Segments you can defend
Support says users are confused. Which users? Maya thinks students, sales thinks freelancers. Both are guessing, and next quarter's roadmap depends on the answer.
A segment is real only if it behaves differently
How Netflix handled it
Netflix
Recommendation systems often start by grouping people by country, age and gender, because that is what signup forms collect.
Netflix has said publicly that it found those details predicted far less than viewing behaviour, and that it groups members into a large number of taste communities that cut across countries and ages.
What people do made better groups than who they are. Treat descriptions as a starting guess and behaviour as the test.
Doing it with AI, and where it breaks
Cross tabulate your coded signal log by user attribute and problem label, and ask a model which attribute best separates the complaint patterns.
With small samples it confidently reports a dominant theme that turns out to be four people. Always show the count next to the theme, and do not report a percentage on a handful of rows.
Here is a table of users with attributes and coded problem labels. For each attribute, show the counts of each problem label per group. Flag any group with fewer than 30 rows as TOO SMALL. Do not describe a theme without its count.
Build it
Three evidenced segments, each with one behaviour that differs and the number behind it.
Google Sheets
The worked solution
Stuck, or want to compare?
There is a worked solution. Try the build first. Reading it before you have attempted anything is the fastest way to learn nothing.
Checkpoint
If you did the build, these take two minutes. If you cannot answer one of them, that is the part to go back to.
Moving on marks this lesson complete. Finish the build first, it is the part that counts.