What does quality mean in market research?
In market research terms ‘quality’ refers to the relevance, reliability, and accuracy of the data that has been gathered as part of online surveys conducted through market research platforms like the Cint Exchange.
High-quality market research data is essential for ensuring that the consumer insights that have been gathered are trustworthy, representative, meaningful, and ultimately impactful.
Having access to high-quality consumer insights enables organizations to make data-driven decisions efficiently and effectively.
What is poor-quality data in market research?
Poor-quality data can be the result of research fraud, poor open-ends from professional survey-takers or disengaged users. Fraudulent actors use a variety of methods, including velocity attacks and click farms, to gain access to rewards offered upon completion of online surveys.
The result is data that is unreliable, untrustworthy, and unrepresentative.
Outlining the risks of poor-quality data in market research
Bad quality market research data hurts. Receiving poor-quality data is not only frustrating, but it has real implications for everyone involved in the process.
What, though, are some of the risks and consequences attached to poor-quality data?
Limiting research insights
Consumer insights are incredibly powerful. High-quality, reliable, and trustworthy market research data equips organizations of all sizes and stripes with the ability to build business strategies, develop research-enabled solutions, publish credible research, and more.
Working with unreliable, poor-quality market research data makes actioning on insights difficult, limiting you and your organization.
Additional costs
Getting hold of market research data through online surveys costs time and money.
However, as budgets continue to shrink, workers across industries are expected to achieve more with less. Insights professionals across the industry are facing shortages of time and budget.
That means being strategic about every dollar spent in an attempt to ensure that the data you gather has the maximum impact on decision-making and strategic planning, whatever sector you work in.
Having to repeat online surveys due to poor-quality data potentially means running that same survey again, adding to your costs and taking up more of your time.
Having to repeat online surveys due to poor-quality data potentially means running that same survey again, adding to your costs and taking up more of your time.
Loss of trust
Businesses are increasingly vulnerable to AI-enhanced fraud. But by investing in technology to combat fraud, companies can protect their reputations and maintain customer trust.
Failure to do so may lead to significant financial losses and even legal ramifications.
Avoiding poor-quality data in market research
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 know-how, and engineering expertise to proactively address survey fraud.
Cint combines advanced tech with dedicated teams to ensure real people answer real surveys.
As such, 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. We combine advanced tech with dedicated teams to ensure real people answer real surveys.
As the data quality landscape evolves, Cint is focused on collaborating with industry minds to identify and implement solutions, and improving knowledge on how best to tackle fraud throughout the workflow
Learn more about Cint’s approach to quality
Read more about Cint’s ongoing commitment to tackling fraud and ensuring high-quality market research data in the Restech space here.
