When we first experienced ChatGPT in 2023, our reactions were astonishment, disbelief, and fascination.
Never would we have imagined that, in such a short time, artificial intelligence would become so deeply integrated into our conversations, everyday life and, most importantly, our work.
Today we can say with certainty that AI has already revolutionized qualitative market research (and who knows what will happen in the future!). But why integrate it into our work processes?
After careful analysis, for now, we have identified 7 key reasons!
Let's start with the first, certainly more unusual, but undoubtedly interesting motif: the creation of podcasts or audio summaries from search data!
🔍 How does it work?
Some generative AI tools, after receiving as input research data (such as audio transcripts of interviews and focus groups) are able to create in a matter of minutes:
Thus, this technology enables:
🎧 Listen to key evidence in audio format, making it easier and more immediate to enjoy the results.
🎙️ Simulate debates among virtual experts, to explore data dynamically and immersively.
🗣️ Interact vocally with content, asking questions and delving into specific aspects.
We have realized, from some of the testing we have done, that analyzing research data through an audio format can stimulate reasoning and foster new interpretations and reasoning, just as happens in a research debriefing. In essence, it can make analysis more immersive, turning it into an interactive experience with the data we have at hand, no longer just to be read. And who knows, maybe it will become a way to integrate data presentation in a more inspiring way!
The second reason is AI's support in cross-referencing data in ways we might not have considered. It is a bit like dealing with a “qualitative-quantitative synthetic” researcher who can help us to:
To make the most of AI in cross-sub-target analysis in qualitative research, it is essential to structure all information clearly and consistently, avoiding ambiguity. How?
📌 By standardizing the sample variables, without unclear abbreviations or acronyms (e.g. better “Women” instead of just “F” and “30-40 years old” instead of “30-40”)
📌 Maintaining consistency in file and variable nomenclature
📌 Correctly associating variables with respondents: for each participant, for example, relevant characteristics should be indicated (e.g., “Marco, 35 years old, Rome, Single, User Brand X”), naturally avoiding sensitive data such as last name or email
📌 Training artificial intelligence on context: to improve the quality of the analysis, it is important to always provide key details to help the AI interpret the data correctly
The third reason, certainly the most common, is the use of generative AI tools to summarize the key points of an analysis, facilitating and speeding up its written production. In particular, AI summaries can be used to:
Translated with DeepL.com (free version)
Certainly the main advantages are speed and efficiency. AI, in fact, helps us drastically reduce the time in which to produce a written text (to be understood as a summary email…certainly not the full report!), systematizing the main content clearly and effectively.
However, here are some critical aspects to consider:
⚠️ Overly general or verbose summaries: some templates can generate overly detailed texts or, conversely, too superficial and obvious
⚠️ Importance of the quality of the prompt: defining the prompt correctly is essential for precise and targeted insights…in short, never take anything for granted! However, there are models that suggest prompts for analysis, but we will discuss this in more detail in the future!
⚠️ Risk of loss of context: AI does not capture emotional or implicit nuances, typical of qualitative research!
Therefore, for optimal results, our advice is to always review AI summaries and combine them with our own point of view, our own sensitivity as researchers, adding the value of our own experience!
One of the advantages of AI in qualitative research is the ability to extract and analyze verbalizations quickly and efficiently. However, there is a crucial aspect to consider: some AI models tend to “invent” answers, generating verbalizations that may not actually be present in the collected data… To avoid this risk, it is therefore crucial to use tools that work exclusively on uploaded sources.
Advanced AI models exist to load specific datasets and extract authentic verbalizations. For example, by selecting a summary item, such as a key topic or a quoted brand, AI can return all occurrences in which that term appears, thus facilitating and speeding up the analysis and collection of those verbatims for use on our research reports. But this can only happen if the source data has been properly input and loaded into the system!
Of course, we will never tire of reiterating, an essential aspect to consider is data confidentiality. Not all AI models guarantee that the information used remains private and is not used to “feed” the system itself. Be careful, therefore, to always choose tools that ensure the protection of sensitive data!
Another reason to use AI advantageously is the ability to generate summaries, emails, and content directly in the original language, without having to go through intermediate translations. This not only speeds up the process, but also ensures:
✍️ Greater accuracy, avoiding errors or distortions that often result from machine translations
🗣️ Increased naturalness and adherence to context: advanced AI tools allow the linguistic tone and register to be adapted to the specific context, producing texts that are natural and consistent with the target language and culture… This can be particularly useful for international or multi-country research, where understanding linguistic and cultural nuances is essential for quality return of results
⏳ Optimizing time and productivity: eliminating the translation step allows researchers to focus on data analysis and interpretation, reducing processing time and improving overall efficiency
One of the most fascinating aspects of artificial intelligence is the ability to generate custom images, an option that stimulates experimentation and offers numerous opportunities. There are a number of templates, both free and paid, that enable the creation of customized visuals, icons, and images without having to resort to pre-packaged stock.
In the context of qualitative research, AI can thus be used to produce effective visual representations, such as detailed personas, even cast in specific environments/contexts, typical consumer identity cards or specific icons illustrating key insights.
A possibility that allows presentations to be more engaging, relevant, and distinctive, while also avoiding the frustration of searching for images on icon sites without ever finding them as we imagined them to be…
In short, with AI, you have the ability to get unique and relevant visual elements aligned with the context of the search…truly amazing!
In qualitative research, the ability to synthesize complex concepts into bullet points is crucial, especially in our reports. Thanks to AI, generating effective bulleted lists (such as a product's classic pluses and minuses, a product's purchase drivers…) becomes a faster process, but also a structured one. In fact, artificial intelligence allows us to organize information in a clearer and more impactful way, avoiding scattering and making content more readable and memorable.
🧠 Stimulation of reasoning and creativity
In addition to pure organization, automatic bullet point generation helps you think better about content. It is like having a virtual assistant that suggests possible structures, highlights connections between concepts, and can even unlock new drafting logic. This process stimulates reasoning, facilitating the construction of more robust and articulate arguments.
🚀 More efficiency in creating materials
Let's face it: when you need to prepare presentations, reports or summary documents, starting from an AI-generated list saves time and allows you to work with an already organized base. AI, in fact, can support us in selecting the most relevant information, suggesting priorities and categories, making the work smoother and less prone to creative blocks, also increasingly due to the stress of shortening delivery times.
Watch the video below and learn how AI is transforming the world of qualitative research! 🚀
