DATA QUALITY

Protect the value of every response.

Data quality starts with clear respondent eligibility, a deliberate sample plan and study-specific expectations agreed before execution.

QUALITY AS PART OF SCOPE

Quality is a shared project requirement. Use the topics below to identify what your team expects, what must be clarified and which responsibilities need to be assigned in scope.

A PROJECT-SPECIFIC APPROACH

Turn quality expectations into project requirements.

Explain who should qualify, which responses or behaviors would be unacceptable, how quotas should be structured and what needs review. The relevant approach and ownership can then be defined in scope.

Illustration of a quality process from sample collection to validation

WHAT TO DISCUSS

01

Respondent eligibility

Separate must-have qualifications from useful profiling variables, and make clear which conditions determine participation.

02

Sample requirements

Document requested completes, quota cells, target markets, survey length and any criteria likely to affect incidence or availability.

03

Project quality requirements

Identify the checks, thresholds, exclusions or review expectations that need to be discussed and assigned before execution.

04

Execution scope

Clarify responsibilities, fieldwork expectations, escalation points and delivery requirements so the agreed scope remains unambiguous.

These topics frame the quality conversation. The measures applied to a project depend on the requirements and capabilities confirmed in scope.

HOW VERISAFE WORKS

Signals reviewed across identity, access and response behavior.

VeriSafe is GNL Research’s proprietary quality platform. Specialized third-party capabilities may also be combined with GNL controls where relevant to a project.

Account integrity

Checks for duplicate accounts and repeat participation against the relevant project rules.

Device and network review

Device, IP, proxy and VPN signals are reviewed alongside geographic consistency.

Response behavior

Completion speed, open-text quality and consistency across answers can be assessed.

Automation detection

Automated or bot-like participation is screened through GNL’s proprietary controls.

Identity checks

Identity or phone verification can be applied where it forms part of the participant or project requirements.

Human review

Flagged activity can be reviewed manually, with an additional project-end quality review.

OUR QUALITY PROCESS

Quality decisions stay visible from sourcing to resolution.

  1. 01

    Source Review

    Confirm the proposed mix of Koalur and any selected partner supply for the audience and market.

  2. 02

    Respondent Eligibility

    Translate qualification, exclusion and previous-participation rules into the project setup.

  3. 03

    Pre-field Requirements

    Agree sample structure, quotas, survey conditions, quality expectations and responsibilities.

  4. 04

    In-field Monitoring

    Review delivery and relevant quality signals while the project is running.

  5. 05

    Quality Review

    Assess applicable account, device, network and response-behavior signals, including flagged cases.

  6. 06

    Issue Resolution

    Manually review flagged records, remove records confirmed as invalid and address any replacement requirement under the agreed project terms.

OUR PRODUCT

VeriSafe

VeriSafe reflects our focus on data collection and fraud prevention. The conversation begins with respondent eligibility, sample structure and the study-specific requirements that need to be addressed during execution.

Its role is defined project by project. Tell us which quality concerns matter to the study, and we will clarify the relevant checks, responsibilities and scope before work begins.

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