Quality

Confidently source insights from real people

As the world’s largest research marketplace, connecting real people to real questions is the cornerstone of our offering.

Bad data quality hurts. Receiving poor-quality market research data is not only frustrating, but it has real implications for everyone involved in the process. Potential consequences include the limiting of research insights, additional costs, and a loss of trust.

Cint is successfully taking decisive action against fraudulent actors who contribute to poor-quality data, seeking to exploit our platform and the entire online research industry at large through survey fraud.

Cint puts security and quality first to provide researchers with data they can trust.

Our approach

We detect and remove fraudulent activity so that you receive the answers that count

It’s crucial to recognize the importance of fighting AI-driven research fraud with innovative technological solutions. By prioritizing security, integrity, and collaboration, we can not only protect our own business but also the industry at large.

We do this by pairing advanced tech solutions with dedicated teams to ensure that your online survey responses come from real people.

Here’s how it works:

### Advanced  Tech

Our AI-powered Trust Score combined with proprietary fraud fighting tech solutions — such as Server-to-Server — are reinforced by third party fraud detection solutions to remove fraud from the sample workflow.

### Dedicated  Teams

Our dedicated Trust and Safety experts across product, engineering, and operational teams develop tech and operational solutions to eliminate bad actors.

Real People

This powerful combination ensures real people answer real surveys.

Quality delivered through tech and humans

Read more about our multi-faceted approach to delivering real responses to your online surveys:

Secure

End-to-end encryption safeguards our sample workflow. Our server-to-server solutions guarantee secure survey entries, effectively eliminating ghost completions. We also offer a comprehensive set of technology-driven solutions to ensure the integrity of both respondents and surveys.

Predict

Trust Score proactively predicts fraud before it happens, employing AI to identify potentially fraudulent participants, and machine learning to continually evolve.

Protect

Cross-functional teams spanning product, engineering, and operations ensure the integrity of our market research platform through ongoing innovation, strategic program deployment, and strict policy enforcement.

Partner

Cint uses top-tier third-party solutions that work seamlessly with our data quality protocols. Our ever-changing array of third-party vendors assist Cint in preventing attacks from automated scripted bot attacks, deduplication, fraudulent VPN usage and IP addresses.

Our strategy is anchored by five key data quality pillars, supported by continuous assessment

Our comprehensive quality strategy encompasses all areas of our operations and products, built on five clearly defined data quality pillars. A weakness in any one pillar can affect the overall quality. To support each pillar, we have established programs that allow us to take both proactive and reactive measures to maintain and improve data quality standards. Cint evaluates performance on this front across a series of absolute and relative metrics.

Cint’s five pillars of quality:

Respondent quality

We define high-quality respondents as those who are real, unique, representative, and engaged.

Supplier quality

We define high-quality suppliers as those who provide high-quality respondents (i.e. real, unique and representative).

Buyer quality

We define high-quality buyers as those whose online surveys provide a good experience for respondents and who are taking the necessary steps to ensure quality on their end.

Operational/service quality

We define high operational quality as a means of understanding our delivery of excellent services through a combination of critical measures.

Product quality

We commit to designing and selecting best in class products to prevent fraudulent activity and ensure the integrity of our market research platform.

How we do it

Combining proprietary and third-party security solutions

Researchers depend on Cint to connect them to authentic respondents to collect high-quality consumer insights. We safeguard the Cint Exchange ecosystem by proactively developing industry-leading, AI and machine learning data security protocols to combat data fraud.

Security, safety, and privacy are at the heart of our organization. As the market research industry at large adapts to changes and challenges within the sector, so do we. Cint’s dedicated Trust and Safety team is backed by decades of market research industry and engineering expertise to proactively address survey fraud.

Internal Programs

Cint partners with underperforming suppliers to improve reconciliation rates and panel security, monitoring buyer reversal behaviors and offering guidance on best practices and improvement opportunities. Specialists analyze project-level quality issues to identify root causes of data fraud and implement corrective actions. Cint also continually updates its profiling question library to ensure researchers have access to high-quality questions.

Third-party solutions

Cint leverages premium third-party solutions that integrate effortlessly with our data quality protocols. Our constantly evolving network of external vendors helps Cint safeguard against automated bot attacks, deduplication issues, fraudulent VPN usage, and suspicious IP addresses.

Proprietary tech

Cint is at the forefront of developing automated data integrity checks using complex algorithms and always learning AI through Trust Score to identify and stop both auto-generated bot attacks and human fraud at scale.

We also offer a dedicated server-to-server API connection that securely connects sample buyers directly to the Cint Exchange helping to eliminate ghost completes and link manipulation.

Cint Trust Score predicts fraud before it happens

Cint’s proprietary Trust Score is the centerpiece of our global data quality commitment to deliver genuine insights and survey data from real people.

The Cint Trust Score model is powered by AI and machine learning, grading respondents each time they enter the survey workflow, performing survey data quality checks.

Designed to proactively predict when a session may result in a negative reconciliation it is powered by machine learning. The model is vigilant, adaptive, and ready to consume data signals to adjust to new patterns in fraudulent activity.

The Trust Score model uses artificial intelligence to grade survey participants based on a complex set of criteria, ensuring that potentially fraudulent behavior is flagged promptly, with the survey session in question being terminated with immediate effect. Its inherent learning capability means it can adapt to the ever-evolving landscape of survey data quality.

Proactive prediction

Trust Score predicts when a session may result in a reversal, allowing for timely intervention and market research fraud risk mitigation.

Vigilant and adaptive

Trust Score continuously tracks trends and patterns, enabling Cint to leverage machine learning for regular updates to the model.

Continuous monitoring

Trust Score is continuously monitoring the impact and effectiveness across supplier, buyer, and country levels, providing valuable market research data to improve strategies and decision-making.

Country level thresholds

Trust Score balances fraudster catch rate with false positives, adjusted based on country, ensuring optimal accuracy and efficiency in detection.

Credentials

Quality validated by leading industry bodies

Cint is proud to be linked with the following global organisations that demonstrate our commitment to industry standards and excellence.

Global Data Quality initiative

We are a proud and active member of the Global Data Quality initiative involved in a range of working groups to align on an industry-wide solution to our industry-wide problem.

ESOMAR

We are committed to being a transparent sample provider. Take a look at our responses to ESOMAR questions.

Certification Institute for Research Quality (CIRQ)

Global HQ

CIRQ, a subsidiary of the Insights Association, was established in 2009 to provide assessment and certification services to marketing research, insights and data analytics firms seeking certification to ISO and other industry standards.

A 501(c)(6) non-profit entity, CIRQ is in compliance with all ISO requirements for certification bodies and is managed with oversight from an independent Board of Directors and an annual review by external authorities on ISO requirements.

CIRQ Certificate 20252 Cint Australia

Testimonials

Here’s what Cint customers have to say

Don’t just take our word for it. Here’s what some of Cint’s valued customers say about our ongoing efforts to maintain the highest standard of quality possible.

“It’s really important that the quality of the data that we collect is absolutely best in class, and the most rigorous. And that’s what Cint helps us with.”

Karen Nelson-Field

Founder, Amplified

“The reasons we selected Cint are global coverage, a shared vision in technology with a great roadmap for improvements, but also the commitment to quality.”

James “JT” Turner

Founder/CEO, Delineate

“We are interested in investing further in data quality and insights. We want to use Cint’s robust quality control mechanisms and analytics tools to help us with that.”

Thami Ndlovu

Founder, Datavise

“One of the things that I like with Cint and I’m confident about going forward in the future is that Cint will be staying a certain number of steps ahead of this bad part of the industry and helping to keep our data as accurate and clean as possible.”

Mark Wiser

Founder, Wiser Insights Group

Resources

The Quality Check: Why data quality is a shared responsibility

This episode of The Quality Check explores how everyone has a role to play when it comes to maintaining data quality standards.

The Quality Check: Cint’s five pillars of quality

Learn more about how Cint evaluates different aspects of data quality.

The Quality Check: Five top tips for suppliers

Get expert advice on supply-side quality issues with this episode of The Quality Check.

The Quality Check: Five top tips for buyers

Get the lowdown on five tips for buyers looking to ensure and maintain high standards of data quality.

Introducing the Global Data Quality feedback loop

Exploring a new framework for collective industry standards around data quality.

What is data quality and why is it crucial for business success?

We explore quality and explain why clean, reliable data is the foundation of trustworthy insights and smart business decisions.

The ghost completes of past, present, and future

Get an insight into how Cint has adapted to one of the biggest problems facing the market research industry.

Quality-related reasons to be thankful

Looking back at our efforts to combat fraud in 2025.

Discussing data quality at ESOMAR Congress 2025

Get the low down on what Cint CEO Patrick Comer and VP of Trust and Safety Jimmy Snyder had to say about combating fraud while protecting the respondent experience.

“Too good to be true”: The illusion of quality in the age of AI-generated survey responses

Cint’s Jimmy Snyder explores how we’re entering an era where survey responses may be polished and articulate — but not necessarily real.

Data quality drives return on investment — here’s how

Learn how reliable, accurate survey data leads to stronger decision-making, better targeting, and optimized resource allocation for effective insights.

Why poor quality data poses risks in market research

Understanding the negative impacts of poor quality data on the research industry.

Exploring AI, ethics, and data quality

AI isn’t going anywhere, so how do researchers incorporate it ethically and with quality in mind?

What is a ghost complete?

Learn what ghost completes are, why they’re an issue, and what Cint is doing to combat them.

What is a velocity attack?

Learn what velocity attacks are, why they’re an issue, and what Cint is doing to combat them.

What is a click farm?

Understanding what click farms are, how they operate, and how they can be combated.

Effective strategies for combatting fraud in the Restech industry

Fraud is an ongoing issue in the research and insights space. Here’s how buyers and suppliers can fight back.

Why is data quality so important when it comes to programmatic research?

Two experts explain why when you’re working with programmatic research, quality is key.

Fighting bots with bots: how to combat AI-driven fraud

Cint’s Jimmy Snyder, Vice President of Trust and Safety, and Shelby Downes, Senior Program Manager, discuss a range of approaches for taking action against the bots.

Inside the thinking AI brain

Cint’s experts break down the importance of our Trust Score model

How Cint keeps your data safe — and secure

A conversation with Cint experts Dhruv Mathur, Vice President, Information Security and Caroline Tahon, Data Protection Officer, on keeping data as safe and secure as possible.