
    }
)

focus_group = response.json()
print(f"Focus Group ID: {focus_group['id']}")
print(f"Status: {focus_group['status']}")

Question Types

Type	Description	Response Format	Best For
`NPS`	Net Promoter Score (0–10)	Numeric score + reasoning	Measuring recommendation likelihood
`LIKERT`	5-point agreement scale	Scale value (1–5) + explanation	Gauging attitudes and opinions
`MULTIPLE_CHOICE`	Select from defined options	Selected option(s) + reasoning	Feature preference, forced choices
`OPEN_ENDED`	Free-form response	Detailed text	Exploratory insights, “why” questions
`RATING`	Star rating (1–5)	Rating + explanation	Quick satisfaction scoring
`YES_NO`	Binary choice	Yes/No + reasoning	Go/no-go decisions, adoption intent
`RANKING`	Order options by preference	Ranked list + reasoning	Priority ordering, trade-off analysis
`SLIDER`	Numeric scale (custom range)	Value + explanation	Fine-grained measurement
`MATRIX`	Multiple items on same scale	Matrix of responses	Comparing multiple attributes
`SEMANTIC_DIFFERENTIAL`	Between two opposites	Position (1–7) + explanation	Brand perception, positioning
`CONJOINT`	Trade-off analysis	Preference data	Pricing and feature optimization
`MAXDIFF`	Best/worst selection	Selection + reasoning	Finding most/least important attributes

Recommended mix: Start with 1–2 quantitative questions (NPS, Likert, Rating) to get scoreable data, then add 1–2 qualitative questions (Open-ended) for depth. Finish with a Ranking or Semantic Differential for nuanced insight. Keep total questions under 10 for best quality.

Polling for Results

Focus Groups run asynchronously. Poll the status endpoint until the study completes.

import time
import requests

focus_group_id = "fg_abc123"
headers = {"Authorization": "Bearer mvra_live_your_key_here"}

while True:
    response = requests.get(
        f"https://app.mavera.io/api/v1/focus-groups/{focus_group_id}",
        headers=headers
    )
    result = response.json()

    if result["status"] == "COMPLETED":
        print("Focus group complete!")
        break
    elif result["status"] == "FAILED":
        print(f"Focus group failed: {result.get('error')}")
        break
    else:
        print(f"Status: {result['status']}... waiting")
        time.sleep(5)

Status values:

Status	Meaning
`PENDING`	Study created, not yet started
`PROCESSING`	Responses being generated
`COMPLETED`	All results available
`FAILED`	An error occurred

Interpreting Results

Aggregate Scores

Each question returns an aggregate summary alongside individual responses.

result = response.json()

for question_result in result["results"]:
    print(f"\nQuestion: {question_result['question']}")
    print(f"Type: {question_result['type']}")
    print(f"Summary: {question_result['summary']}")

    if question_result["type"] == "NPS":
        print(f"  NPS Score: {question_result['nps_score']}")
        print(f"  Promoters: {question_result['promoters']}%")
        print(f"  Passives: {question_result['passives']}%")
        print(f"  Detractors: {question_result['detractors']}%")

    elif question_result["type"] == "LIKERT":
        print(f"  Mean: {question_result['mean']}")
        print(f"  Median: {question_result['median']}")
        print(f"  Std Dev: {question_result['std_dev']}")

    elif question_result["type"] == "MULTIPLE_CHOICE":
        for option, count in question_result["distribution"].items():
            print(f"  {option}: {count} votes")

    elif question_result["type"] == "RANKING":
        for rank, item in enumerate(question_result["average_ranking"], 1):
            print(f"  #{rank}: {item}")

Individual Responses

Drill into individual respondent data for qualitative depth.

for question_result in result["results"]:
    print(f"\nQuestion: {question_result['question']}")
    for resp in question_result["responses"][:5]:
        print(f"\n  Persona: {resp['persona_name']}")
        if "score" in resp:
            print(f"  Score: {resp['score']}")
        print(f"  Reasoning: {resp['reasoning'][:200]}...")

Cross-Segment Analysis

Compare how different persona segments responded to the same question.

nps_results = result["results"][0]

segment_scores = {}
for resp in nps_results["responses"]:
    segment = resp["persona_name"]
    if segment not in segment_scores:
        segment_scores[segment] = []
    segment_scores[segment].append(resp["score"])

for segment, scores in segment_scores.items():
    avg = sum(scores) / len(scores)
    print(f"{segment}: avg NPS = {avg:.1f}")

Response Format

{
  "id": "fg_abc123",
  "name": "Product Launch Feedback",
  "status": "COMPLETED",
  "sample_size": 50,
  "personas": [
    { "id": "gen_z_consumer", "name": "Gen Z Consumer" },
    { "id": "millennial_professional", "name": "Millennial Professional" },
    { "id": "gen_x_parent", "name": "Gen X Parent" }
  ],
  "questions": [
    { "id": "q1", "question": "How likely are you to recommend this product?", "type": "NPS", "order": 1 }
  ],
  "results": [
    {
      "question_id": "q1",
      "question": "How likely are you to recommend this product?",
      "type": "NPS",
      "nps_score": 42,
      "promoters": 55,
      "passives": 32,
      "detractors": 13,
      "summary": "Strong positive sentiment overall. Gen Z respondents showed the highest likelihood to recommend, driven by the product's sustainability messaging...",
      "responses": [
        {
          "persona_id": "gen_z_consumer",
          "persona_name": "Gen Z Consumer",
          "score": 9,
          "reasoning": "I'd definitely recommend this — the sustainability angle is genuine, not performative, and the design feels like it was made for my generation."
        },
        {
          "persona_id": "gen_x_parent",
          "persona_name": "Gen X Parent",
          "score": 7,
          "reasoning": "Good product, fair price. I'd mention it if someone asked but probably wouldn't go out of my way to recommend it."
        }
      ]
    }
  ],
  "usage": {
    "credits_used": 150
  }
}

When to Use Focus Groups vs Chat vs Mave

Scenario	Best Tool	Why
”How would Gen Z react to this tagline?”	Chat	Single persona, single question — fast and cheap
”Compare reactions across 4 segments with NPS scores”	Focus Groups	Multiple personas, quantitative data, statistical aggregation
”What’s the market size for EV charging in Europe?”	Mave	Research question needing web data and citations
”Test 3 ad concepts with 50 respondents each”	Focus Groups	Structured comparison at scale
”Get a quick gut-check from a millennial”	Chat	Conversational, low-cost, real-time
”Deep competitive analysis with sources”	Mave	Multi-source research with validation

Credits and Pricing

Credit cost depends on sample size, question count, and question complexity.

Sample Size	3 Questions	5 Questions	10 Questions
10–25	30–50 credits	50–75 credits	75–120 credits
26–50	50–85 credits	75–125 credits	125–200 credits
51–100	85–140 credits	125–200 credits	200–350 credits
100+	140–250 credits	200–350 credits	350–500+ credits

Open-ended and conjoint questions cost more per respondent than NPS or yes/no questions because they require more generation.

Listing Focus Groups

Retrieve all focus groups in your workspace.

response = requests.get(
    "https://app.mavera.io/api/v1/focus-groups",
    headers={"Authorization": "Bearer mvra_live_your_key_here"}
)

for fg in response.json()["data"]:
    print(f"{fg['name']} — {fg['status']} — {fg['sample_size']} respondents")

Best Practices

Mix quantitative and qualitative questions

Combine NPS or Likert questions (numbers you can chart) with open-ended questions (explanations you can quote). This gives you both statistical confidence and narrative depth.

Choose diverse but relevant personas

Include personas that represent your actual target audience segments. A panel of Gen Z + Millennial + Gen X gives generational breadth. Adding a Budget Shopper and a Luxury Consumer gives psychographic breadth.

Keep studies focused

5–8 well-crafted questions yield better results than 15+ rushed ones. Each additional question adds cost and can cause response fatigue in the model.

Use for pre-launch validation

Focus Groups are ideal for validating messaging, product concepts, pricing strategies, and feature prioritization before committing real-world budget.

Run comparative studies

Create two focus groups with the same personas but different stimuli (e.g., two taglines, two product descriptions) to see which resonates more.

Cross-reference with Chat

After a Focus Group reveals a surprising result, follow up with a Chat conversation using the same persona to explore the “why” in depth.

Focus Group results are simulated perspectives, not real consumer data. They are best used for directional insights, concept screening, and hypothesis generation. Validate high-stakes decisions with real audience research.

Next Steps

Run First Focus Group

Step-by-step tutorial with complete scripts

Quickstart: Focus Groups

Get started in 15 minutes

Personas

Choose and create personas for your panels

API Reference

Full API specification

Getting Started

Core Concepts

Features

SDKs & Libraries

Key Features

12 Question Types

Persona-Based Panels

Scalable Sample Sizes

Aggregate + Individual

Focus Group Lifecycle

Creating a Focus Group

Full Creation Example

Question Types

Polling for Results

Interpreting Results

Aggregate Scores

Individual Responses

Cross-Segment Analysis

Response Format

When to Use Focus Groups vs Chat vs Mave

Credits and Pricing

Listing Focus Groups

Best Practices

Next Steps

Run First Focus Group

Quickstart: Focus Groups

Personas

API Reference

Getting Started

Core Concepts

Features

SDKs & Libraries

​Key Features

12 Question Types

Persona-Based Panels

Scalable Sample Sizes

Aggregate + Individual

​Focus Group Lifecycle

​Creating a Focus Group

​Full Creation Example

​Question Types

​Polling for Results

​Interpreting Results

​Aggregate Scores

​Individual Responses

​Cross-Segment Analysis

​Response Format

​When to Use Focus Groups vs Chat vs Mave

​Credits and Pricing

​Listing Focus Groups

​Best Practices

​Next Steps

Run First Focus Group

Quickstart: Focus Groups

Personas

API Reference

Key Features

Focus Group Lifecycle

Creating a Focus Group

Full Creation Example

Question Types

Polling for Results

Interpreting Results

Aggregate Scores

Individual Responses

Cross-Segment Analysis

Response Format

When to Use Focus Groups vs Chat vs Mave

Credits and Pricing

Listing Focus Groups

Best Practices

Next Steps