The dividing line between qualitative and quantitative research is blurring, partly due to Conversational AI.

That's why our Data Specialist Demian van Gils takes you through this development and shows you how this traditional dividing line is slowly blurring. He kicks off this series with Conversational AI.

What is Conversational AI?

With Conversational AI, respondents engage in conversation with an AI assistant via text or voice. Respondents are not presented with fixed blocks of questions, but engage in a dynamic, natural conversation. They type or speak their answers, after which the system asks relevant follow-up questions.

We no longer program a complete, fixed questionnaire. Instead, we mainly determine the direction of the survey: the topics to be covered, the type of information we want to collect, and any limits, such as the maximum number of follow-up questions. Within those limits, the AI asks follow-up questions that tie in with the respondent's previous answers. This gives each respondent a customized route, without you losing control.

Why is Conversational AI relevant?

A fixed questionnaire is strong in consistency, but less so in nuance. You sometimes miss out on respondents' spontaneous thoughts, doubts, and subtle insights. That's where Conversational AI makes the difference: it responds to new information and adapts when the conversation takes a different turn. This offers three clear advantages:

1. Greater depth and relevance
Conversational AI allows us to ask questions that tie in precisely with what someone has just said. This gives you more context, motivation, and emotion. This makes it a powerful addition to quantitative research.

2. A more natural experience for the respondent
Respondents are given the space to talk about what is important to them. This leads to more spontaneous input and often longer, richer answers.

3. Flexible deployment
It doesn't have to be all or nothing. You can keep part of your research standard and apply Conversational AI in a specific block. This allows you to maintain control over time and budget, while still getting the extra focus you're looking for.

When should you use Conversational AI?

Conversational AI is particularly valuable when you want to gain deeper insight into respondents' personal experiences, motivations, or context. Because the conversation assistant can ask follow-up questions at relevant moments, this method works extremely well for exploratory research, such as concept testing, customer journeys, pulse measurements, and other studies where nuance is important. Conversational AI is less suitable for measuring developments over time or when numerical insights are necessary.

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