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What is Qual-at-Scale?
(And What Does it Mean for Research?)
What is Qual-at-Scale? (And What Does it Mean for Research?)
Over the past 70 years, quantitative data has grown in prevalence and popularity. Because tools such as surveys can quickly gather data across large samples of people, it can seem at first glance that this is the best way to understand a population, especially compared to more time-intensive data collection methods such as interviews and behavioral observations. Still, what these qualitative data methods lack in speed and sample size, they make up for tenfold in their ability to uncover nuance and complexity in human decision-making. Traditional surveys may tell us what people do, but it takes a conversation to understand why. And the why is what differentiates customer research that’s alright from customer research that’s outstanding.
The barriers that have held so many groups back from this kind of game-changing research are understandable. Qualitative insights are difficult to collect. Conversations are time-consuming and tough to organize. This complex data is challenging to analyze. These costs add up to a much steeper price tag.
That’s if you’re doing things the old-fashioned way.
New technology is now reducing the time, difficulty, and cost associated with finding these deep insights. AI is beginning to automate significant portions of the research stack– enhancing recruiting, scaling qualitative analysis, and even conducting video interviews. This technology-infused research is sometimes referred to as “qual-at-scale,” and more and more companies are using it to deliver what their customers actually want.
The first level involves using AI to perform discrete tasks that humans once performed. One example is a tool like Otter.ai, which uses artificial intelligence to transcribe conversations and, more recently, allows users to ask questions about the conversations. It eliminates some manual labor, but not much.
The second level is sometimes called “human-in-the-loop” because its primary functions involve collaboration between technology and humans. At this level, AI acts as an efficient research assistant. Fuel Cycle is an example at this level. It can recommend questions before the interview and create summaries analyzing the primary themes afterward. However, the tool requires a human to conduct the interview and know what questions to ask to identify the meaning, so more time is needed.
As AI advances, a new approach becomes possible. Imagine if AI could schedule and conduct the interview for you, analyze the transcripts for themes, and even generate quantitative insights from those interviews. It’s this final level of integration that we have implemented at Listen Labs. Our AI interviewer allows customers to provide feedback whenever and wherever works for them. It utilizes video to capture the full range of human expression. Once the interviews are complete, it can answer questions about the collected data.
By handling the heavy lifting of processing huge amounts of conversations, AI interviews give your team more time to focus on turning those insights into better products and experiences for your customers.
What is Qual-at-Scale?
Qual-at-scale is an approach to research that lets AI handle the time-consuming parts of research, freeing companies up to have more meaningful conversations. The goal is simple: remove the barriers that have traditionally made qualitative research expensive and slow while keeping all the depth and richness that makes these conversations so valuable in the first place. Instead of researchers spending weeks organizing interviews and analyzing transcripts, AI tools help process and find patterns in conversations with hundreds or thousands of people at once.
This kind of research offers three levels of AI integration.
A New Standard for Market Research
With qual-at-scale, the old trade-off between depth and scale is no longer a barrier. The real power of this approach isn't just in its efficiency or cost savings; gathering and analyzing customer insights with AI at this level has made deep, meaningful research accessible to organizations of all sizes. When more companies can access rich, qualitative insights, we all benefit from better products, services, and experiences that meet real needs rather than perceived ones.
The value of conversations in uncovering the reasoning behind customer opinion and behavior remains constant in a landscape of ever-evolving technology. Qual-at-scale makes tapping into that “why” through a path toward deeper, more actionable insights easier.
When to Use Each Type of Research (Qualitative, Quantitative, and Qual-at-Scale)
While Qual-at-scale may seem like a new catch-all category for answering questions, the most effective research uses each tool—qualitative, quantitative, or qual-at-scale—to best address the needs at a given time. There are still some situations in which traditional qualitative or quantitative data collection might be best.
Quantitative data collection
Traditional quantitative data collection methods often fail to capture the depth and nuance necessary for actionable insights based on complex motivations and behaviors. However, they are still useful for certain kinds of research.
Surveys can be valuable for validating trends across large groups quickly and efficiently. For instance, if a company has already identified potential pain points in its customer experience through qualitative research, a survey can help confirm how widespread those issues are within a broader audience. This makes surveys particularly useful for quantifying insights gained from smaller, more in-depth studies.
Additionally, surveys excel at collecting specific, structured data points, such as customer demographics, purchase frequency, or satisfaction scores. An e-commerce company might use a survey to determine the percentage of customers who find their checkout process too complicated. While these insights are less nuanced than what qualitative methods provide, they can be instrumental in identifying priorities for improvement and measuring the impact of changes over time.
Qualitative data collection
There are also certain scenarios where traditional qualitative data methods still hold a significant advantage over qual-at-scale, particularly when the nature of the research demands a personalized touch or deeper human involvement.
For example, when participants are expensive to recruit, such as executives or industry experts, there won’t be a large enough number of interviews to need extensive AI assistance to complete them. Additionally, a one-on-one interview with a skilled researcher can delve deeper into complex industry insights.
Traditional methods also excel during the initial discovery phase of research, especially when entering an unfamiliar market or exploring a new topic. In such cases, human conversations often reveal subtleties or make on-the-spot connections that an AI might fail to detect and follow in the questioning. For instance, a company investigating consumer preferences in a new market might want to start with in-person interviews to determine what questions to ask and what the company wants to know before expanding to a larger interview base.
Relationship-building is another area where traditional qualitative methods shine. In long-term engagements or when researching particularly sensitive topics—such as mental health—trust and rapport are critical. There can be a level of uncertainty around technology that can make some people hesitant to share their feelings around those sensitive topics. Having a human being there to build rapport is still a valuable tool in those cases.
Qual-at-scale data collection
While there are still a lot of instances where traditional qualitative or quantitative methods make sense, qual-at-scale is rapidly growing in other areas. When research requires large sample sizes or broad geographic reach, AI tools can engage hundreds or thousands of participants remotely and asynchronously in a way that’s able to collect nuanced insights. It’s also ideal when resources are constrained, whether in terms of time, budget, or available staff, as the efficiency of AI reduces logistical bottlenecks. Moreover, it excels in identifying patterns and themes across diverse feedback, offering insights that would be too time-consuming to uncover manually.
This approach is particularly valuable in:
Product development: understand customer pain points and desires to design or improve offerings.
Optimizing user experience (UX): identify areas of friction in the customer journey and get targeted solution suggestions.
Marketing strategy: uncover the emotional and rational drivers behind customer decisions, enabling more resonant messaging.
Competitive analysis: reveal why customers prefer one product or service over another to help stay ahead of the market.
Internal research: gather employee feedback to improve workplace culture and productivity.
And more uses yet to be discovered…