AI-driven hypothesis testing built on real consumer data
METHODOLOGY
Our methodology integrates official government datasets with large language model technology to create a new approach to audience analysis. By combining actual population data from Lithuania's Department of Statistics with AI simulation capabilities, we address the core challenge of market research: accessing reliable insights without the traditional time and cost constraints. The foundation rests on three official datasets covering spending behaviors, digital patterns, and demographic characteristics. We apply statistical modeling to identify behavioral segments within this real population data, then use AI to simulate responses and generate insights about these segments. This approach maintains the statistical validity of government data while adding the analytical depth and speed that AI provides for hypothesis testing and concept exploration.
Open Data Foundation
Our tool is built on three official datasets from Lithuania's Department of Statistics, each providing unique insights into different aspects of consumer behavior:
01.
The foundation rests on three official datasets covering spending behaviors, digital patterns, and demographic characteristics. We apply statistical modeling to identify behavioral segments within this real population data, then use AI to simulate responses and generate insights about these segments.
02.
This approach maintains the statistical validity of government data while adding the analytical depth and speed that AI provides for hypothesis testing and concept exploration.
03.
Our methodology integrates official government datasets with large language model technology to create a new approach to audience analysis. By combining actual population data from Lithuania's Department of Statistics with AI simulation capabilities, we address the core challenge of market research: accessing reliable insights without the traditional time and cost constraints.
01.
The foundation rests on three official datasets covering spending behaviors, digital patterns, and demographic characteristics. We apply statistical modeling to identify behavioral segments within this real population data, then use AI to simulate responses and generate insights about these segments.
02.
This approach maintains the statistical validity of government data while adding the analytical depth and speed that AI provides for hypothesis testing and concept exploration.
03.
Our methodology integrates official government datasets with large language model technology to create a new approach to audience analysis. By combining actual population data from Lithuania's Department of Statistics with AI simulation capabilities, we address the core challenge of market research: accessing reliable insights without the traditional time and cost constraints.
We developed a sophisticated demographic matching system that leverages the strengths of each dataset while maintaining statistical integrity. Our approach identifies common demographic variables (age, income, region, education) across all three datasets and creates parallel insights for identical demographic groups. When examining any specific demographic cohort, we extract their unique characteristics from each dataset: spending behaviors from the budget survey, digital engagement patterns from the technology survey, and socioeconomic context from the living conditions survey. This methodology allows us to build multi-dimensional segment profiles that combine behavioral, digital, and socioeconomic insights into a single, actionable customer understanding.
Beyond basic demographic breakdowns, our research team has developed proprietary metrics that reveal deeper behavioral insights. We've engineered calculations that uncover spending priorities, financial behavior patterns, consumption preferences, and digital engagement levels that aren't immediately visible in raw survey data. These metrics transform population statistical observations into actionable segment characteristics that matter for business decisions.
01.
SEGMENT
Our segments aren't just demographic boxes—they represent distinct lifestyle patterns revealed through behavioral analysis. We systematically generated thousands of demographic combinations, ranging from simple two-variable groups to more complex four-variable intersections, creating a detailed map of the Lithuanian population. Using statistical modeling, we identified which demographic characteristics most strongly predict behavioral deviations across spending and digital engagement categories. Each segment is evaluated based on its distinctiveness—how significantly it differs from average population behavior patterns. Only segments showing meaningful statistical deviation and sufficient sample size make it into our final database.Our scoring algorithm ensures that every recommended segment represents a genuine behavioral insight, not just a demographic coincidence.
02.
SEGMENT
Our segment selection process prioritizes segments that demonstrate the strongest behavioral distinctiveness from the general population. We apply statistical thresholds—segments must represent sufficient sample sizes in our source datasets and show meaningful differences in spending patterns or digital behaviors. For category-specific analysis, our algorithm identifies the optimal combination of segments that best represent different approaches to each spending category, ensuring coverage from highly targeted micro-segments to broader market approaches. This multi-tiered selection process guarantees that every segment we present offers genuine strategic value backed by statistical significance.
03.
SEGMENT
Rather than presenting abstract percentages, we project each segment's size to Lithuania's total population, giving you actual numbers of people in each group. This projection allows for more realistic market sizing and business planning based on concrete audience volumes. We've developed five segment size categories to help interpret market reach: Micro segments (under 40,000 people)Niche segments (40,000-70,000)Compact segments (70,001-110,000)Established segments (110,001-180,000)Mass segments (over 180,000) These classifications help to understand the scale of the potential audience and plan marketing strategies accordingly.
AI Simulation
We've extensively tested our AI simulation capabilities against real-world research projects conducted by our team. In controlled comparisons, our AI-generated insights demonstrated strong alignment with traditional survey results across key metrics. When we've run parallel studies—both traditional research and our AI simulation—on identical business questions, the strategic recommendations converged remarkably. Our confidence in these results stems from years of hands-on consumer research experience. As seasoned researchers, we know what good data looks like, what insights make sense, and what red flags indicate unreliable results. This expertise allows us to continuously calibrate and validate our AI outputs against professional research standards.
Every simulation scenario has been designed, tested, and refined by our team of brand strategists, data scientists, and research specialists. We don't rely on AI black boxes—instead, we've built guardrails and validation systems based on decades of combined research experience. Our approach combines the speed and scale of AI processing with the nuanced understanding that comes from real-world research practice. When the AI generates insights about consumer behavior, our experts evaluate whether those insights align with established consumer psychology principles and market realities. The result is a tool that delivers the reliability of traditional research with the accessibility and speed that modern businesses demand. Each output is effectively pre-validated by research professionals who understand both the data sources and the business applications.
LITERATURE / USE CASES
The integration of AI into market research isn't just another tech trend—it's backed by rigorous academic research from leading universities worldwide. Here's what the science tells us about when and how LLM-powered insights can enhance your research process.
LITERATURE / USE CASES
Hypothesis Generation & Concept Testing
Recent groundbreaking research from elite universities demonstrates that LLMs can generate research hypotheses that mirror expert-level insights in terms of novelty, clearly surpassing LLM-only approaches when combined with specialized knowledge frameworks (Hou & Ji, 2024). For market research, this translates to AI's ability to help you rapidly explore "what if" scenarios before committing to expensive primary research.
What this means for you:
Instead of spending weeks brainstorming potential audience segments or customer motivations, our tool can generate testable hypotheses about consumer behavior patterns in minutes. Think of it as your research team's creative partner for the ideation phase.
Pattern Discovery in Consumer Data
University of Texas Dallas researchers developed novel statistical frameworks that efficiently integrate LLM-generated insights with real consumer data, achieving error reduction of 24.9% to 79.8% compared to traditional approaches (Wang et al., 2024). This breakthrough demonstrates AI's power in uncovering consumer patterns.
Real-world impact:
Our segmentation algorithm identifies subtle behavioral patterns in your target market that traditional analysis might take months to uncover, giving you a head start on understanding your customers.
"Silicon Samples" for Exploratory Research
Leading consumer research experts from Columbia Business School advocate for "silicon samples"—AI-generated respondent profiles—specifically for upstream research activities like pretesting and pilot studies rather than main studies (Sarstedt et al., 2024). The research shows that when properly conditioned with demographic backstories, LLMs can serve as effective proxies for specific population groups, demonstrating "algorithmic fidelity" that reflects real human attitudes and beliefs (Argyle et al., 2023).
Your advantage:
Before launching expensive research, test your questions, concepts, and messaging with AI-simulated audiences that represent your target demographics. Validate your approach efficiently, then scale your insights.
When AI Insights Prove Most Valuable
01.
Academic validation shows AI excels at identifying audience segments based on spending patterns and digital behaviors—exactly what our tool specializes in. Research consistently demonstrates that LLMs perform well on tasks involving generalization and comparison of information provided directly in instructions, making them ideal for demographic profiling and behavioral clustering (Park et al., 2023).
02.
Consumer research experts recommend enforcing variability in AI outputs to generate a range of plausible results rather than striving for precise estimates, making AI particularly valuable for hypothesis generation and exploratory research (Sarstedt et al., 2024). This aligns perfectly with our tool's design philosophy.
03.
Virginia researchers studying qualitative analysis found that LLMs provided consistent and reliable results that could validate traditional coding approaches, often identifying nuances that human coders initially missed (Tai et al., 2024). This demonstrates AI's ability to augment analytical capabilities.
01.
Academic validation shows AI excels at identifying audience segments based on spending patterns and digital behaviors—exactly what our tool specializes in. Research consistently demonstrates that LLMs perform well on tasks involving generalization and comparison of information provided directly in instructions, making them ideal for demographic profiling and behavioral clustering (Park et al., 2023).
02.
Consumer research experts recommend enforcing variability in AI outputs to generate a range of plausible results rather than striving for precise estimates, making AI particularly valuable for hypothesis generation and exploratory research (Sarstedt et al., 2024). This aligns perfectly with our tool's design philosophy.
03.
Virginia researchers studying qualitative analysis found that LLMs provided consistent and reliable results that could validate traditional coding approaches, often identifying nuances that human coders initially missed (Tai et al., 2024). This demonstrates AI's ability to augment analytical capabilities.
The academic evidence consistently points to one conclusion: AI-powered market research tools deliver significant value when designed around proven methodologies. Our tool leverages these research-backed approaches, giving you the speed and insight of cutting-edge AI technology with the reliability that academic validation provides.
01.
Use our AI-powered segmentation to rapidly explore market opportunities, test initial hypotheses, and refine your research questions. Then scale your validated insights with confidence.
02.
Begin your market research with data-driven hypotheses about your audience's behaviors, preferences, and characteristics.
03.
Test multiple approaches quickly and efficiently before committing to large-scale research investments.
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Bryan, J. G., Niu, H., & Li, D. (2025). Incorporating LLM-derived information into hypothesis testing for genomics applications. bioRxiv preprint.
Chen, Z., & Zou, J. (2023). How is ChatGPT's behavior changing over time? arXiv preprint arXiv:2307.09009.
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Hou, Y., & Ji, H. (2024). Automating psychological hypothesis generation with AI: When large language models meet causal graph. Humanities and Social Sciences Communications, 11, 1-15.
Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, 1-22.
Sarstedt, M., Wilczynski, P., & Melewar, T. C. (2024). Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines. Psychology & Marketing, 41(6), 1254-1270.
Tai, R. H., Bentley, L. R., Xia, X., Sitt, J. M., Fankhauser, S. C., Chicas-Mosier, A. M., & Monteith, B. G. (2024). An examination of the use of large language models to aid analysis of textual data. International Journal of Qualitative Methods, 23, 16094069241231168.
Wang, M., Zhang, H., Li, D., Chen, Y., Liang, P., & Singh, P. V. (2024). Large language models for market research: A data-augmentation approach. arXiv preprint arXiv:2412.19363.