Affinity Mapping

Affinity Map

An affinity map is a method used to organise many ideas into groups with common themes. It is a great way to cluster secondary (facts and figures) with primary (feelings and emotions) and to create insightful knowledge from.

What is it:

A tool to organise many insights into groups with common themes or relationships.

Why use this method:

When you are working with a complex problem it can feel overwhelming. An affinity map keeps momentum when dealing with a large amount of chaotic data from multiple sources such as interviews, observations, desk research, being immersed in situations (ideally you are working with more than 100 individual pieces of data). This tool can be used by an individual or a team to identify connections in data. An affinity map clusters themes and creates hierarchies, showing what is important to the people who have been researched and helps to determine areas of focus for ideation. It is highly visable, so a team can work with it and once it is complete, it can be a way of sharing insights with stakeholders. An affinity map can also be used to cluster and make sense of ideas in later phases.

How to use this method:

• This can be done solo, but ideally is done in a small group, including participants who have spent time understanding users. You can involve stakeholders if you want to gain their understanding of the insights.
• After you have completed your interviews, observations and engagement with users, capture each of the observations or quotes that stand out to you on a post it (one post it per insight). You can colour code the insights by persona’s or by the category of the person if you want to (ie. Current user, potential user, past user).
• Put all the post it’s onto a very large sheet of paper or whiteboard. Start to move the data into related groups, connected insights, similar quotes or observations. Give each cluster a heading so you remember what that group is about. There could be some post it’s that don’t belong to a group, they can just float around the edges.
• Once you have clustered related groups, you will see some clusters are large and others are small. Check to see if very large clusters should be divided into sub-clusters.
• Splitting into 2 – 3 in a group, read through a cluster, and summarise the insight whilst retaining the voice of the user (ie: I don’t like to fill out a survey if I never hear back about what will change”). Write these insights on a large post it and place next to or on top of the cluster. You should be able to form a hierarchy of major insights and minor insights.
• Cluster similar major and minor insights. Altogether, form up the statements that summarise the similar insights. These are your themes.
• With each theme we turn it into a question to solve. The simplest way to do this is to add How Might We to the theme. We then test to see if it makes sense and reword if necessary and then test it again to check it will solve the original challenge. Repeat for the other themes.
• Celebrate!!! We have (re)defined our challenge!

Resources:

Large paper on the wall or whiteboard, post it’s, pens and markers or an online whiteboard like Miro.

Tips from using in the field:

• This is a great tool when you have chaotic data and need team consensus on insights.
• It works well when you have over 100 pieces of data.
• Sometimes you may need a facilitator to keep momentum if there is disagreement about what cluster an insight should go in.
• It encourages new ways of thinking so embrace this.
• It is important that everyone has read and understands each data point and knows its purpose and context.

A suggestion is to colour code data – what can you see are the benefits of colour coding the data?

Post your thoughts below