Bury the consumer insight cemetery

How Generative AI helps capitalise on qualitative consumer insights.

Imagine you are a Chief Innovation Officer, and your job is to create winning new products and services that fit the market perfectly. Or you are a marketer, and your job is to cultivate a brand positioning that speaks to the personal values and needs of your audience.

What do you need to be successful? First and foremost: Data - nuanced, detailed, accurate data with depth. This is where qualitative consumer research comes in. You can listen to consumers' conversations on chat or social media, but the best way to get under their skin is to talk - in an in-depth Skype interview, a face-to-face exploratory interview, a focus group discussion or a workshop.

The face-to-face interaction, including body language, implicit meaning and physically expressed emotions, is key to generating insights with depth and relevance.

Despite its obvious benefits and critical role in innovation and marketing, the potential of qualitative consumer insight is often not fully leveraged. One of the reasons for this is the time and money required for such initiatives. The 'data' output of qualitative research is typically dozens, if not hundreds, of pages of transcripts or minutes of conversations. Until now, sifting through these mountains of text has been time-consuming and labour-intensive.

New tools boost speed and quality of research

New tools based on generative AI, more specifically Large Language Models (LLMs), are speeding up the process of analysing qualitative consumer data.
But what exactly are LLMs? Large Language Models are a cutting-edge technology that uses numerical, statistical methods to process and understand text documents written in everyday English, Chinese, French and other natural languages. The leading publicly available LLMs are Chat GTP and Bard. But there are a plethora of other models with specific features that will be available in the coming months.

One of the main strengths of LLMs is their ability to summarise large amounts of text. For this reason, tools based on generative AI speed up the process of analysing piles of transcripts, extracting key points and providing the most relevant patterns about consumers' needs and wants, experiences, preferences and emotional involvement.

The most advanced LLMs go far beyond summarising. They can take qualitative consumer insights and create new products, advertising concepts or social media content that resonates with the exact audience you have in mind. With this capability, generative AI is taking consumer insight into a new era. Consumer insight is no longer about static reports that take time to read and digest and end up on the MR department's report cimetary.

Fresh data is the new energy that drives creativity and innovation

Qualitative consumer insight is the new energy that drives creative, innovative business processes. Qualitative research data becomes part of a continuous effort to ask virtual, never-resting consumers to come up with winning solutions with perfect market fit.

You can see the importance of fresh data input with a simple copy-paste of - anonymised - qualitative insights. Even better if you have access to the most powerful version of an LLM. The $20 for ChatGPT plus, for example, is well spent. But to get the most out of LLMs, you need direct access via APIs. If you code, this will be a piece of cake. Alternatively, you can use one of the emerging applications that take care of the 'technical' part: First, processing and preparing large amounts of qualitative consumer data - transcripts, minutes, chat logs and user-generated content. Second, optimising the interaction with the AI model. For example, Insight-lab.ai has been optimised to 'digest' qualitative data.

P.S. Get your free trial of now and dive into the new era of qualitative research: https://www.insight-lab.ai/#apply