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 talking to users, I wanted to understand broader patterns and started discovery with a survey. In open-ended responses, 25% of participants noted that industry relevance was significant. A ranking of the "most valuable data" revealed a clear preference for: 1) years of experience, 2) skills, and 3) job titles. Respondents also reported an average confidence level of 2.7 out of 5 when it comes to salary negotiations, highlighting a need for support in this area.
It shows that job titles by themselves just don’t give enough context for designers.
Interviews
Inconsistent job titles harm salary transparency
This pattern repeated itself in interviews, with a majority pointing out that job titles don’t give enough insight into what someone does. As a result, designers don't trust the numbers they see. I had brought the idea of collecting data on designer profiles to our PM, who had been resistant to the idea and worried that gender roles might affect people's answers. After some talks, we reframed skills into responsibilities, shifting to a more objective view.
This would allow designers to better interpret the data.
Design
Turning constraints into direction
While the team now agreed that industry and responsibilities were important, we didn't collect that data yet, and the dashboard needed to be done Yesterday. For the design, this meant that data had to take a backseat, while still giving value to users.
To give as much relevance as possible, we created filters on job category and years of experience.
1
Salary Calculator for personalized data.
Overview for broad insights.
Insights for deeper synthesized data.
2
The filter system gave designers the control to explore data that matched their role and experience level — making insights personal and actionable.
3
Bell curve with salary distribution data, to give users detailed information on salaries relevant to their situation.
4
Highlights regional differences, which is important for decentralizing beyond Copenhagen.
5
Salary by company type to showcase differences in salaries and pension based on company type.
6
I anticipated that benefits presented this way would look similar regardless of filtering and not be of value to users, but let it be up to testing.
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 played with our dataset, revealing that our filtering was fragmenting the data heavily. 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 decide whether to combine the data to achieve meaningful results with multi-select options.
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
