Create growth experiments to persuade teams to invest in user-requested features

How to persuade teams to invest in new features requested by users

Published in

7 min read

10 hours ago

A designer in business casual is standing next to a tripod with something drawn on it and pointing at it to try and present something. Other people around the table are looking at it somewhat.
Art by midjourney

I learned to appreciate Growth Experiments when I convinced my team to finally build a new feature users constantly requested.

In an age of tight UX budgets, I’ve found that specific types of user feedback can fall flat. Your team is probably super interested in fixing problems and usability issues, but they’re often much less interested in user feature requests.

Some of this may be due to a lack of resources or other priorities. Still, I’ve also run into another main reason: investing many resources based on qualitative research alone is hard. After all, you only tested with a few people: how do you know others will also want the same suggestion?

This is where growth experiments can help: making a case with solid evidence and data to make a user suggestion a reality. Here’s how it works.

Growth Experiments and how they can augment user research

At its core, a Growth Experiment consists of 5 steps:

  1. Analyze User/Data Insights to identify customer problems
  2. Attach problems to clear outcome metrics like Monthly Active Users (MAU)
  3. Ideate on design solutions
  4. Create hypotheses on how specific solutions can impact an outcome metric and by how much
  5. Execute scientific tests to prove/disprove hypotheses
The main steps of Growth Process. It starts with Hypotheses, then prioritizing a hypothesis, experimenting, and analysis

Growth experiments are often considered part of a separate job field called Growth Design, which I’ve previously discussed. However, there’s one place where this field often intersects with your typical Design process: User research.

When conducting user research, you don’t just hear the user’s impressions on the tasks you’ve given them; you often learn more about their motivations, job responsibilities, frustrations, and more.

Leave a comment

Your email address will not be published.