We have found that leveraging AI can improve live research and identify nuanced patterns and connections that live below the surface. Perhaps most valuable, AI serves as a dynamic activation catalyst for businesses, transforming deliverables into a platform that offers several ways to understand and interact with data.

In this post, we focus on two areas where AI is improving insights – the ability to interrogate data/commentary and probing open-ended responses.

One of the most powerful applications of AI in research is to analyze structured and unstructured data to uncover meaningful patterns and insights. Using a prompt-based platform trained on your proprietary data, coupled with external Large Language Models (“LLMs”), researchers can:

• Ask specific questions about quantitative data to find valuable learnings (e.g., “What is the demographic profile of consumers most open to Product X?”)
• Eliminate the need for extensive cross-tabulation of data (e.g., “What is the answer distribution to Question 5 in the survey by each generational cohort in the South?”)
• Using frameworks and mental models to analyze both quantitative and qualitative data in a myriad of ways (e.g., “Using a jobs-to-be-done framework, list the 20 most common core needs addressed by the X category, with a brief explanation of each.”)
• Identify relevant case studies of how other organizations addressed similar issues (e.g., “A key finding from this study is X. Provide three relevant case studies of how businesses in similar categories have successfully addressed this issue.”)

These are only a few examples of what is possible when interrogating data with AI, with use cases varying by the type of research study.

A limitation that came with the adoption of online research was the ability to probe, which was a mainstay of in-person and telephone research.

Using generative AI to pose meaningful and relevant follow-up questions based on open-ended responses removes this barrier. During a live survey, and based on instruction provided to the LLM, participant responses will be reviewed, and one or more follow-up questions will be asked.  For example, if a survey participant answers, “I like the product features,” this can be followed by an AI-enabled probe of “What specific features did you like?”

This has particular value for research that evaluates products, concepts, positionings, and advertising, where detailed feedback is extremely beneficial to product, strategy, and creative teams. However, the use cases extend far beyond testing, as it allows researchers to extract insights from survey participants who may not initially be forthcoming with feedback.

Russell Research has been actively investing in, developing, and testing AI applications for custom research, and are currently using/employing AI in some form throughout the entirety of the research process.  We’ve found that incorporating artificial intelligence across the process represents a remarkable opportunity to improve quality and more deeply understand target populations.

Be on the lookout for the next installment of this series where we’ll explore ways that AI can help extrapolate insights beyond what was posed in your research.