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Data Trust Score Overview

The Data Trust Score (DTS) is a unified quality signal designed to evaluate the reliability of a participant across their entire lifecycle, not just at the moment they enter a study. Unlike traditional fraud or quality scoring systems in market research, which rely primarily on one-time device checks, DTS combines real-time device intelligence with longitudinal behavioral history to produce a more accurate and defensible assessment of participant trustworthiness.

What the Score Represents​

The Data Trust Score is a numeric indicator of participant quality, expressed on a 0–1000 scale. It reflects both:

  • The integrity of the participant’s device and environment
  • How that participant has historically behaved in surveys

Rather than making a binary pass/fail decision, DTS provides a graduated score that captures nuance, improvement over time, and risk signals that may only emerge after repeated participation.

How It Works​

At a high level, DTS starts from a neutral baseline and adjusts upward or downward based on observed signals.

Baseline Trust​

Every participant begins with a fixed starting score of 600, representing neutral trust in the absence of history.

Device Quality Adjustment​

The system evaluates technical and environmental characteristics of the participant’s device. Strong, consistent device signals result in minimal impact, while weak or anomalous signals reduce the score more aggressively. This ensures that high-quality devices are not over-penalized, while risky setups are appropriately weighted.

Behavioral History Adjustment​

DTS incorporates historical outcomes collected through post-survey and transaction-level quality signals. This includes evidence of successful participation, flags, and failures. Behavioral impact is applied proportionally based on how much history exists. Participants with limited history are scored conservatively, while those with extensive records are scored with greater confidence.

Confidence Scaling​

Behavioral influence grows as more data is collected. Early interactions have limited impact, while consistent long-term patterns (positive or negative) carry increasing weight.

The final score is bound to ensure consistency and comparability across participants.

Personas: Interpreting the Score​

To make the score easier to understand and act upon, DTS assigns each participant to a persona. Personas are descriptive categories that explain why a participant falls into a given trust range.

They are not arbitrary labels, but summaries of dominant patterns such as:

  • Repeated low-quality behavior (Keyboard Masher)
  • Strong consistency and reliability (Gold Standard)
  • Early-stage participation with limited data (Newbie)
  • Signals indicating elevated anonymity or device risk (Incognito Operator)

Personas allow teams to reason about participant quality in plain language while still benefiting from a quantitative scoring model.

Why It Matters​

The Data Trust Score enables better decision-making throughout the research lifecycle:

  • More accurate quality screening by combining device and behavior signals
  • Stronger fraud resistance through longitudinal tracking
  • Clear explainability via personas rather than opaque scores alone

By shifting from point-in-time checks to an evolving trust model, DTS provides a more resilient, fair, and scalable approach to participant quality assessment.