// description
The KJ Method is a technique for making sense of large amounts of unstructured qualitative data. Participants write individual observations, ideas, or data points on cards or sticky notes, then silently group them into natural clusters based on affinity. The clusters are labelled only after grouping, which prevents premature categorisation. The method surfaces patterns and themes that structured analysis might miss.
// history
Jiro Kawakita, a Japanese anthropologist, developed the method in the 1960s as a way to organise field research data from his expeditions in Nepal and other regions. He described it in his 1967 book Hassou-hou. The technique was adopted by Japanese industry within quality management circles and spread internationally under the name "affinity diagram" as part of the Seven Management and Planning Tools.
// example
A KDP publisher has collected 90 customer review fragments (copied from Amazon reviews across their niche). She writes each fragment on a sticky note and clusters them silently. Six natural clusters emerge: "easy to use daily," "helps me stay consistent," "wish it had more space," "love the design," "started me on a habit," and "bought as a gift." The gift cluster is unexpected and large — she hadn't marketed the product as a gift, but 20% of reviewers mention gifting. This reveals an entire untapped marketing angle she immediately adds to her listings.
// katharyne's take
This is one of the best tools for mining customer reviews — your own and your competitors'. Collect 50-100 review fragments from Amazon or Etsy, paste them into a doc or print them out, and cluster them by theme. The patterns you find in 30 minutes of clustering will tell you more about what your market actually values than any amount of market research tool output. Pay special attention to the clusters you didn't expect — those are your hidden positioning opportunities. ChatGPT can help with the initial clustering if you paste in the reviews and ask it to group by theme.
// creative uses
- Copy 50-100 Amazon reviews from your top three competitors in your KDP niche into ChatGPT and prompt: "Group these review fragments into themes based on what buyers mention. Label each cluster and count how many reviews fall in it." The output is an instant affinity diagram. The largest unexpected cluster is your next product opportunity or marketing angle.
- Use the KJ Method after a course cohort ends: copy all the messages, emails, and community posts your students wrote during the course onto sticky notes (or a Miro board). Cluster silently. The themes that emerge tell you what the course actually delivered vs. what you thought you were teaching — and what module to add next.
- Apply it to your inbox: copy the last 50 customer questions you've received via Etsy messages, email, or social DMs onto sticky notes and cluster them. The largest cluster of repetitive questions is either a product design problem (fix the product) or a listing clarity problem (fix the copy). Both fixes are worth making before answering the same question a 51st time.
// quick actions
- Go to Amazon right now and read the 3-star reviews for the top three books in your KDP niche. Copy each review's key complaint into a separate line in a Google Doc. When you have 20+ lines, look for clusters. The complaints that cluster are market gaps your next book can address — far more valuable than optimising against 5-star reviews.
- Use Miro or FigJam (both free) to run a digital affinity diagram on your next research project: paste each data point as a sticky note and drag them into groups. The visual layout makes clusters obvious in ways that a spreadsheet of themes never does.
- Run a KJ session on your own course or product ideas: write each idea on a separate sticky note, then cluster them by "who they serve" rather than "what they are." The clusters reveal product lines and audience segments you were unconsciously already developing — and which ideas don't fit any cluster (a clear signal to deprioritise them).
// prompt ideas
I'm going to paste [number] customer reviews from Amazon/Etsy for [your niche or product type]. Please group them into affinity clusters by the main theme each review expresses. Label each cluster, list how many reviews fall in it, and flag any cluster that surprises you — those are the hidden positioning opportunities I want to explore first.
Here are the questions and messages I've received from customers about [your digital product or KDP book] over the past [time period]. Cluster them by theme using the KJ Method logic — group by natural affinity, label clusters after grouping, and tell me which cluster is largest. I want to know whether this is a product problem or a listing clarity problem.
I have a list of [number] product or content ideas for [your niche]. Apply affinity clustering to group them by the underlying audience need they serve rather than by format or topic. Show me the clusters, identify which cluster has the most momentum, and flag any ideas that don't fit — those are candidates to cut.