Focus Groups

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_id",
            "millennial_professional_id",
            "gen_x_parent_id"
        ],
        "questions": [
            {
                "question": "How likely are you to recommend this product?",
                "type": "NPS",
                "order": 1
            },
            {
                "question": "Rate the product design",
                "type": "LIKERT",
                "order": 2
            },
            {
                "question": "Which features interest you most?",
                "type": "MULTIPLE_CHOICE",
                "options": ["Feature A", "Feature B", "Feature C", "Feature D"],
                "order": 3
            },
            {
                "question": "What would make you more likely to purchase?",
                "type": "OPEN_ENDED",
                "order": 4
            }
        ],
        "workspace_id": "your_workspace_id"
    }
)

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

# Get focus group with results
response = requests.get(
    f"https://app.mavera.io/api/v1/focus-groups/{focus_group_id}",
    headers={"Authorization": "Bearer mvra_live_your_key_here"}
)

result = response.json()

# Access aggregated results
for question_result in result["results"]:
    print(f"Question: {question_result['question']}")
    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"Detractors: {question_result['detractors']}%")

{
  "id": "fg_abc123",
  "name": "Product Launch Feedback",
  "status": "COMPLETED",
  "sample_size": 50,
  "personas": [...],
  "questions": [...],
  "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 with Gen Z showing highest likelihood to recommend...",
      "responses": [
        {
          "persona_id": "gen_z_consumer_id",
          "persona_name": "Gen Z Consumer",
          "score": 9,
          "reasoning": "The product aligns with my values..."
        }
      ]
    }
  ],
  "usage": {
    "credits_used": 150
  }
}