The teams that stay most focused on their users win.

It’s easy to get off-track and start building the wrong product or selling the wrong ideas if you’re not constantly talking to your users and refreshing your understanding of what they need.

As you get bigger, it gets harder and harder to conduct productive user interviews. Talking to other human beings is a time-consuming process. It’s messy and often low-signal.

An effective UX research workflow amplifies that signal so the team can get insights faster.

The problem with your user interview workflow

Learning from user interviews is always an intuitive process.

As you talk to people, you begin to notice clusters of correlations between people and their usage of the product. You notice that a lot of feature X’s usage is from people with the title “director of marketing.” You notice that companies of 100 people or fewer tend to have trouble justifying the price point of your Pro plan.

You take these insights and plot them out, eventually forming groups of users with distinct needs, buying processes, or price sensitivities:

These personas are supposed to be used for everything from business development to sales to product management.

Too often, however, your user personas end up in a Google Doc that doesn’t get updated for months. It lays fallow while your team builds the product, solves problems, and readjusts. Startups often just don’t have the time to constantly redo the process of data collection and analysis that went into their personas the first time.

Many have even raised the question of whether user personas, as an idea, are simply dead. There are some big problems with them (or at least the way they’re commonly thought of today):

People are multidimensional, not schematic: You don’t get nuance when you’re defining people according to their title and salary.

You don’t get nuance when you’re defining people according to their title and salary. Personas are static and fragile: Changes in your market and changes in your product will throw your user personas off track. Keeping them updated is a significant logistics and data challenge.

Changes in your market and changes in your product will throw your user personas off track. Keeping them updated is a significant logistics and data challenge. Too hard to explore data, too easy to introduce bias: You rely mainly on your instincts to figure out how people fit together. That introduces a huge opportunity for confirmation bias to seep into the process.

That said, the solution is not to ditch personas, but to flip around the process that produces them.

The user taxonomy

Getting productive information out of your user interviews starts with setting up a user taxonomy.

Before you fly a plane, you check all the instruments and make sure that everything is working properly. You need to know that while you’re in the air you’re going to be collecting all the information you need, and that all the feedback you receive from your dashboard will be accurate.

Here, you should be instrumenting your workflow so that you’re getting the maximum amount of useful data from each interaction with a user.

The same way Linnaean taxonomy lets us classify flora and fauna, a user taxonomy lets us classify the users we talk to according to demographic and behavioral attributes like:

age : how old they are

: how old they are income : how much money they earn per year, estimated

: how much money they earn per year, estimated products purchased: what products/services they’ve purchased in the past

This involves equal parts art and science — you need to know your product and your market in order to pick characteristics properly.

Here’s an example of the potential metadata around a user:

A quick but important note on UX ethics: please only use and store metadata that the users have explicitly given you permission to use, in this context, and make sure that you’re in compliance with any privacy laws that are applicable to your region.

As you do, you progressively build a profile of who your customers are:

You can capture this type of information in a database like Airtable: