Sago Health is now TriVoca Health.

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Data Quality

Data integrity is central to strong market research, and TriVoca Health upholds this standard throughout the entire research process. We employ comprehensive quality controls—from initial sampling to post-field review—to ensure reliable, actionable data.

Sample & Recruitment

Before your project even begins, you need confidence in the voices responding.
We ensure data quality on the TriVoca Health panel with the following measures:

  • Multiple channels, including affiliate networks, publishers, mobile apps, print media, and in-person approaches, help us recruit new members to our panel.

  • We employ a variety of validation techniques to ensure panelist authenticity, including double opt-in, CAPTCHA, government-issued photo ID verification, digital fingerprinting, and IP verification.

  • Regular checks on recruitment traffic, lead quality, and member participation metrics to find opportunities for optimization.

  • Our team performs frequent reviews focused on minimizing fraud, along with utilizing AI tools and techniques.

  • Project Managers watch for and manage data quality issues in their assigned projects. Any suspicious accounts are shared with the Panel Management team, which will review and take necessary action, such as warnings, additional monitoring, or suspension.

  • Proactively deactivate accounts that breach our
    Terms of Use.

  • Sampling frameworks are tailored to project objectives, integrating national coverage, census benchmarks, and balanced demographic representation.

  • Vendor onboarding includes comprehensive due diligence, such as background checks, security reviews, and ongoing monitoring of performance standards.

In-Field

Data quality doesn’t stop once your project begins—it’s continuously monitored, analyzed, and improved throughout the entire fielding process. We combine behavioral analysis, advanced technology, and ongoing review processes to ensure your data remains accurate, reliable, and actionable from start to finish.

We Maintain Quality

Consistent Source Optimization

Removal data from partner sources is continuously reviewed and analyzed to:

  • Identify high-risk traffic sources
  • Prioritize high-quality respondents
  • Strengthen future data collection efforts

Early-Stage Review

When programming a survey, we conduct thorough checks at project launch to identify issues early and set a strong foundation.

Continuous Data Cleaning

Our team applies ongoing cleaning protocols throughout fielding, ensuring your dataset stays accurate as responses come in.

Advanced Fraud Prevention Technology

We leverage the capabilities of Research Defender to protect against evolving threats, including:

130+ Digital Fingerprint Signals

Identifying unique respondents through advanced tracking methods

24-Hour Activity Monitoring

Detecting unusual spikes or hyperactive participation patterns

“No-Fly List” Protection

Blocking known bad actors across multiple sources

AI-Powered Duplicate Detection

Identifying repeated responses across surveys and platforms

Third-Party Fraud Network Integration

Utilizing actively maintained fraud lists from adjacent industries

Emerging Threat Detection

Flagging suspicious technologies used to bypass safeguards

Post-Field Analysis

Once your results are in, we don’t just leave you to figure out if they’re reliable. Our approaches help confirm your data is trustworthy.

Before delivery, Project Managers (qualitative) or the Data Processing team (quantitative) validate and clean final data files, using a multi-level framework to flag and mitigate low-quality survey behaviors.

Three-Tier Evaluation Process

We leverage a three-tier process focused on measuring several key behavioral indicators to identify potential “bad actors” within the dataset. As part of this approach, the TriVoca Health team evaluates all responses for:

Speeding

Identifying respondents who complete the survey significantly faster than expected.

Straight-lining

Detecting patterns where respondents select the same option across grid or matrix questions without meaningful engagement.

Quality of Open-Ended Responses

Reviewing for relevance, adequacy, and coherence in text inputs.

This structured evaluation is one of the ways that helps ensure that only high-quality, authentic responses are included in the final dataset.

In compliance with data-cleaning guidelines, respondent replacements are issued, and panelists deemed unusable are flagged to the Panel Management team for appropriate action.