In AIMI studies, responses are often open-ended and can vary in depth, style, and level of engagement. As a result, quality management needs to be adapted, rather than following the same approach relied on for traditional surveys.
Compared to traditional surveys:
Responses may vary more in length, depth, and style
Repetitive or templated answers may occur more frequently
Participant engagement will not be uniform
Natural variability is expected for open-ended questions
As such, where possible, quality checks should be built directly into the survey flow, e.g., screening or termination criteria.
It is important that there is a cross-functional alignment on what constitutes high-quality responses and that built-in checks support it.
Different stakeholders may define “quality” differently. Here are some examples to consider:
Research teams may focus on clarity and insight
AI/ML teams may focus on structure, variability, or training usefulness
Global panel partners typically focus on fair evaluation and consistent criteria
To assist qualitative researchers in maintaining high standards, the Global Data Quality Initiative has published the Safeguarding Qualitative Research handbook. Drawing from this resource, our core recommendations include:
Protect all projects equally, as fraudsters will target any type of project
Run quality and fraud checks in screening surveys to weed out bad actors early on
Set behavior expectations in screening surveys for respondents
Use knowledge checks for specialist topics, but be mindful about respondent recall for specific events, e.g. a respondent might say that they have purchased a brand in the past month, but it was actually in the past few months.
We encourage you to review the GDQ’s Internal Approaches document for a full list of measures that can be taken to improve data quality
Balancing the quality of responses and feasibility
It is important to ensure that your study design and respondent experience make it possible for respondents to participate and successfully submit a response. Best practices include:
Aligning on quality criteria upfront
Using in-survey checks where possible
Reviewing data early and calibrating project settings
Maintaining a balance between quality standards and achievable performance
Using the correct qualifications to find your target audience within the Cint Exchange
This will make sure you can field on time, manage your budget effectively, and access all available supply for your project’s requirements.
Learn more about feasibility in the Cint Exchange in our FAQs.
Submitting correct reconciliations
If responses do not meet your requirements, they can be rejected within the survey (preferred) or during survey review.
Nevertheless, it is crucial to respect the respondents who have dedicated their time to providing insights. Before deciding to exclude a participant, evaluate if they demonstrated honesty and engagement. If so, their data should typically be retained. Cases where data lacks depth despite a participant being honest and engaged often point toward a necessity for refining the survey structure or the interviewing methodology.
Please note that sharing clear examples or data points of why a response needs to be reconciled helps to improve the future quality of responses.
Please keep in mind that not all variation in responses indicates poor quality and that some level of variability is expected in open-ended formats.
Learn more about the Cint Exchange reconciliation policy and how to submit reconciliations in our FAQs.
Iterative project performance
Unlike traditional surveys, AIMI studies are rarely optimal at launch.
They require:
Early testing to check and resolve project errors
Monitoring of real performance
Iterative adjustments to increase performance
We recommend that Project Managers closely track:
Drop-off points (especially media steps)
Conversion rates
Actual completion time vs expected effort
In the Cint Exchange, tracking performance data is made simple by leveraging the target group’s performance tab.
Learn more about tracking target group performance in our FAQs.
Platform Setup & Technical Validation
Before launching a study, we recommend that Project Managers ensure:
All redirects function correctly (complete, terminate, overquota)
Respondent IDs (RIDs) pass accurately
The end-to-end journey is tested, and a respondent can successfully submit a response
This is especially critical when:
External tools are used in the respondent experience
Custom logic or media capture is involved
Privacy considerations
Please ensure clear and explicit respondent consent is obtained:
Provide directions for taking the survey to set clear expectations for respondents
Explain, if possible, how the data will be used
Inform respondents of their data rights and processes for making further enquiries about their data and participation in the research, if applicable
To ensure full compliance with both local and international privacy regulations, we recommend consulting with legal experts or in-house privacy counsel to verify that all required measures have been implemented.