Affinity Clustering template

Mural’s affinity clustering template gives your team a ready-made canvas for sorting through messy, unstructured information and finding the patterns that matter. Rooted in Affinity Clustering from the LUMA System, a human-centered design method developed by LUMA Institute (a Mural company), this template helps you group ideas into themes, spot hidden connections, and move from raw data to clear direction.

Whether you’re working through customer interview transcripts, sprint retrospective notes, or a pile of post-brainstorm sticky notes, affinity clustering (also called affinity diagramming or affinity mapping) brings order to the chaos. Instead of staring at a wall of disconnected inputs, your team can organize ideas into groups, label what matters, and walk away with insights you can act on. For a deeper dive into the method itself, check out our guide to affinity diagrams.

What is an affinity clustering template?

An affinity clustering template is a pre-structured workspace designed to help teams sort large volumes of information by similarity. It provides designated areas for capturing individual data points, clustering related items together, and labeling the themes that emerge. Think of it as the digital version of covering a conference room wall in sticky notes and then reorganizing them into meaningful groups, except everyone can contribute from anywhere, in real time.

This method is especially useful when you’re dealing with qualitative data that doesn’t fit neatly into a spreadsheet: user research findings, brainstorm outputs, stakeholder feedback, or open-ended survey responses. The affinity diagram template format makes it easy to start broad, then narrow in on the patterns that matter most to your team’s next decision.

Key features of the affinity clustering template

Mural’s affinity clustering template supports the full clustering workflow, from initial data capture through final prioritization. Here’s what’s built in.

The template opens with pre-built grouping areas on the canvas so your team has a starting framework rather than a blank screen. Adjust, expand, or collapse these zones as your clusters take shape. Every data point, observation, or idea gets captured on its own digital sticky note, and you can color-code notes by source, participant, or theme to keep track of where inputs originated.

Where it gets interesting is the AI layer. Mural AI can automatically cluster sticky notes by topic, saving your team the manual effort of sorting through hundreds of inputs. With Preview Mode, you review AI-suggested clusters and rearrange them before committing, so the final output reflects your team’s judgment, not just the algorithm’s. And when you add your own titles to AI-generated clusters, they stay exactly as you wrote them; your categories appear the way you intended.

Collaboration runs through the entire experience. Distributed teams can cluster, discuss, and reorganize simultaneously on the same canvas without waiting for someone else to finish their section. And once your clusters are defined, anonymous voting surfaces the themes your team considers most important, turning a visual exercise into a clear set of priorities.

The affinity clustering template helps you:

• Identify patterns and themes buried in large, unstructured datasets

• Group qualitative data into meaningful categories your team can act on

• Analyze customer feedback, research findings, and brainstorm outputs in a shared workspace

• Reveal non-obvious relationships between ideas that might be missed in a list or spreadsheet

• Simplify complex information into a visual structure that’s easy to communicate to stakeholders

• Turn raw data into prioritized insights and concrete next steps

• Collaborate across locations and time zones without losing context

How to use the affinity clustering template

Getting started with affinity clustering doesn’t require a certification or a half-day workshop. Here’s a practical, step-by-step approach your team can follow in Mural.

Step 1: Gather your data

Before the clustering begins, bring all your raw inputs into one place. Add data to the template as individual sticky notes, one idea, observation, or data point per note. These might come from user interviews, survey responses, brainstorming sessions, retrospective feedback, or research findings. If you’re pulling from multiple sources, color-code your sticky notes to track where each input originated (for example, blue for customer interviews, yellow for internal feedback, green for survey data).

Step 2: Group ideas into themes

This is where the real work happens. Have your team review the sticky notes and begin moving related items into proximity. You can do this collaboratively in real time, or use Mural AI’s Cluster feature to generate an initial grouping, then adjust from there using Preview Mode.

As patterns emerge, you’ll start to see natural groupings form. Customer feedback might cluster into themes like usability issues, feature requests, and onboarding challenges. Don’t force categories too early; let the data guide you.

Step 3: Label your clusters

Once your groupings stabilize, give each cluster a clear, descriptive label. A good label captures the essence of the theme in a few words (“Onboarding friction” is more useful than “Category 3”). These labels become the vocabulary your team uses to discuss priorities and next steps.

Step 4: Identify insights and next steps

With your clusters labeled and visible, step back and look at the big picture. Which themes have the most sticky notes? Which ones surprised you? Use voting to prioritize the clusters that deserve immediate attention, then define concrete action items for each.

This is also a good moment to capture what you’ve learned. Summarize the top insights and share them with stakeholders who weren’t in the session. The visual format makes it easy for others to understand how you got from raw data to recommendations.

When to use affinity clustering (with examples)

Affinity clustering works best when your team has more information than it knows what to do with. Here are the scenarios where it adds the most value.

UX research synthesis

After a round of user interviews or usability tests, your team is sitting on a mountain of transcripts and observation notes. Clustering those findings by theme helps you move from “we heard a lot of things” to “here are the three user needs that matter most.”

Customer feedback analysis

Support tickets, NPS comments, and survey responses pile up fast. Affinity clustering helps product and CX teams organize that feedback into themed action areas instead of triaging one data point at a time.

Brainstorming and ideation

A good brainstorm produces dozens (or hundreds) of raw ideas. Clustering groups those ideas into related concepts your team can evaluate, compare, and build on, rather than scrolling through an unstructured list.

Sprint retrospectives

Retros generate a mix of wins, frustrations, and suggestions. Sorting them into clusters reveals whether the same issues keep recurring, and gives your team clear categories to assign owners and next steps.

Product discovery

Early-stage product work involves market research, competitor analysis, and stakeholder input from multiple directions. Clustering these inputs helps your team identify validated opportunity areas for the roadmap.

Research synthesis

Whether it’s a literature review, a set of expert interviews, or field research notes, clustering organizes your evidence base so findings are easier to reference, communicate, and act on.

Design thinking workshops

Outputs from empathy mapping, journey mapping, or “How Might We” exercises are natural candidates for affinity clustering. The themed insights feed directly into ideation, giving your next round of creative work a sharper starting point.

If your team regularly runs any of these exercises, having the affinity clustering template ready in your workspace saves setup time and gives every session a consistent structure. Explore more research and analysis use cases to see how Mural supports these workflows.

Tips for running an affinity clustering workshop

Running a strong affinity clustering session is less about the template and more about how your team engages with the data. Here’s what we’ve seen work well.

1. Make sure you have enough data to work with before you start. Clustering five sticky notes won’t reveal much. Aim for a dataset large enough that patterns aren’t immediately obvious, because that’s where the method earns its keep. If you’re running a brainstorm beforehand, give participants time to generate individually before the group session.

2. Resist the urge to name your clusters too early. This is straight from the LUMA Institute playbook: let groupings shift and evolve before you commit to category names. Premature labels can box your team into seeing what they expect rather than what’s actually there.

3. Assign sticky note colors to different data sources, participants, or sentiment types. This makes it easier to spot whether a cluster is dominated by one voice or represents a true cross-functional pattern.

4. Try silent sorting first. Give your team a few minutes to move sticky notes independently before opening up group discussion. This reduces anchoring bias and makes sure quieter team members have a chance to shape the outcome.

5. When it’s time to vote, give your team specific criteria. “Vote for the cluster most likely to impact retention” produces more actionable results than “vote for your top three.” Anonymous voting in Mural keeps the process honest.

6. Once your initial clusters are set, try reorganizing the same data into a different number of groups. Collapsing from eight clusters to four, or expanding from three to six, can surface relationships your first pass missed.