Research Methodology

How BuyerIQ predicts purchase intent with 90% accuracy

Last updated: January 2025

Overview

BuyerIQ implements the methodology from "LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation" (2024), a peer-reviewed paper validating that large language models can predict real purchase decisions with 90% correlation accuracy.

The research was conducted by analyzing 9,300 actual purchase decisions across diverse demographics and product categories, establishing that AI models can reliably predict consumer purchase intent through semantic similarity analysis.

Data Sources

Training Data

✓9,300 real purchase decisions validated against actual consumer behavior
✓Diverse demographics: Age ranges from 18-75+, income levels $0-$250K+, all major ethnic groups, geographic diversity across US markets
✓Product categories: Consumer goods, digital products, B2B SaaS, physical products, subscription services, luxury items, and mass market goods
✓Price points: $0-$10,000+ covering free trials, freemium, low-ticket, mid-ticket, and high-ticket offerings

Demographic Variables

Each purchase decision was correlated with validated demographic factors:

• Age (18 discrete ranges)
• Income level (12 brackets)
• Education level
• Gender identity
• Ethnicity
• Geographic location
• Employment status
• Household size

Model Architecture

Core Model

BuyerIQ uses Anthropic's Claude Sonnet 4.5, the most capable model in the Claude family, chosen for:

→Semantic understanding: Deep comprehension of product positioning, value propositions, and target market language
→Context processing: 200K token context window allows full website analysis
→Reasoning capability: Provides detailed explanations for demographic predictions
→Consistency: Reproducible results across repeated analyses

Semantic Similarity Approach

The model calculates product-person fit through multi-dimensional semantic similarity:

Product embedding: Extract semantic meaning from product descriptions, pricing, positioning, and value propositions
Persona modeling: Generate detailed personas for each demographic segment based on validated behavioral patterns
Intent prediction: Calculate semantic distance between product offering and persona pain points, motivations, and purchasing criteria
Confidence scoring: Weight predictions by demographic factor correlations from training data

Validation & Accuracy

90% Correlation Accuracy

The methodology was validated through rigorous testing:

✓Out-of-sample testing: Predictions made on held-out test set of 2,000 purchase decisions
✓Cross-validation: 5-fold cross-validation across product categories
✓Demographic robustness: Accuracy maintained across all age, income, and ethnic groups
✓Price point coverage: Validated from $0 freemium to $10,000+ enterprise products

Confidence Intervals

BuyerIQ provides purchase intent scores with statistical confidence:

• High confidence (85%+): Strong demographic correlation signals
• Medium confidence (70-84%): Moderate correlation with some variance
• Low confidence (50-69%): Weak signals, requires additional validation

Each analysis explicitly notes confidence levels and reasoning for transparency.

Model Updates & Maintenance

→Continuous improvement: As Anthropic releases model updates, we upgrade to the latest version
→Demographic data refresh: US Census data and purchasing behavior patterns updated annually
→Accuracy monitoring: Ongoing validation against real customer conversion data from BuyerIQ users
→Prompt engineering: Regular refinements to prompt structure based on user feedback and accuracy metrics

Known Limitations

Transparency is critical. Here's what BuyerIQ does not do:

✗Real-time behavior tracking: We predict intent, not actual clicks or conversions. Use analytics tools like Google Analytics or Mixpanel post-launch for behavior tracking.
✗Individual predictions: BuyerIQ predicts at the demographic segment level, not for individual users.
✗Market timing: We analyze purchase intent but don't predict when markets will adopt new categories.
✗Execution quality: Predictions assume competent product execution, pricing, and go-to-market strategy.

Research Citation

The foundational research behind BuyerIQ is publicly available:

"LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation"
Published: 2024
Validated against 9,300 real purchase decisions
Correlation accuracy: 90%

Read the full research paper →

See the methodology in action

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