End-to-end process
B2C
0-1
2025
Empowering designers in negotiating salaries to increase memberships
UX Unite has released a job market report in a PDF format for the past two years. Now, they want to create an interactive dashboard on their website to provide designers with updated insights into job market data and hope to turn more website visitors into community members.
Challenge
Designers lack relevant salary data
There is no reliable data in the UX field in Denmark and as a result, many designers are underpaid and face challenges in securing fair compensation as experienced by many of UX Unite's members. The company struggles to find the workforce to generate yearly reports and chasing individuals to fill out salary surveys.
By addressing this gap, designers will be empowered with data that supports fair negotiations and helps them advocate for themselves confidently.
Survey
Skills say more than job titles
Before interviews, I aimed to identify broader patterns and surveyed with the user group: designers. In open-ended responses, 25% noted that industry relevance was significant to them. They ranked the "most valuable data" as: 1) years of experience, 2) skills, and 3) job titles. They also reported an average confidence level of 2.7 out of 5 in negotiating, highlighting the need for support.
The survey indicated that job titles alone lack sufficient context for designers.
Interviews
Inconsistent job titles harm salary transparency
In interviews, many designers expressed that job titles often lack insight into an individual’s actual responsibilities. As a result, they tend to distrust existing salary data. I proposed the idea of collecting data on designer profiles to our Project Manager, who initially resisted it, concerned that gender roles might influence self-assessment. After some discussions, we reframed our approach by focusing on responsibilities rather than skills.
This shift would enable designers to assess themselves objectively while still adding context for others using the data.
Design
Turning constraints into direction
While we were now on the same page about which data to include, the dashboard needed to be "done Yesterday". Since "responsibilities" and "industry" were new data points that we hadn't collected yet, they would have to be set aside for now. We needed to emphasize the data that was already available while making room for the new data points to be implemented smoothly when they are ready.
To give as much relevance as possible, we created filters on "job category" and "years of experience".
Making data easy to understand, fast.
Our focus was on making the data fast and easy to understand. I refined the cards through continuous collaborations with the team. Our data analyst had concerns about the interpretation of the pension in percentages stacked together with the base salary.
After experimenting with variations, I opted for vertical bars with pension in a separate column, which allows for a clear and quick overview, as well as better accessibility.
Reality checking design with actual data
To test how the design would perform with real data, I experimented with our dataset, revealing that our filtering was heavily fragmenting the data. I proposed merging some job categories, since we had learned that designers don't focus heavily on them. There was concern that some users might not feel represented by the titles.
Instead, we allowed designers to choose whether to combine the data and achieve meaningful results with multi-select options.
The bell curve with filters "UX Designer" + "1 Year"
Giving context to combined data categories
The multi-select functionality created a new challenge – how do we make sure designers understand the results of combined categories? I created a data pool section giving designers insight into the composition of the filtered data. I decided to add responsibility data here, not completely sure how to shape it.
Usability testing
The bell curve isn't ringing any bells
For the usability testing, I was especially curious of designers reaction to multi-selection in the filters and their understanding of the bell curve. The bell curve confused all except one designer, with many getting confused or misinterpreting the chart. The data pool had mixed results; some didn’t notice it, and some had difficulties figuring out what to expect before opening the card.
The majority were happy using the multi-select-option while a few didn't notice it as an option.
Iteration
Simplifying salary range achieving 100% clarity
While I considered adding informative labels to the bell curve, I changed it completely. Instead, I created one simple bar that would show distribution, highlighting the 75% range, median, and lowest + highest salaries, giving the users all the information they were looking for, making the assisting cards obsolete.
I also added the data number to the data pool information, giving designers an idea of the validity of the results.
Retrospective
