The research behind vcrowd—and why it works

Good research needs proof. This study brings it.

The core finding

A new method called Semantic Similarity Rating (SSR) turns open text into realistic Likert-style purchase-intent scores. It asks the model to write a short opinion first. Then it maps that text to a 1–5 scale using embedding similarity to reference statements. The result looks and behaves like real survey data. arXiv

What the study did

• Evaluated 57 consumer surveys with ~9,300 human responses in personal care.

• Compared human distributions to synthetic ones.

• Measured two things:

  1. how close the distribution is (via KS similarity), and

  2. whether concept rankings match (correlation vs a human test–retest ceiling).

• SSR hit ~90% of human test–retest reliability and preserved realistic spread on the 5-point scale. arXiv

Why this matters for PMs and PMMs

• You don’t just get a number. You get text + score.

• You keep variance and rank order across concepts.

• You can compare ideas and see the why behind the vote.

• You move from qual → quant in one run.

How SSR works (plain words)

• Ask for a short opinion first.

• Convert that text to a score using cosine similarity with five anchor statements (1–5).

• Do it many times. Aggregate into a probability distribution per response.

• This preserves the natural shape of answers and avoids narrow, skewed outputs from direct numeric prompts. arXiv

Where vcrowd applies it

• Concept testing. Compare A/B/C ideas and rank winners.

• Message testing. Score clarity, relevance, and credibility by persona.

• Pricing and packaging. Surface objections and value drivers.

• Objection mining. Pull themes and sentiment for sales enablement.

Why it’s credible

• Consistent method. Same brief. Same questions. Same anchors.

• Replicable. Many synthetic respondents per persona.

• Explainable. Every metric links back to verbatims.

• Aligned with human patterns. Distributions stay realistic; concept order holds up. arXiv

Practical tips

• Keep questions tight (10–30).

• Test one variable at a time.

• Segment by persona and ICP.

• Rerun often to track drift.

Limits and good practice

This is not a panel replacement for all cases. It is an evidence engine for fast screening. Triangulate with human panels, product analytics, and in-market tests as you scale spend. arXiv

Bottom line

SSR makes AI focus groups behave like useful research, not toy demos. It keeps nuance from text and converts it into decision-ready numbers. That’s why vcrowd can deliver instant customer insights you can trust—and act on.