Improving creator discoverability when visiting Patreon’s desktop website.

UX Research



Usability Testing


For the second case study in my UX/UI Design bootcamp with Memorisely, I worked alone through a 5-week design process to identify the problem, ideate and present a solution that would improve Patreon’s creator discoverability.

Patreon is a platform that allows creators to earn income from their work by providing exclusive content and incentives to their fans (patrons).


User Interviews

AI Synthesis



Usability Testing








November 2023 (5 weeks)

The Problem

Potential patrons on Patreon have limited discoverability when searching for new creators to support. By only offering search functionality on the "Find Creators" page means users must have previous knowledge about the creator they wish to support.


Improving discoverability on Patreon’s “Find Creators” page by showcasing the diverse range on talent on the platform could increase engagement and support for a wider range of creators. An improvement in this area could lead to an increased patronage, higher creator satisfaction and overall growth of the platform’s user base and revenue.


After a short design process, I presented a solution that expanded the “Find Creators” page by displaying creator suggestions in a range of categories that individual users may be interested in. The new personalise flow enables users to set their preferences for content categories and delivery formats.


Business Goals

New User Registration Rate

Track the increase in the number of new users signing up on Patreon as a direct result of the improved discovery experience.

Engagement Metrics

Measure the engagement level of new users with the discovery features, including time spent on discovery pages, number of creators viewed per session, and frequency of return visits.

Conversion Rate

Monitor the rate at which new users become active patrons after using the discovery feature.

Creator Impact

Evaluate the increase in support for creators, especially those who previously had lower visibility, as a result of the new discovery mechanism.

User Interviews

I began my research by conducting generative user interviews with existing and non-existing users of Patreon. The aim was to understand their content preferences and discoverability expectations, as well as to discover their barriers preventing them supporting creators financially through online platforms.

I hosted three virtual one-to-one interviews via Zoom with the assistance of Otter.ia, a free service for transcribing meeting notes.


Following the interviews, I used ChatGPT to synthesis the transcripts and identify common patterns in the data, creating insight clusters for the three biggest trends.

In all three interviews, users described their experiences of “Doom scrolling” when discovering creators via social networks like Instagram and TikTok and video sharing platforms like YouTube. All of these platforms use some form of algorithmic recommendation engine for discoverability.

The key insights highlighted a need for algorithmic personalisation when recommending creators. Other key insights included prioritising quality content over popularity and offering diverse and affordable support models.

Usability Audit

The existing "Find Creators" page on Patreon has a large search field that helps users look for specific creators or topics. Beneath the search field, there are popular topics that users can select to find related search results quickly. The search results appear as a list of creator profiles without any particular order.

The Algorithm Problem

When someone signs up on Patreon as a creator, there is a message that assures users that there are no algorithms used on the platform. After researching further, I found out that Patreon's CEO, Jack Conte, spoke out publicly against social media platforms' use of algorithmic curation. He believes that this kind of recommendation engine forces creators to tailor their art to fit the algorithms, which limits their creativity.

“creators feel pressure to make stuff for the algorithm, instead of from their hearts.“

– Jack Conte, CEO of Patreon

Competitor Benchmarking

I wanted to see how competitor products approach this problem and whether they used algorithms to recommend content and profiles to other users. I identified several direct and indirect competitors and analysed the creator discoverability pages on their websites. Two such website included Ko-Fi and Just Giving.


I chose to analyse Ko-Fi as a direct competitor of Patreon, as it allows creators to setup pages where they can post content and offer incentives in exchange for donations or tips. Like Patreon, Ko-Fi's Explore page has a simple search feature, helping users find creators by name or by topics of interest. Below the search field, Ko-Fi displays featured creators as well as content highlights from a selection of topics. These features will likely lead to more discoverability of new creators, but as the curated suggestions are not based on the users preferences, I believe there could be a better solution to show suggestions that are more relevant to the user.

Just Giving

I consider JustGiving to be an indirect competitor, as the website has a similar structure whereby a fundraiser signs up to create a fundraising page and asks other users to donate to their cause. The homepage highlights current campaigns that are “Happening now” as well as displaying categories to filter search results.

Benchmarking Conclusion

Analysing the competitor's discoverability pages highlighted just how limited Patreon's "Find creators" page is. By only having a search field on the page, users must have previous knowledge about the creator they wish to support.



As I was brainstorming ways to improve this experience, I noticed an opportunity to display additional content beneath the search field. This would enable users to explore and discover new creators that they were not yet familiar with, and potentially encourage to them to support these creators on the platform.

The benefit of the enhanced page could be increased if the suggestions could be personalised to the user's own preferences. So I also considered how we might identify users preferences without using an algorithmic approach based on their previous searches or who they already follow.

What can we add?

Personalised creator suggestions

Suggest creators that users may be interested in based on their preferences.

New user onboarding survey

Gather users content preferences through a short survey that can be integrated into the existing new user onboarding flow.

What can we improve?

Category specific creator suggestions

Enhancing the "Find creators" page with profile suggestions for creators in popular categories.

Dark theme

Provide an optional dark theme for improved contrast or aesthetic preferences.


In order to personalise the creator suggestions, I needed to understand more about the user. So I created a short survey asking the following three questions.

What are your favourite topics?

The first question presents the user with a list of topics, from which they can select all that interest them.

What are your preferred content formats?

The next questions asks the user to select all content formats that they prefer from a list (e.g. podcasts, video, blogs, live streams)

Which of the listed creators are you familiar with?

The user is presented with a list of Patreon creators that match with the answers from the first two questions. The user can select multiple creators from the list.

User Flow

With these questions in mind, I created a user flow to plot out how I would integrate the survey into the existing "Find Creators" page.


Low Fidelity Wireframes

I sketched out some low fidelity wireframes to better understand the user flow through the new personalise preferences feature. This helped me identify any missing interactions before creating the final designs.

Styles & Components

As I began to work on the high-fidelity designs, I sampled colours and font styles from the Patreon website in order to maintain consistency with they're existing design system.

Colour Variables

Instead of defining colour styles like I had done in the past, I decided to try out Figma's new feature for creating colour variables. I created colours for both neutral and primary palettes, as well as for success, error, warning, and info messages. By using variables, I was able to use mode-specific semantic aliasing, where I could define a set of global colour values and then map them to equivalent aliases, specific to light and dark modes.


I identified elements in my wireframes that would be reused across multiple screens and created them as components in Figma.

High Fidelity Designs

My final designs show the user flow as they view the "Find Creators" page. The first screen shows the enhanced "Find Creators" page with suggestions for popular categories, such as Gaming and Podcasts. The blue popover modal draws attention to the preferences button beside the search field. Prompting users to find suggestions that match their interests. Clicking on this button begins the discovery survey.

During the survey, the progress indicator keeps users informed as they advance through the questions, showing that there are only three steps in the process.

Dark Theme & Size Variables

Having experimented with Figma's colour and size variables, I was able to easily create versions of my designs for different sized displays as well as a dark theme.


Usability Testing

With my prototype complete, I was now ready to begin usability testing. I used the platform Maze to create a selection of tasks and was able to get real users insights after they had interacted with my prototype.

Test Outcomes

The outcomes from the usability testing were rather mixed. Users found it useful to personalise their creator suggestions and enjoyed having them organised into categories on the “Find Creator” page. Testers responded that when choosing topics they felt overwhelmed by having too many options to choose from.

I made adjustments to the final prototype by making the topic and format button text in a heavier font weight and added a hover state to these buttons to make them easier to select.