Symptom: Tags multiply and become impossible to maintain.
Cause: No governance: anyone creates new tags without checking for existing ones.
Fix: Define a small taxonomy first; use bulk-tag and merge instead of creating one-off tags.
Technical SEO
Title, meta description, canonical URL, OG tags, Twitter cards, BreadcrumbList, FAQPage schema, and WebApplication schema are configured for this insight clustering helper route.
Insight Clustering Helper (Free)
This insight clustering tool is for turning qualitative research notes and feedback into tags and themes (thematic analysis / affinity mapping), not for numeric analytics or generic dashboards. It helps product managers and researchers turn atomic notes from interviews, surveys, or support tickets into well-governed tags, clusters, and themes that stakeholders can understand in one page.
Move from messy qualitative notes to clear tags, themes, and cluster-backed insights you can defend in roadmap and research reviews. No login required; everything runs in your browser.
- Import notes via CSV (paste or upload), tag and cluster them with auto-assist (TF-IDF and k-means), and build themes with evidence and segment breakdowns.
- Export CSV, Markdown, JSON, or XLSX and share a URL snapshot so your team can review or continue synthesis later.
- Use the stakeholder summary and co-occurrence view to spot patterns and align on top themes before roadmap planning.
No login. Runs in your browser. We do not store your data on our servers.
No login. Runs in your browser. We do not store your data on our servers.
Paste CSV below, then click Import.
Notes (0)
| Select | ID | Text | Segment | Tags |
|---|
Paste CSV or load an example to add notes.
How it works
Paste or upload qualitative notes as atomic observations (one insight or quote per row). Organize them with tags and run local clustering (TF-IDF + k-means) to discover groups. Turn high-cohesion clusters into named themes with clear insight statements, evidence quotes, and segment breakdowns. Export or share a URL so your team can review or continue synthesis later.
Clustering
Auto-cluster uses TF-IDF and k-means (cosine similarity) to group similar notes into 5–12 clusters. You can set k manually or use auto. Clusters get suggested titles and keywords; rename and merge them to match how you think about the data. Treat clustering as a draft, not final truth.
Themes
Themes are insight statements backed by evidence. Select notes (e.g. from a cluster or by tag), then create a theme with a title and one-sentence statement. The tool records evidence note IDs and segment breakdown so your theme is defensible with quotes. Use the stakeholder summary to share top themes with leadership before roadmap or research reviews.
Exports
Export your workspace as CSV (notes with tags, cluster, themes), Markdown (top themes and stakeholder summary), JSON (full state for roundtrips), or XLSX (Notes, Clusters, Themes, Tags, Segments sheets). Use the template CSV for import so columns match: note_id, text, source, participant_id, segment, question, timestamp.
Pro tips
- Keep notes atomic: one observation or quote per row so tags and clusters map cleanly.
- Define a small tag taxonomy before bulk-tagging so you avoid tag sprawl and near-duplicates.
- Use segments (e.g. participant_id, question, source) so you can break down themes by cohort or question.
- Run auto-cluster with k=5–8 first, then rename clusters and merge or split based on your judgment.
- Turn high-cohesion clusters into themes by writing a one-sentence insight and attaching 2–3 evidence quotes.
- Export the stakeholder summary early and share it with one partner before socializing broadly.
- Use co-occurrence to spot tags that often appear together and consider merging or creating parent tags.
- Re-run clustering after adding notes so new data doesn’t sit in an orphan cluster.
- Keep theme statements testable: “Users struggle with X when Y” is better than “Users want better X.”
- Document your tag definitions in a short glossary so the next person can continue synthesis.
Common mistakes
Symptom: Clusters feel random or don’t match how you think about the data.
Cause: Relying only on auto-cluster without renaming or merging clusters.
Fix: Treat auto-cluster as a draft: rename clusters, merge similar ones, and assign notes manually when needed.
Symptom: Themes are vague and not defensible with evidence.
Cause: Writing theme statements without tying them to specific notes or quotes.
Fix: Attach 2–3 evidence note IDs to each theme and add a short quote in the stakeholder summary.
Symptom: Segment breakdown is missing or inconsistent.
Cause: Notes imported without segment (e.g. participant_id, question) or segment spelled differently.
Fix: Use a standard CSV template with segment column and normalize values (e.g. lowercase, trim) on import.
Symptom: Synthesis takes too long and feels overwhelming.
Cause: Trying to tag and cluster hundreds of notes in one pass.
Fix: Start with a subset (e.g. 30–50 notes), establish tags and themes, then scale with the same taxonomy.
Symptom: Stakeholders disagree on what the top themes mean.
Cause: Theme titles and statements are ambiguous or not tied to a clear insight.
Fix: Write theme statements in one sentence with a clear “so what” and keep evidence visible.
Symptom: Export doesn’t match what you see in the UI.
Cause: Export runs on a stale or partial state (e.g. before last clustering).
Fix: Re-export after any major change; use JSON export as the source of truth for roundtrips.
Symptom: Auto-tag suggestions are noisy or irrelevant.
Cause: Suggestions are purely frequency-based and ignore context or segment.
Fix: Use suggestions as a starting set; then refine by merging and deleting low-value tags.
FAQ
Is this insight clustering tool free?
Yes. The tool is free and runs in your browser with no login. You can import notes, tag, cluster, build themes, and export CSV, Markdown, JSON, or XLSX without limits. There are no paywalls, trial caps, or account requirements; all processing happens locally so your qualitative data never leaves your device.
Can I use this for both interviews and support tickets?
Yes. The tool works with any atomic notes: interview highlights, survey open-ends, or support ticket summaries. Use the segment column to separate sources (e.g. interview vs support) and break down themes by segment. The same tagging and clustering workflow applies so you can compare patterns across data types.
How does auto-clustering work?
Notes are turned into text vectors using TF-IDF. We run k-means (cosine similarity) to group similar notes into 5–12 clusters. You can set k or use auto. Clusters get suggested titles and keywords; you can rename and merge them. All processing runs in your browser so no data is sent to a server.
What is the difference between tags and clusters?
Tags are labels you assign (or suggest) to notes; they can be hierarchical and you control the taxonomy. Clusters are groups from an algorithm; they help you discover structure. Often you turn high-cohesion clusters into themes with insight statements and evidence so stakeholders can act on them.
How do I create a theme?
Select a set of notes (e.g. from a cluster or by tag), then create a theme with a title and insight statement. The tool records evidence note IDs and segment breakdown so your theme is defensible with quotes. Use the stakeholder summary to share top themes with your team or leadership.
Can I share my workspace with someone else?
Yes. Use the Share action to generate a URL that encodes the full workspace (notes, tags, clusters, themes, assignments). Anyone with the link can open it and continue editing in their browser. The URL is compressed so it stays usable even with many notes and tags.
What CSV format do I need for import?
Use the template: note_id, text, source, participant_id, segment, question, timestamp. At minimum you need note_id and text. Other columns help with segment breakdown and filtering. Download the template from the tool page to avoid header mismatches when you paste or upload.
Does the tool support large datasets?
It is designed for hundreds of notes (e.g. 500) in the browser. For very large datasets, consider splitting by segment or importing in batches and merging tag/themes after. Performance stays reasonable because clustering and tagging run locally without server round-trips.
What is the stakeholder summary?
An export section that lists top themes with insight statements, 2–3 evidence quotes, segment breakdown, and recommended next steps. Use it to align stakeholders before roadmap or research reviews. You can copy it from the Stakeholder tab or include it in the Markdown export.
How does this fit with other CraftUp discovery tools?
Use it after interviews (e.g. from the Interview Script Generator) to turn notes into themes. Feed themes into JTBD, personas, problem statements, or opportunity solution trees for prioritization and roadmap planning. Exports are structured so you can hand off cleanly to those workflows.
Learn more with CraftUp
Courses, blog guides, and glossary entries to deepen your qualitative synthesis and discovery skills.
Turn qualitative notes into clear themes
Use CraftUp’s discovery tools and courses to connect research synthesis with JTBD, personas, and prioritization so insights lead to action.
Freshness
Last updated: 2026-03-05
- Launched Insight Clustering Helper with tagging, TF-IDF/k-means clustering, and theme building.
- Added CSV/MD/JSON/XLSX exports and shareable URL snapshots for team handoffs.
- Included 3 example datasets (B2C learning, B2B workflow, support tickets) with 30+ notes each.
- Added stakeholder summary, co-occurrence view, and auto-tag suggestions (local, disclosed).