QuMind Unveils AI Feature to Automatically Check Survey Response Quality
03 April 2025

Authored by Josephine Tolond, Research Operations Manager at QuMind

QuMind has unveiled a new AI feature designed to automatically check the quality of survey responses, eliminating bad data in real time. This cutting-edge tool leverages the power of AI to save researchers countless hours of quality checking, making survey data collection more efficient and accurate than ever before.

The Challenge of Bad Data

Anyone who has worked in market research understands the frustration of poor-quality survey responses. These include participants who rush through surveys, giving answers that are irrelevant or nonsensical. Such responses often skew the data, leading to inaccurate insights and ultimately impacting decision-making. Manual data cleaning, by identifying and removing invalid responses, can be time-consuming, frustrating, and prone to human error. At QuMind, we set out to tackle this challenge by harnessing AI to enhance data quality in real time. Our goal was to develop a solution that not only identifies poor responses but also does so efficiently, ensuring researchers can focus on insights rather than data cleaning.

The feature uses advanced AI algorithms to automatically flag and remove respondents who complete surveys too quickly, without reading or considering the questions thoroughly.  Researchers will no longer need to manually sift through responses to identify this type of bad data. Instead, the AI will handle this task in real time, ensuring that only thoughtful, high-quality responses remain in the dataset.

Real Time Quality Checks - bad open ends & speeders

The core of this new feature is its ability to conduct real-time quality checks. By automatically analysing each respondent's data as they complete the survey, the AI determines whether their answers are adequate based on the context of the questions. For example, if a respondent provides a generic or irrelevant answer that doesn’t align with the question being asked, the AI will flag this response for removal.

This process will help to eliminate bad text responses, such as random or nonsensical answers that would typically be challenging to detect manually. The AI checks each response's relevancy and clarity, ensuring that only valid, meaningful data enters the final dataset. This significantly reduces the time spent on quality control and improves the overall integrity of the research.

Separate Storage for Bad Data

In instances where bad data is flagged, QuMind's platform stores these responses separately from the live survey data. Users can easily access these responses if needed, but they won’t be included in the analysis. This feature gives researchers more control over their data and prevents bad responses from impacting the overall results.

Moreover, the platform allows users to override the AI’s decisions when necessary. For example, if a researcher feels that a flagged response should be included in the data, they can manually adjust the settings to restore it. This flexibility ensures that the AI’s role is supportive rather than restrictive, allowing researchers to maintain control over the data and their research outcomes.

QuMind’s Vision for AI in Research

At QuMind, the integration of AI into the research process goes beyond simply improving efficiency; it’s about enhancing the quality and reliability of market research. The company believes in using AI to augment human capabilities, not replace them. While AI is a powerful tool for automating mundane tasks, QuMind recognises that human researchers are essential for critically evaluating and interpreting data.

AI can assist researchers by quickly analysing large amounts of data, identifying patterns, and providing insights in real-time.  However, the role of human researchers in evaluating and verifying AI outputs remains crucial. Researchers have the critical thinking skills needed to recognise biases, ensure accuracy, and contextualise the results AI generates.

QuMind’s approach to AI in market research is centered on collaboration. Their AI tool, QuAI, has been designed to focus on specific, ring-fenced and closed loop surveys, ensuring that the data it processes is both relevant and accurate. This level of precision enables researchers to leverage AI to streamline their workflow while still maintaining high levels of data integrity.

By combining the best of both worlds, AI's efficiency and human insight, QuMind aims to provide research teams with faster, more comprehensive, and actionable data that drives smarter decision-making.

New Talent Joining QuMind

As part of its commitment to enhancing its market research capabilities, QuMind is excited to announce the addition of Josephine Tolond to its team. With over 20 years of experience in market research project management, Josephine brings a wealth of knowledge and expertise to the company. Her extensive background includes managing complex international surveys, overseeing fast-turnaround projects, and in survey design, data analysis, and team leadership.

Josephine's experience makes her an ideal addition to QuMind’s operations.  The QuMind team is excited about the value she will bring, and will be invaluable in shaping the future of QuMind's platform and the services we offer our clients.