Research Methodology

How BuyerIQ generates hypotheses—and what you still need to validate

Last updated: September 2026

Overview

BuyerIQ is an AI-assisted hypothesis generator. It uses product information, a tier-specific prompt and a language model to suggest segments, market scenarios and experiments. It does not implement a calibrated purchase-prediction system.

A generated score is not a survey response, observed conversion rate, measured product-market fit or validated willingness to pay. External research does not establish BuyerIQ accuracy; we do not currently publish a product-specific predictive accuracy benchmark.

Data Sources

Inputs Used by the Application

✓Product information: User-provided descriptions, pricing and website-extracted content. These inputs can be incomplete or incorrect.
✓Field notes: User-reported observations can inform refinement. They are not independently verified or necessarily representative.
✓Seller uploads: Mapped transaction lines support deterministic totals and descriptive comparisons, subject to data coverage and mapping quality.
✓Model knowledge: General model knowledge is not live research. Sources are not verified merely because a model mentions them.

Segments Are Hypotheses

For B2B, useful hypotheses focus on roles and firmographics—not invented demographic differences:

• Role and seniority
• Industry and company size
• Buying committee and budget authority
• Procurement and security requirements
• Trigger events and pain points
• Distribution access and validation evidence

Model Architecture

Core Model

BuyerIQ uses an Anthropic language model through application prompts. Model selection can vary by route and deployment. Its role is to:

→Interpret inputs: Suggest possible positioning, needs and market segments from the supplied product context.
→Generate structured output: Produce report sections from the supplied text, not a separately sampled panel of respondents.
→Explain hypotheses: Offer rationale and proposed experiments; explanations alone are not proof.
→Support refinement: Reconsider recommendations when users provide new observations. Repeated generations can differ.

What Is Actually Computed

The buyer workflow does not separately compute product/person embeddings, semantic-distance estimates or calibrated confidence weights.

Buyer reports: Structured model-generated hypotheses. Dashboard scores summarize these generated values using heuristics.
Market scenarios: Prompts request explicit assumptions, annual units, geography and conservative/base/upside cases. These are not independently verified forecasts.
Seller metrics: Code computes signed line totals, preserves explicit zero profit, and reports coverage and reconciliation discrepancies. Quantity does not multiply financial amounts.
Margin and ROI: Margin is the unweighted mean of available row margins in percentage points. Capital ROI is net profit divided by net cost when available and nonzero. Undefined ratios retain UI-compatible zero placeholders with availability metadata.

Validation & Accuracy

No Published Predictive Accuracy Claim

We have not established a product-specific accuracy percentage. A defensible future evaluation would require:

✓Held-out outcomes: Representative, consented purchase or conversion observations not used to build the prompts.
✓Predefined metrics: Compare against meaningful baselines and report calibration, errors and uncertainty—not a borrowed correlation score.
✓Scope and robustness: Evaluate categories, buying contexts and repeated-run stability, reporting where results do not generalize.
✓Reproducibility: Record model, prompt, input and evaluation versions. Deterministic arithmetic tests do not measure model prediction quality.

Evidence Labels, Not Confidence Intervals

• Observed: Uploaded outcomes or user-reported observations, with their limitations.
• Retrieved: A supplied external source with provenance and date; model memory is not retrieval.
• Assumed: An explicit scenario input requiring verification.
• Generated hypothesis: Model inference to test in the field.

High/medium/low labels and heuristic scores are not statistical intervals. Older reports may lack evidence metadata and should be treated as unverified.

Model Updates & Maintenance

→Model changes: Provider and prompt changes can change outputs. A newer model does not by itself establish improved accuracy.
→Source freshness: Check cited dates yourself; the application does not guarantee annually refreshed demographic research.
→Customer validation: Field notes support learning but are not an automated, independently validated conversion benchmark.
→Prompt improvements: Evidence requirements guide generation but do not guarantee factual output. Verify important claims before acting.

Known Limitations

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

✗No real-time behavior tracking: Generated scores are not actual clicks, purchases or conversions. Validate with your own appropriately collected outcomes.
✗No validated individual or group prediction: Segment suggestions are hypotheses, not evidence of behavior by a demographic group.
✗No timing guarantee: Market scenarios do not establish when or whether adoption will occur.
✗Financial limits: Seller totals depend on correct mapping, signed refunds and a single reporting currency. Missing fees, mixed currencies and incomplete costs require review.

Research Citation

External research explores language-model purchase-intent elicitation. It is background reading, not a validation of this application or evidence that its experimental pipeline is implemented here:

arXiv:2510.08338 — external purchase-intent elicitation research.
Consult the paper for its methods, population, version and limitations.
Its results must not be interpreted as BuyerIQ product accuracy.

Read the full research paper →

See the methodology in action

Generate a starting set of market hypotheses, then test the important assumptions with customers and observed outcomes.

Try BuyerIQ Now →