How Has Qualitative Research Evolved with Research Tech & AI?

Over the past two episodes of The Campaign for Real Qual, we’ve established a foundation:

Episode 1: Why we need this conversation in the first place – qualitative research is evolving, but at what cost?

Episode 2: What makes great qual? We defined core principles to evaluate whether research tech is helping us do better qual or simply driving efficiency.

Now in Episode 3, we examine the key technological shifts that have shaped qualitative research since the turn of the century – asking whether these innovations have truly advanced insight, or simply optimized efficiency.

Mobile Phones: Making Ethnography Commercially Viable

For as long as qualitative research has existed, ethnography has been considered the gold standard. But for most commercial market research, it was simply too expensive or time-consuming to implement.

That changed with mobile ethnography. In 2008, while working with MESH Experience, I saw firsthand how mobile phones could reshape how we captured people’s behaviour. Even before smartphones became mainstream, a simple text message could capture a brand interaction in real-time – removing the researcher’s presence, and with it, potential bias.

As smartphones became ubiquitous, this shift accelerated. Participants could now document their experiences through photos and videos, providing researchers with a richer, more contextual view of real-world behaviour. And they could now express themselves on their own terms, in their own time.

This marked a fundamental shift - from researcher-led inquiry to participant-led insight generation.

From Bulletin Boards to Online Communities & Co-Creation

The early 2000s saw the rise of bulletin boards - an early attempt at asynchronous qual. For the first time, researchers could engage participants over days or weeks rather than a single session, allowing for wider geographic reach and more considered responses (although System 1 thinking teaches us that considered responses are not always better!).

The full power of asynchronous qual wasn’t realised until online community platforms emerged, allowing for a mix of individual and social tasks, image and video-based activities, and iterative discussions, giving researchers a deeper, more flexible way to engage participants.

As Tom Woodnutt, an expert in asynchronous qual, highlighted in January’s Asia Digital Insights Summit, this method brings:

1. Greater depth – Participants engage over multiple days, rather than a single session, leading to more input from everyone and richer, more reflective insights

2. More authentic responses – Without group pressure, people feel freer to express their real thoughts.

3. More iterative learnings – Researchers can adjust questions and explore ideas based on early responses, making studies more fluid and adaptive

More than just a new format, online communities reshaped the role of participants. No longer just respondents, they became co-creators - actively shaping product development, testing brand concepts, and engaging in real-time digital workshops.

Social Media and Other Passive Research Methods

One of the biggest shifts in qualitative research was the rise of passive data collection - where instead of asking questions, researchers could simply observe behaviours, conversations, and interactions as they naturally occurred.

While social media listening, search behaviour tracking, and passive mobile data collection expanded the ways we could observe behaviour, they lacked the depth and meaning-making central to qual. These tools are undoubtedly useful, but rather than replacing qual, they have ultimately served as complementary sources of data and on occasions, generate qual briefs when we need to understand the 'why' behind the 'what'.

These methods marked the industry’s first step toward automation, a pre-cursor to today’s AI-powered qualitative tools.

The Covid Impact: The Forced Shift to Online Focus Groups & IDIs

Before Covid, online platforms that could host focus groups and IDIs existed, but adoption was limited. Many clients and researchers still preferred face-to-face groups and depth interviews, believing in-person interaction was irreplaceable.

Then came 2020. And suddenly, we had no choice.

Almost overnight, Zoom, Teams, and purpose-built qual platforms became the default. Sessions that once took place in carefully curated viewing facilities were now happening from participants’ living rooms and home offices.

While researchers could quickly pivot, it became clear that moving online had changed the dynamic (especially for focus groups):

• A loss of in-room energy. Focus groups thrive on spontaneity, body language, and group dynamics. Online, discussions felt more structured, less fluid.

• Harder to read non-verbal cues – without physical presence, researchers had to rely entirely on verbal expression and tone.

• A more transactional feel – online sessions felt like a series of Q&A exchanges, rather than natural discussions.

There were of course benefits – convenience, lower costs (and no client travel budgets needed) and inclusivity of a wider range of participants. Some participants, less influenced by the presence of others were also more open and honest.

I’d hypothesise that the shift to online impacted research design beyond geographical reach; where previously a moderator would be trusted to internalise the objectives and was trusted with a large degree of flexibility in the discussion, modern discussion guides seem more structured and lengthier.

Reflecting these limitations, researchers increasingly turned to pre-tasks - an approach influenced by mobile ethnography and online communities. Over time, pre-tasks became a fixture in both online and face-to-face qual.

The shift to online has become entrenched. While in-person research has returned, online groups and IDIs remain a permanent fixture - not because they’re better, but because they’re easier and more cost-efficient.

AI & Automation: The Next Frontier in Qualitative Research

AI is already influencing every stage of a qualitative research project. In the coming episodes of The Campaign for Real Qual, we’ll explore AI’s impact across different aspects of qual:

1. How AI is Reshaping Research Culture 

2. Research Design in the AI Era

3. Human vs AI Moderation

4. Analysis Using AI

5. AI-Enabled Reporting & Insight Dissemination

6. Maintaining an Original Voice

Conclusion: What Have We Gained, What Have We Lost?

Over the past two decades, qualitative research has gone through seismic shifts - some that have genuinely enhanced our ability to generate deep insight, and others that have made research more convenient but not necessarily better.

Few would argue against the benefits of mobile ethnography and asynchronous qual. These approaches have given us richer, more contextual insight, allowing us to capture real-world behaviours as they happen rather than relying on memory and post-rationalisation. They have made qual more inclusive, more scalable, and more participant-led, providing researchers with the opportunity to design more creative and engaging studies – much needed when participant engagement is at an all-time low.

But other changes have been more about efficiency than quality. The widespread adoption of online focus groups and IDIs, particularly post-Covid, has been driven more by cost, logistics, and accessibility than by a belief that they offer better insight than in-person discussions. The energy of a room, the subtlety of non-verbal cues, and the richness of a natural, face-to-face exchange - these things don’t always translate well to a screen. A new generation of qualitative researchers watching focus groups and IDIs via Zoom may never fully appreciate the felt sense you get as a moderator (or even an observer) when face to face.

Then there are passive methods, which promised a new kind of qual - one where we wouldn’t need to ask questions at all, just observe behaviours and conversations at scale. A useful addition to the researcher’s toolkit - they tell us what people do but not always why, complimentary to qual rather than replacing it.

These technological shifts have reshaped client expectations. Many brands now prioritize speed and scalability, sometimes at the expense of richer, more exploratory insight. As a result, qualitative researchers increasingly need to educate stakeholders on the trade-offs. Bridging this gap between research rigor and client demands will be crucial as the industry continues to evolve.

Which brings us to the next major shift in qualitative research - AI. For early adopters, AI is already changing how we design, moderate, analyse, and report qual.

That’s what the next few articles in The Campaign for Real Qual will explore.