Leveraging generative AI for research insights

Editor’s note: Diane Lauridsen is head of consumer insights at UScellular.

Use of artificial intelligence (AI) is a hot topic across the research community, including how and when to use (or not use) AI for insights. Earlier this year I conducted a case study to explore if the use of generative AI could be leveraged to develop a messaging strategy with a goal to improve conversion from consideration to brand choice. This consumer journey is well-known by marketers, with many books written about the value of brand equity. While we talk about brands as having financial value and assets to be managed, ultimately brands exist in the minds of consumers, and this means they can create meaningful relationships. Brands that create meaningful relationships and manage both a consumer’s head and their heart hold stronger equity. In other words, we connect with brands that seem to speak our language and reflect the image we want to project.

Brands also help to reduce the cognitive load of life. Psychologist Daniel Kahneman suggests that brand loyalties help us lower the amount of thinking and decision-making we must do in a day. We have more than enough processing to do without having to stress over every purchase. Everyday decisions tend to be made using System 1 decision-making, while we use our rational and conscientious brain (System 2) for more rational decision-making. An Ehrenberg-Bass Institute and B2B Institute study noted that in addition to focusing on trying to build specific brand associations in consumers’ minds, it may be equally important, if not more important, to manage the number and interconnectedness of the brand’s associations. The higher the overlap and interconnection of these brand with category associations the lower the likelihood that customers will switch to another brand and the stronger likelihood the brand will convert consideration to choice (brand and category density). 

Evaluating the limits of generative AI in research 

Our case study came to life to assess generative AI’s ability to capture category associations, and to better understand its ability to replicate primary research. The overarching lesson: generative AI (with guardrails) can be used in research to provide insights.

In this case study, AI was used to successfully develop a list of category associations to be used in brand density message strategy research; however, one question didn’t provide the breadth or depth of information. In short, generative AI isn’t a one and done solution. Rather, leveraging AI is more effective for generating insights when asking multiple questions and being specific in how the question is worded, as well as leveraging multiple AI tools to bolster confidence in accuracy.

To be a bit more specific, when asking a simple question of, “What comes to mind when you think about ‘category’?” AI generates broad, rational (head) themes that are scattered and lacking commonality. In comparing results from AI to results from primary research using the same question, consumers provided descriptive words, images, colors and emotive phrases to describe the category. This led to a learning that it is best to ask very specific questions, drill down to get to specific words, associations and imagery that consumers would use to describe a brand until all variations are exhausted. By doing this I was able build a full understanding of a category using AI – asking questions such as, “What images come to mind when you think about (CATEGORY)?” and, “What specific features come to mind when you think about (CATEGORY)?”

To boost confidence with results, I used multiple AI sources and then I looked for commonalities to refine the output. These AI findings were cross-checked to primary research which confirmed that AI could indeed be used effectively for this type of analysis by using multiple questions balanced with researcher judgement and creativity.

Leveraging AI associations for the category speeds up the research insight process – saving time and money. AI can be used to supplement, rather than replace, human creativity and judgement.

In summary, generative AI proved useful for broad questions:

  • AI is more consistent and impactful when asking multiple questions.
  • AI can be used to save time, for example auto coding: read and analyze hundreds of comments in seconds.
  • AI provides comprehensive high-level themes.
  • Leveraging gen AI associations for the category can speed up the research insight process, saving time and money.