Why measure outcomes?

In the modern media landscape, the idea of ensuring that upper-funnel marketing activities connect directly to lower-funnel results has become increasingly important. 

Put simply, outcomes measurement is a means of tracking how media investments drive specific business objectives. These are also known as outcomes, hence the name. Simple, right?

Across the industry, marketers need to understand the impact of their brand advertising budgets and to tell that impact story to stakeholders. After all, when every dollar counts, it’s crucial to know what their spend is actually delivering. 

Let’s explore why connecting attitudes to action is a fundamental way of proving the value of brand media budgets in an era where every cent is fought over, and every decision made before, during, and after a campaign can be critical.

What challenges does measuring outcomes solve?

Marketers know that every campaign comes with its own challenges to solve. 

For some, it is imperative to provide better proof of revenue from upper-funnel campaigns while also demonstrating the revenue-generating value of their own platforms. 

As Laura Manning, Cint’s SVP of Measurement, puts it, “connecting brand ad buys to real sales gives brand marketers the proof they need to get reinvestment in upper-funnel advertising that can shift not just inventory but consumer perceptions.”

Investing in outcomes measurement techniques can be a way of gaining a competitive advantage when it comes to competing for media budgets. For example, with an outcomes approach in place, it’s possible to chart the causality between metrics like brand lift and the eventual sales lift.  

On the agency side, many marketers feel under pressure from clients to go about demonstrably proving the return on investment (ROI) of agency services when it comes to launching cross-channel campaigns. 

They may also find themselves wanting to stop allocating budget to performance media campaigns by, again, providing evidence that brand campaigns really are capable of driving revenue. 

Why has measuring outcomes become so important? What is Cint doing about it?

Successfully integrating outcomes into wider media measurement strategies has become important for a variety of reasons. 

The outcomes approach is an opportunity to “close the loop” for upper-funnel campaigns and give momentum for requests for reinvestment in brand lift. Tying ROI to brand lift through outcomes measurement also presents an opportunity to win back lost media budgets from brands taking marketing in-house or investing in direct response campaigns.

“There’s a shift happening in brand measurement. As brands use MTA and MMM to connect touchpoints across the user journey, brand marketers are expected to provide proof that upper-funnel marketing efforts are generating revenue,” says Manning. “Outcomes measurement provides the simplest solution for publishers, platforms, and agencies to connect brand lift KPIs to real sales.”

Additionally, those data points allow marketers to make truly informed campaign decisions, providing a solid basis for potential optimization strategies to be ideated and eventually deployed. 

“Marketers have always known that brand investment moves the needle on sales,” said Kate Crandall, Principal Product Manager. “The goal of outcomes measurement is not just proving it, but making it actionable.”

Learn more about Lucid Measurement

Want to know more about how you can optimize lift results for your campaigns through Lucid Measurement even when budgets are shrinking?

Get in touch with Cint today.

Welcome to The Quality Check

Data quality standards are at the heart of everything we do at Cint. For our customers, high-quality data is paramount, and that’s why Cint puts security and quality first to provide researchers with data they can trust.

In addition to our blogs, reports, guides, and white papers that touch on all things quality, 2025 saw us launch The Quality Check, a new short-form video series hosted by members of the Trust and Safety team here at Cint.

Why data quality is a collective responsibility

The first episode of The Quality Check saw Jimmy Snyder — Cint’s VP of Trust and Safety Operations — exploring how and why tackling poor data quality is a joint responsibility: buyers, suppliers, platforms and researchers all play a role in maintaining data quality standards across the industry. 

On the buyer front, Snyder broke down everything from the role that the respondent experience plays in shaping quality to the importance of utilizing server-to-server redirects to combat fraud. 

When it came to understanding supplier responsibilities, he discussed the vitality of robust panel management and making sure that the people taking surveys are actually who they say they are — think of it as proactive policing, not reactive damage control.

For platforms themselves — like our own Cint Exchange — Snyder reminded viewers that they have a responsibility to build trust in their particular ecosystem by providing clear communication channels for buyers and suppliers alike.  

“When we all hold our weight and hold each other accountable, that’s when we truly unlock the power of reliable insights,” Snyder said. 

You can watch the full episode of The Quality Check below.

Want to know more about Cint’s commitment to quality?

Have you enjoyed this episode of The Quality Check and found yourself wanting to know more about Cint’s holistic commitment to quality? 

Head over to our Quality page to get the lowdown on how we pair advanced tech with dedicated teams to fight data fraud and deliver high-quality consumer insights. 

You can also check out more of our quality-related content by making your way to our Quality Hub right now. 

Welcome to The Quality Check

Data quality standards are at the heart of everything we do at Cint. For our customers, high-quality data is paramount, and that’s why Cint puts security and quality first to provide researchers with data they can trust.

In addition to our blogs, reports, guides, and white papers that touch on all things quality, we’ve launched The Quality Check, a short-form video series hosted by members of the Trust and Safety team here at Cint.

Cint’s five pillars of quality

This episode of The Quality Check sees Jimmy Snyder — Cint’s VP of Trust and Safety Operations — giving viewers the lowdown on Cint’s five pillars of data quality.

Learn how Cint takes decisive action against fraudulent actors and implements protective measures to ensure high-quality experiences for everyone on our platform, and discover how we ensure best-in-class products through a blend of third-party and proprietary solutions.

Watch the video in full below.

Key takeaways

Here are Snyder’s key takeaways:

1. Respondent quality: We define high-quality respondents as those who are real, unique, representative, and engaged. Cint has a multitude of protective measures in place to ensure that the respondent taking your survey is a real person who is supposed to take the surveys that they should be taking.
2. 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. We run operational programs to ensure we have high quality buyers deliver high quality experiences for respondents. 
3. Supplier quality: We also want high-quality suppliers who provide high-quality respondents. This means they’re real, unique and representative. As such, Cint runs operational programs to ensure our supplier quality is the best it can be.
4. Operational quality: High operational quality means that Cint is delivering excellent service by both subjective and objective measures. For example, we run a voice of customer program which allows us to assess customer satisfaction. And we pay close attention to all customer feedback.
5. Product quality: Our products are best in class when it comes to preventing fraudulent activity and ensuring the integrity of our market research platform. We ensure this by investing in a blend of third party and proprietary solutions. We never accept the status quo and maintain a dynamic roadmap for continued improvement.

Want to know more about Cint’s commitment to quality?

Enjoyed this episode of The Quality Check and found yourself wishing to know more about Cint’s holistic commitment to quality? 

Head over to our Quality page to get the lowdown on how we pair advanced tech with dedicated teams to fight data fraud and deliver high-quality consumer insights. 

You can also check out more of our quality-related content by making your way to our Quality Hub right now.

Welcome to The Quality Check

Data quality standards are at the heart of everything we do at Cint. For our customers, high-quality data is paramount, and that’s why Cint puts security and quality first to provide researchers with data they can trust.

In addition to our blogs, reports, guides, and white papers that touch on all things quality, 2025 saw us launch The Quality Check, a new short-form series hosted by members of the Trust and Safety team here at Cint.

Best practices for suppliers

This episode of The Quality Check sees Shelby Downes — Senior Program Manager at Cint — running through a series of best practices for suppliers who want to keep on top of data quality issues. 

From securing server-to-server redirects and vetting panelists to proactively monitoring subsource performance and suspicious activity, the video is a perfect primer for anyone on the supply side of market research who needs or wants a quality-related refresher.

Watch it in full below:

Welcome to The Quality Check

Data quality standards are at the heart of everything we do at Cint. For our customers, high-quality data is paramount, and that’s why Cint puts security and quality first to provide researchers with data they can trust.

In addition to our blogs, reports, guides, and white papers that touch on all things quality, 2025 saw us launch The Quality Check, a new short-form video series hosted by members of the Trust and Safety team here at Cint.

Best practices for buyers

This episode of The Quality Check sees Jimmy Snyder — Cint’s VP of Trust and Safety Operations — running through his top five tips for buyers looking to ensure and maintain high standards of data quality. 

From detailing how best to ensure that the entire respondent workflow has been secured, to giving a brief grounding in the fundamentals of survey design, this episode is a must-watch for any buyer who wants a succinct guide to best practices around data quality.

You can watch the full episode of The Quality Check below. 

What is customer segmentation?

Customer segmentation, also known as demographic segmentation, is the name given the process of dividing a large customer base into specific groups or personas. 

In the world of market research and media measurement, this involves breaking respondent groups (i.e. the people who actively take part in the surveys you’re looking to run) into smaller segments according to varying traits. 

These variables include things like age, household income, or employment status.

Why is customer segmentation important?

Breaking a wider group down into more specific customer or demographic segments allows for a deeper understanding of consumer needs, wants, and desires. 

Having access to insights that hone in on specific customer segments makes it easier for an organization to tailor their aims and objectives to the right demographics. 

Doing so allows business to more effectively curate target campaigns or rethink products, engaging with specific demographic segments based on the unique behaviors or characteristics shared within that segment.

How does customer segmentation work in market research and media measurement?

Cint audience segments are built from fully consented and continuously verified data from millions of global consumers who enter our research platform each year to answer surveys.

Survey respondents fill in screening questions to ensure that they’re a solid demographic fit for the research in question, minimizing the risk of surveying people who don’t fit into the correct customer segment.

As the world’s largest market research marketplace, we collate hundreds of suppliers in one place, enabling you to find a sample that fits the most specific of customer segments. Cint enables you to define your audience based on over 200 profiling attributes, getting you responses from the right people, whoever they are, wherever they are.

Learn more about customer segmentation at Cint

Ready to start your customer segmentation journey with us? Head here to learn more about Cint Verified Audiences and why our platform could make all the difference with your approach to demographic segmentation.

What is first-party data?

First-party data is the information an organization gathers straight from its customers and audience across all their channels, and always with their consent.

First-party data is powerful, and a comprehensive first-party data strategy is essential for making smarter business decisions.

In modern research and measurement, activating that data (turning raw consumer data into actionable insights that guide campaign strategy and validate decision making) is more critical than ever.

Why is first-party data essential in research and measurement?

First-party data is essential in the worlds of market research and media measurement because it provides organizations with crucial insights into individual consumers, their work, their family, their life, their wants, desires and their behaviors. 

As the data has been gathered directly from the source, first-party data can provide higher accuracy and relevance than second or third-party data. 

Customer data is more important than ever, but ever-evolving privacy standards and restrictions on third-party cookies are changing the way that data is collected. As a result, a comprehensive first-party data strategy is essential for making informed business decisions.

Cint’s first-party data platform

Built on fully consented, self-reported, and continuously verified data from hundreds of millions of consumers who enter our research platform to answer surveys, Cint Verified Audiences is a first-party data platform that enables agencies, brands, and researchers to add new dimensions to their first-party data — without having to conduct customer outreach.

Cint audience segments are built from fully consented and continuously verified data from millions of global consumers who enter our research platform each year to answer surveys.

Cint Verified Audiences help agencies and their brands strengthen first-party data strategies and enable platforms and publishers to support these efforts.

Learn more about Cint Verified Audiences

Want to know more about how Cint can support your first-party data strategy? Head to our Cint Verified Audiences page and get the lowdown on our first-party data platform.

Taking a collective approach to concentrating on quality

Data quality is the central pillar of the entire research ecosystem and consistent good data quality is only achievable when collective efforts are made. A recent webinar hosted by the Market Research Society (MRS) presented the result of organizations making a concerted and collective effort to work collaboratively to optimize data quality and minimize the harmful effects of fraud across the industry. 

Taking place in late January 2026, the discussion focused on a newly devised ‘feedback loop’ that aims to give the research industry a framework for sharing standardized feedback on issues pertaining to data quality and a means of categorizing exactly why respondents may be removed from survey data. 

This feedback loop — which was created thanks to MRS’ existing Global Data Quality (GDQ) initiative — is a means of engendering transparent conversations within a complex ecosystem. 

Cint is a proud participant in this initiative, leveraging its expertise and processes to help shape the codeframe, and contributing to a pilot with sample data. 

Cint’s role in the pilot was being the platform that Cobalt Sky used to collect data for a project. Cobalt Sky then attempted to scrub that data and append reasons from the codeframe, going on to provide feedback based on their experience. 

Cint also created an example of how suppliers may use the code frame: submission > storage > action.

The GDQ webinar showcased the power of collective thinking where MRS’ Debrah Harding was joined by Joanna Price (Kantar), Rebecca Cole (Cobalt Sky), Bob Fawson (Data Quality Co-Op) and Rachel Alltmont (Samplecon) to discuss the feedback loop. 

Speaking the same language

The MRS webinar introduced attendees — and the wider industry — to a ‘Code Frame’ that acts as the foundation of a shared language for discussing issues related to data quality in modern market research. 

That code frame outlines 18 reasons why a respondent may find themselves removed from a study for quality reasons, giving market research professionals a common vocabulary for identifying specific reversal and reconciliation reasons. 

It covers everything from explicit trap questions (defined as ‘failure at a question designed to check whether participants are paying attention’) to speeding (‘completing a questionnaire faster than reasonably expected’). 

The code frame is available online, and all the definitions refer back to the wider GDQ glossary.

What are the benefits of having a shared vocabulary for buyers and suppliers?

Taken collectively, organizations — like Cint — who feed into and work with the GDQ feedback loop can look forward to greater transparency and clarity, a swifter resolution of data quality issues, and strengthened trust between buyers and suppliers. 

“Reversals have often felt like a black box to suppliers who need insights to diagnose issues with supply and deploy the appropriate remedies,” says Shelby Downes, Senior Program Manager at Cint. “Buyers and suppliers should be excited for a framework that promotes transparency around reversals and can trigger conversations which lead to meaningful improvements in quality.”

Cint’s approach to quality feedback loops

At Cint, we’ve established our own foundation for transparency through the creation and implementation of feedback loops which ensure we maintain a healthy and accountable data collection ecosystem where participants are rewarded fairly, and there is consistent monitoring of the quality issues buyers face.

Within the Cint Exchange, we provide customers with a reconciliation process that allows buyers to remove completes that do not meet quality standards. 

All buyers operating within our marketplace have a responsibility to submit their own reversals and every response that is reversed should be associated with a reversal reason. 

Reversal reasons range from ghost completes (which is when a complete for a respondent has been registered but there is no evidence of that specific respondent in the buyer survey) to speeding (where a respondent’s length of interview (LOI) was outside of the reasonable LOI for the survey) and auditing of this kind helps improve the health of the Cint Exchange. 

Like the wider GDQ proposal, it is a reconciliation policy that relies on a collective effort: Buyer reconciliations inform both Cint and our suppliers, enabling us to identify new fraud trends and take action to protect buyers.

The policy is underpinned by a range of tech solutions and programs to ensure its success:

• Platform Tracking – ensures users have full visibility of which completes are eligible for reconciliation, enabling buyers to review and process reconciliations quickly.
• Automated Monitoring – an automated protocol that enforces Cint’s reconciliation policy. It assesses reconciliations for potential mishaps, such as over-removals, and checks the validity of the reasons. 
• Human Validation – the automated system is backed by an operational program, where a team of specialists is on hand to conduct manual investigations where needed.

“Cint’s reconciliation policy provides us with data that is essential for the success of our operational programs designed to improve both buyer and supplier quality,” says Downes. “These insights mean we hit the ground running when diagnosing issues and identifying solutions.

Read more about Cint’s ongoing commitment to quality

Head over to our Quality page to get the lowdown on how we pair advanced tech with dedicated teams to fight data fraud and deliver high-quality consumer insights. 

You can also check out more of our quality-related content by making your way to our Quality Hub right now.

Introduction

Kathryn Failon is VP, Data & Measurement at Cint. In our latest article, she explores the increasingly important world of data clean rooms.

A data clean room journey

My data clean room journey began at the 2024 RampUp conference in San Francisco, which is where I first started hearing chatter about the subject in earnest. This can be attributed in part to the event host, LiveRamp, a data collaboration platform, having acquired Habu, a cross-cloud clean room specialist, to the tune of $200M just a few weeks prior. 

For an industry that had been pretty quiet on the mergers and acquisitions front in recent years, that was certainly one way to get people talking. Kudos to their marketing and PR teams on that one.

While the deal was not isolated insofar as companies advertising their investments in this fresh on the scene technology, it was one of the largest to date.

Companies like AppsFlyer, a mobile marketing analytics platform, and Optable, an identity management platform, had announced data clean room capabilities in 2022, and Amazon’s AWS Clean Rooms entered GA in March 2023. But the acronym DCR – and this is an industry that loves acronyms – didn’t really enter the mainstream lexicon until 2024.

That was the year when Netflix, Roku, Disney, and NBCUniversal began publicizing their partnerships with cloud-based data platform Snowflake. It was also when Google’s clean room solution BigQuery entered Google Analytics. Data Intelligence platform Databricks also launched with Samsung Ads in Australia and announced an agency partnership with Canvas Worldwide. 

Fast forward to April 2025, and WPP purchased clean room start-up InfoSum for a whopping $150M. Investments like that made it clear that DCRs were here to stay. 

Although I have taken a personal interest in the subject over the past few years to help set up Cint’s first DCR workflow with a client, they have yet to fully infiltrate the daily lives of my colleagues, team members, and clients. 

When asked to describe what a clean room actually is, I’ve been met with many puzzled looks and uncertain responses to the effect of “something to do with privacy and data,” which is not that dissimilar from how my parents would describe my job to anyone who asks them what it is that I do for work.  

As we start 2026, and as more companies look to collaborate with disparate data sets in a manner that is privacy compliant, a refresh is beneficial: What is a data clean room, and why are they important? And then more specifically, how does Cint use data clean rooms now, and what is next for Cint on the clean room front? 

There’s no doubt that the media measurement landscape will be impacted, and I look forward to navigating these changes with everyone in the months to come. Let’s dive in.

What is a data clean room?

A phrase that I learned over the summer that resonated for me is that a data clean room is a “strategic collaboration platform.” It offers a privacy-safe environment where user-level data is anonymized, which allows all parties to rest assured that they can combine datasets while remaining in compliance with various state and country laws. 

Why are data clean rooms important?

As we’ve established, data clean rooms are essential tools for privacy-compliant collaboration between organizations looking to share insights, allowing customers to merge and analyze partner datasets for more detailed consumer insights.

With securely combined data on their side, customers can accurately measure campaign return on investment (ROI), deepen and refine specific audience targeting, and ultimately uncover deeper customer attributes and behaviors without compromising user privacy.

Not only do well-functioning data clean rooms offer organizations and their customers protection from falling foul of aforementioned privacy regulations, but they also provide the added benefit (to advertisers and marketers alike) of enriching data. This gives customers an even better chance to adapt their campaign strategies and ensure that optimization is both possible and seamless.

How does Cint use data clean rooms?

Cint currently supports clients who are interested in implementing data clean room workflows and collaborating within these environments. Through our integrations team, we work closely with partners to ensure data is accessed and analyzed in a way that aligns with both client objectives and privacy requirements.

Cint is equipped to help clients navigate the technical and operational complexities of clean room adoption. As demand continues to grow, we remain focused on making these workflows scalable for our partners.

What’s next for Cint and clean rooms?

Data clean rooms may have entered the industry conversation through splashy acquisitions and high-profile partnerships, but their staying power is rooted in something far more fundamental: the need to collaborate with data in a way that is scalable, privacy-first, and future-proof. As regulations tighten and third-party identifiers continue to erode, the ability to safely combine insights across organizations is no longer a nice-to-have, it’s a requirement.

While the technology itself can feel abstract or intimidating, clean rooms ultimately facilitate insights without compromising trust. As adoption continues to grow across the media landscape, understanding how and when to use clean rooms will become an increasingly critical skill set.

Cint is well-positioned for this next phase. With experience supporting clean room workflows, we’re committed to helping clients navigate this evolving landscape with confidence. The clean room conversation may only have gone mainstream in the last couple of years, but its impact on measurement is just getting started, and we’re excited to be part of what comes next.

Navigating a volatile and uncertain era

With the economic, political, and social landscapes in an ongoing period of flux and uncertainty, decision-makers, more than ever, need access to scalable insights that shape strategy and drive decisive action.

In 2026, checking the temperature of the market and assessing consumer attitudes has become a necessity. That’s why so many researchers rely on Cint’s scale and quality of survey responses to help them validate the decisions that matter the most. 

As the pace of change accelerates, the research industry is looking toward augmented methods that supplement traditional human insights. One such approach is the use of synthetic data. 

Synthetic data is a means of simulating respondent behavior, attempting to create a model based on previously-accrued insights; data that has been algorithmically generated from previously captured real-world data samples.

Phil Ahad, Managing Director of Data at Cint, shares insights into the practical uses of synthetic data and explains how human expertise remains at its core.

The enduring value of quantitative research in an evolving market

At Cint, we connect researchers to millions of voices from over 130 countries around the world, delivering a truly global approach to insights. 

Our marketplace, the Cint Exchange, provides customers with access to over 800 large, diverse, and niche global sample providers. With Cint, you don’t just get high-quality data: you get global scale. From Austria to Zambia, Moldova to Myanmar, we connect customers to engaged global audiences ready to answer surveys.

As the world changes, so do we. Cint leverages AI across research operations, production, sampling, and quality to make quantitative research faster, cleaner, and more efficient at massive scale. 

Despite those changes though, one thing remains constant: “Enterprises still need scale, consistency, benchmarking, and confidence. Those requirements do not go away,” Ahad says. 

He goes on to say that it is infrastructure, not survey execution, that makes quantitative research hard to disrupt. He points to having reliable access to people, identity resolution, profiling depth, longitudinal consistency, and governance as potential stumbling blocks. As Ahad says, “Those capabilities take years to build and compound over time.”

Qualitative data can explain why something might be happening; quantitative data has the ability to determine whether it matters and whether leaders can act on that data with confidence. That, Ahad says, is what makes quant foundational to enterprise decision making.

How AI can move quantitative research from automation to unblocking strategic decisions

For Ahad, increased use of AI enforces rigor at scale, with humans providing the grounding truth. Together, they produce outputs executives can rely on, explain, and defend.

“The future is a hybrid model,” says Ahad. “Humans remain the foundation of the data, but AI reduces the amount of work we ask them to do. Humans anchor reality; AI intelligently fills gaps, predicts, and synthesizes based on rich human profiles, behaviors, and historical data.”

Adopting a hybrid model of this kind has the potential to improve both data quality and the respondent experience. Ultimately, it delivers decision-grade insights faster and with more confidence.

“This is not about replacing humans,” Ahad says. “It is about respecting them while removing friction.”

How Cint is leveraging technological advances

Ahad was keen to stress that, “Cint is already leveraging AI across research operations and production to remove friction at scale.” 

The next step for our organization is to begin actively applying AI technologies to data and insight creation in order to address what some (including Ahad) think of as the real restraint in quant: respondent burden.

Those burdens include factors like long or otherwise poorly designed surveys, which can impact respondent engagement levels. 

That’s where Cint’s position is unique. “We already sit at the center of the ecosystem. We have the access, the scale, and the data depth required to move up the value chain,” says Ahad.