Mercury launches Qual at Scale for richer, faster surveys

Mercury Analytics has announced the launch of Qual at Scale, a new research solution that combines the reach of large-scale surveys with the depth of in-depth interviews within a single study and at a cost level similar to traditional quantitative research.

The Qual at Scale solution centres around MercuryIQ, an adaptive artificial intelligence engine designed to interact with survey participants in real time. As respondents offer answers to open-ended survey questions, the AI evaluates the responses and, if necessary, acts as a virtual moderator, probing with additional follow-up questions when answers are incomplete, vague or stray off topic. This method enables the collection of richer and more informative data across large sample sizes.

The system also incorporates question-specific information input by researchers, such as major themes and target insights. MercuryIQ applies this guidance to ensure that each interaction remains focused and aligned with the study's objectives.

"This marks a fundamental shift in how we approach qualitative research. For the first time, we can conduct hundreds — even thousands — of meaningful conversations in parallel, delivering both traditional quantitative insights and the rich nuance behind people's opinions. And we do it at a speed that aligns with today's fast-paced decision making," said Ron Howard, CEO of Mercury Analytics.

The second component of the offering is MercuryAI, a natural language analysis engine. After collection, MercuryAI processes thousands of open-ended responses within minutes. It scans for themes, tone, sentiment and extracts important quotes from the data. The system enables sorting of feedback by topic, audience segment or emotional response and operates without manual coding, aiming to eliminate delays and reduce subjective interpretations.

According to Mercury Analytics, MercuryAI enables teams to handle large volumes of qualitative feedback without compromising on the detail and nuance typically expected from such data. The intent is to streamline workflows, minimise bottlenecks and convert unstructured data into actionable insights.

The company states that Qual at Scale is applicable to several different types of research, including creative and message testing, product and concept feedback, brand and experience tracking, and campaign and policy evaluation. In all use cases, the system is described as being able to uncover not only surface-level responses but also the underlying reasons and motivations for participant opinions and attitudes.

Among its features, Qual at Scale can be configured to add qualitative elements to otherwise structured surveys or be utilised to create open-ended studies from inception. The technology adapts dynamically to participant responses, a design intended to maintain engagement and keep the analysis targeted and meaningful.

The company confirms that Qual at Scale is now available to researchers, agencies and organisations seeking to acquire deeper insight at a faster pace. The system is designed to meet the needs of research teams requiring both the scale of quantitative methods and the detailed perspectives of qualitative approaches, without the extended timelines or subjective bottlenecks often associated with large-scale qualitative work.