Introduction: quick answers to your market research questions
The new AI tools that are revolutionizing market research
The market research tool landscape has evolved dramatically this year. Here are three platforms setting new standards:
- OpenAI GPT-6 Astra
- Perplexity Lily
Using these tools together provides a complete market view, combining external data (like weather) with internal analytics.
How to build a market research workflow with the latest models
Below is a practical step-by-step workflow you can copy and modify for any market niche. The examples use the public APIs of these models (exact call details vary slightly, but the principle remains the same).
Start by gathering raw data: trend feeds, weather data, and any internal datasets you own.
import requests
# Example: fetching weather forecasts from WeatherNext 3
weather_url = "https://weathernext3.googleapis.com/v1/forecast?location=IT&hours=24"
weather_resp = requests.get(weather_url, headers={"Authorization": "Bearer YOUR_KEY"})
weather_data = weather_resp.json()
# Example: sending a request to GPT-6 Astra for data analysis
summary_prompt = """
Analyze the following Italian sales data for 2026 Q3:
{json.dumps(sales_data, indent=2)}
Provide:
1. Main trends by product segment
2. Month-over-month percentage changes
3. Three actionable insights
"""
astra_resp = requests.post(
"https://api.openai.com/v1/chat/completions",
headers={"Authorization": "Bearer OPENAI_KEY"},
json={
"model": "gpt-6-astra",
"messages": [{"role": "user", "content": summary_prompt}],
"temperature": 0.2
}
)
analysis = astra_resp.json()["choices"][0]["message"]["content"]A good market research prompt should explicitly specify role, context, constraints, and desired output format.
Basic prompt structure
You are an expert market analyst specializing in [NICHE].
Input data: [INSERT DATA OR LINK].
Tasks:
1. Identify main trends for [ASPECT 1].
2. Highlight anomalies for [ASPECT 2].
3. Provide three strategic recommendations.
Output format: JSON with keys "trends", "anomalies", "recommendations".
Limits: Max 250 words per section, use real numbers, exclude confidential data.
Result:Once you have the text, parse it into JSON for further processing.
import json, re
# Extract JSON block from output (assuming it's in a markdown code block)
json_str = re.search(r'\{(?:.|\n)*?\}', analysis).group()
parsed = json.loads(json_str)
# You can now write parsed['trends'] to a spreadsheet, upload to a dashboard, etc.Prompt engineering tricks for effective market research
- Define your role.Start with a clear statement of your role (e.g., "I am a market analyst for consumer electronics").
- Include reference data or links.Provide real datasets or URLs to reduce hallucinations.
- Set clear constraints.Limit length, format, and level of detail.
- Request structured output.JSON or spreadsheets are easier to integrate with business intelligence tools.
- Add temporal context.Specify the quarter, year, or exact time window for which you need the data.
Case study: real-world results in 2026
Companies likeMediaMarktandUnilever
Conclusion: put AI to work for your market research
Actionable takeaways
- Experiment with GPT-6 Astra using a structured prompt to generate market analysis in JSON format.
- Implement Lily on your servers for offline processing of proprietary data, ensuring privacy and reducing costs.
- Monitor execution times and data quality; adjust temperature and prompt depth for more accurate results.
- Document every successful prompt in a shared repository to build a reusable market research prompt library.
Conclusion:use these steps as an operational foundation, adapting tools, policies, and controls to your organization’s real-world context.