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Data Quality You Can Defend

Review flagged answers and the evidence behind them. Quality signals guide review; they are not proof of fraud.

Multi-signal fraud scoring
Flag, don't silently reject
Method-aware quality checks
Clay response signals passing through a quality gate that catches rushed or low-quality inputs.

Our posture: flag, don't silently reject

Borderline responses go to human review. Exclusions keep the response and the reason on record, so you can trace the sample behind each result.

One Score, Many Independent Signals

Multiple signals contribute to a quality score and a review recommendation.

Scoring signals and speed benchmarks
Speed measured against your survey's own median completion time
Pattern checks: straight-lining and alternating answers
Open-text quality: gibberish, placeholders, pasted content
Network and device: IP velocity, fingerprinting, duplicates

Why per-survey medians matter

A 90-second completion is suspicious on a 20-minute conjoint and perfectly normal on a 3-question pulse. Benchmarking speed against each survey's own median keeps flags meaningful instead of arbitrary.

What Happens to a Flagged Response

1. Scored

Every signal reports its evidence; the weighted score and flags are stored with the response.

2. Recommended

The score maps to a recommendation under your quality policy — accept, review, or exclude.

3. Reviewed

Review-band responses go to a human queue with the full evidence. Reviewers include, exclude, or escalate.

4. Recorded

Exclusions are snapshotted separately from deletion — nothing disappears, and every analysis base is reconstructable.

Attention checks, built in

Add attention-check questions as a first-class question type — trap questions with a known correct answer that validate respondents are actually reading, right inside the builder.

See How This Compares

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How other tools handle data quality

Evaluating platforms? See how Gotopico stacks up against the tools on your shortlist — data quality included.

Browse comparisons →
Analytics

Where clean data pays off

Quality scoring feeds straight into analysis: charts, crosstabs, and exports built on the responses you chose to keep.

Explore analytics →
Statistical honesty

The other half of defensible research

Clean responses deserve honest statistics: visible sample sizes, confidence intervals, and significance done right — never paywalled.

See our statistical standards →

Run Research You Can Stand Behind

Multi-signal fraud scoring, method-aware quality grades, and an audit trail from raw response to published chart — on every plan.