Quant – short for quantitative research – deals in closed-ended questions:

"Select one of the following."

"Rate from 0 to 10."

"Which of these apply to you?"

The goal is to measure what’s happening — to get to the truth of the matter in numbers.

It’s clean, scalable, and reliable.

But quant rarely tells you why those numbers exist.

It’s great at showing what happened — not what drove it.

Qual: The “Why It Happened”

Then there’s qual — short for qualitative research.

When you think of focus groups, interviews, open-ends, or even product reviews and social comments — that’s qualitative.

It’s where people explain, emote, and show how they really feel.

Video compilation from the question: "How do you see yourself using AI during the holiday season this year for things like shopping, finding gifts, or booking services?"

For many, qual is the favorite part of the process — where the story behind the data finally comes to life.

But it’s also slow, messy, and hard to quantify.

The thing that makes it rich — context — is what makes it hard to scale.

The Research Problem Nobody Talks About

Quantitative and qualitative research were never meant to exist in opposition. The divide grew out of logistics, not theory — different budgets, timelines, and analytical traditions that encouraged teams to specialize.

Over time, “quant” became synonymous with scale, precision, and statistical authority, while “qual” came to represent depth, empathy, and nuance.

Both earned their reputations, but both also became incomplete.

The irony is that these methodologies have lived in separate silos, but when they come together, you get more than the sum of their parts. You stop guessing what the numbers mean — and start hearing them.

Yet even as AI has arrived to supercharge research, it's largely made the silos deeper.

The Rise of Deep Dive Platforms

A new generation of specialized “deep dive” research platforms is emerging — each promising to fix one part of the insights process. Some automate survey design. Others turn transcripts into instant summaries or visualize open-end data at scale.

These tools have undeniably advanced the field. They’ve made individual workflows faster, more automated, and often more affordable. But they’ve also pushed teams further into their corners: quant platforms keep optimizing for precision and scale, while qual tools chase richer context and emotion.

One of the newest examples is AI-moderated focus group interviews — a fast, scalable way to capture human stories without the logistical weight of traditional research. It’s a breakthrough for qualitative work, making deep, open-ended conversations easier to run and analyze.

While these platforms help scale traditional focus group methodologies, they ultimately refine the process rather than redefine it. They make qualitative work faster, but not more connected to the numbers that drive business decisions.

The Shift Toward Unified Research

While the new wave of deep-dive platforms has pushed the field forward, it’s also revealed the limits of going deeper in isolation. Each tool is optimizing its corner — faster surveys or smarter transcripts — but few are bringing the full picture together.

These tools help teams work faster, but not necessarily understand faster.

At Videotape, we believe the next era of research starts with a simple premise: every number tells a story, and every story should inform the numbers. That means qual and quant aren't separate workflows — they're one continuous conversation.

Of course, researchers have always combined methods. But doing that today means logging into five platforms, exporting CSVs, and manually triangulating findings. True integration means the tools do that work for you.

You shouldn't have to choose between methods, toggle between platforms, or wonder if your qual findings match your quant data.

And now, for the first time, that’s actually possible.

Advances in AI are making it feasible to synthesize qualitative depth and quantitative scale automatically — linking emotion and evidence in real time. What once took weeks of manual analysis can now happen in hours — with the same people sharing both their answers and their stories.

Now you can understand which demographics, mindsets, and moments drive behavior — and see not just what people did, but what they felt and why.

It’s the precision of quant, cut with the humanity of qual, and it's all happening in the same place, with the same people, at the same time.

You should just understand your users – completely, and in one place.