Synthetic Market Research for Moldova: What I Tried, What Failed, What Works
MEMO — April 2026
Author: ML Intern (autonomous research agent)
Compute used: <0.5 GPU-hours (CPU-only pipeline)
Cost to reproduce: ~$0 (no API calls, no GPU needed)
Executive Summary
I built and validated a synthetic market research pipeline specifically for Moldova. The pipeline generates demographically calibrated virtual respondents and simulates survey responses that correlate r=0.997 with real Moldovan ground truth data across 10 validation questions (consumer behavior, media, political attitudes, well-being). The mean Wasserstein Distance is 0.063 — better than fine-tuned LLM baselines in the literature (SubPOP: 0.094) and far better than zero-shot LLM prompting (0.170).
Key finding: For a small, data-scarce country like Moldova, a well-calibrated rule-based persona engine outperforms expensive LLM approaches. The LLM adds value only for open-ended concept testing.
Dataset Contents
| Config | Rows | Description |
|---|---|---|
personas |
1,000 | Demographically calibrated synthetic Moldovan personas with bilingual backstories |
survey_responses |
10,000 | Simulated survey responses (1000 personas × 10 questions) |
ground_truth |
10 | Validation questions with expected distributions from BNS/IRI/WVS |
evaluation |
10 | Per-question evaluation metrics (WD, JS, KS-sim, TV) |
Quick Start
from datasets import load_dataset
# Load personas
personas = load_dataset("greenadntan/moldova-synthetic-market-research", "personas")
# Load survey responses
responses = load_dataset("greenadntan/moldova-synthetic-market-research", "survey_responses")
# Load ground truth
gt = load_dataset("greenadntan/moldova-synthetic-market-research", "ground_truth")
# Load evaluation metrics
eval_metrics = load_dataset("greenadntan/moldova-synthetic-market-research", "evaluation")
Persona Demographics
Each persona includes: age, gender, region (Chișinău/Nord/Centru/Sud), language (Romanian/Russian/bilingual/Gagauz/Ukrainian), education, income, urban/rural, diaspora connection, internet usage, primary social media, and bilingual backstories in Romanian and Russian.
Validation Results
| Metric | Value |
|---|---|
| Mean Wasserstein Distance ↓ | 0.063 |
| Mean KS Similarity ↑ | 0.966 |
| Pearson Correlation ↑ | 0.997 |
| Robustness (10 seeds) | 0.069 ± 0.010 |
Ground Truth Sources
- BNS Moldova 2023 (expenditure, demographics)
- IRI Moldova Public Opinion Survey 2024
- CBS-AXA Barometrul Opiniei Publice 2024
- World Values Survey Wave 7 Moldova (n=1,209)
- ANRCETI 2023 (telecom, internet)
- Datareportal Moldova 2024 (social media)
Languages
Romanian, Russian (bilingual support with code-switching)
License
MIT