Not every survey answer comes neatly categorized. When you run a survey, some questions are closed (e.g. multiple choice), while others allow people to write freely - “Other (please specify)” or “Any additional comments.”

These open-ended answers are often the most interesting, but also the hardest to analyze. Reading them all manually takes time, and traditional tools struggle to summarize them accurately.
NEXT’s survey response quantification helps you quickly turn these free-text answers into structured, countable insights.

NEXT already has a powerful data processing pipeline that handles transcripts, tickets, and even surveys. That pipeline uses advanced clustering techniques to group related feedback into themes - ideal for rich, descriptive responses that provide enough context for deeper analysis.

However, not every survey dataset is suitable for that kind of processing.
Some surveys contain thousands of short, simple answers such as “Facebook,” “A friend,” or “I don’t remember.”
Others include incomplete or inconsistent entries - responses that don’t have enough text for the normal clustering pipeline to find patterns.

That’s where survey quantification comes in.
It’s designed for raw survey data - situations where responses are too short, fragmented, or minimal to justify full semantic clustering. Instead of trying to extract complex meaning, it focuses on identifying and counting recurring terms or phrases.

NEXT analyzes a sample of your survey responses to detect common phrases and recurring words. Based on that sample, it generates a set of categories (themes) and the keywords that define them.
It then scans the full dataset to match each answer to its most fitting category - producing a clear breakdown of how many people mentioned each theme.

For example, imagine a question like: “How did you hear about our promotion?”

After quantification, you might see:

This turns 1000s of individual answers into a compact, actionable summary - perfect for spotting trends at a glance.

NEXT’s highlight and clustering pipeline is built for rich qualitative data: transcripts, long feedback, or survey comments. It uses contextual language understanding to uncover deeper themes and relationships.

In contrast, survey quantification focuses on simplicity and scale:

You can think of it as a lightweight alternative: faster, cheaper, and designed for surveys that don’t contain enough text for deeper contextual analysis. Both approaches complement each other.
When survey answers are rich and descriptive, the standard clustering pipeline provides deeper insights. When they’re brief and repetitive, survey quantification gives you the quick numerical overview you need.

Quantified survey data appears directly in your NEXT Threads: When you ask about a survey, you’ll see a breakdown by theme.

Survey quantification is ideal when:

If your survey responses are longer, narrative, or contain rich contextual detail, NEXT’s standard clustering will likely give you deeper insights. For all other cases, survey quantification provides an efficient, reliable shortcut to understanding your data.

Using survey quantification, you can:

Survey quantification bridges the gap between open-ended surveys and quantitative reporting. It’s designed for short or minimal responses that fall outside the scope of traditional clustering - giving you a fast, structured overview of what people said, how often, and in what context.

By combining both clustering and quantification tools, NEXT ensures you can analyze any kind of feedback - from detailed transcripts to one-word survey answers - all within a single, unified workspace.