Introduction: Why AI has become indispensable for modern market research
1. Current trends redefining market research
Two key developments are driving this transformation:
- Streaming TTS models.Cartesia has launched Sonic-3.6, a state space-based text-to-speech model that now dominates both Artificial Analysis Speech scenarios. This enables brands to instantly analyze voice transcripts, even in multilingual contexts.
- Wearable bi-signal analysis.Samsung Research America’s foundation models are learning from wearable device data, offering an unprecedented view into consumers’ emotional and physiological states.
- Agentic AI for operations.Alvys has introduced an agent platform for TMS (Transportation Management System) workflows, demonstrating how agentic automation can optimize not only logistics but also research processes.
These trends converge toward a single goal: making market research faster, deeper, and more actionable.
1.1 How new TTS models are changing data collection
With Sonic-3.6, researchers can generate and analyze thousands of voice responses in seconds. This is particularly useful for:
- Analyzing sentiments expressed in voice feedback.
- Identifying pronunciation patterns that may indicate language barriers.
- Creating interactive avatars for real-time interviews.
1.2 Using wearable data in market research
Samsung Health AI models can extract signals such as heart rate variability, stress levels, and physical activity. By integrating this data with traditional demographic information, brands can:
- Segment the market based on physiological state, not just behavior.
- Assess a product’s real impact on consumer well-being.
- Predict purchase moments with unprecedented accuracy.
2. Building a market research workflow powered by agentic AI
A practical, tested workflow in 2026 combines three phases:
2.1 Collection: Multichannel data extraction
Use APIs that integrate data from:
- Survey platforms (Typeform, SurveyMonkey)
- Voice analysis via Cartesia’s TTS
- Wearable data via Samsung Health SDK
import requests
# Example of unified data collection
urls = {
"survey": "https://api.survey.com/responses",
"voice": "https://api.cartesia.io/transcripts",
"wearable": "https://api.samsunghealth.io/biometrics"
}
for source, url in urls.items():
response = requests.get(url, headers={"Authorization": "Bearer YOUR_KEY"})
data[source] = response.json()2.2 Processing: Generating insights using LLMs
Send raw data to an LLM with a structured prompt designed to extract actionable insights.
prompt = """
Analyze the following market research data and provide:
1. Three key insights (sentiment, demographic trends, product opportunities)
2. A list of concrete actions for each insight
Data:
{dataset}
"""
response = llm.generate(prompt)2.3 Action: Activating insight-based campaigns
Use an agentic workflow to:
- Automatically create audience segments for marketing campaigns
- Generate creative content based on voice transcripts
- Send push notifications to wearable devices when high stress levels are detected (e.g., to offer a relaxation experience)
3. Practical example: Launching a new dietary supplement
A health and wellness brand used the following workflow to quickly validate a new multivitamin supplement:
- Collection:5,000 responses to a short survey, 200 voice interviews processed with Sonic-3.6, and wearable activity data from 1,000 participants.
- Processing:An LLM identified that users with high levels of physical stress (measured by wearables) showed 78% positive sentiment toward natural supplements.
- Action:The agent triggered a programmatic campaign that displayed video creatives generated from voice transcripts to eligible users, achieving a 4.3% conversion rate (compared to an industry average of 2.1%).
Result: Early launch completed with a 340% ROI in just 6 weeks.
4. Checklist for your first steps
- Integrate multichannel data APIs.Start with surveys and voice; add wearables later.
- Write a prompt for insight extraction.Use the prompt model above as a starting point.
- Implement a basic agent.Tools like Alvys or custom low-code nodes can automate action.
- Monitor and iterate.Set up a dashboard for sentiment and engagement metrics; adjust the prompt every two weeks.
Conclusion: The future of market research is already agentic
Start today with a small experiment: connect a survey to an LLM and see what your data reveals. The future of market research is here, and it’s driven by agents.