Methodology

Transparency is the core of this report. To help you trust the data and understand the insights, we want to be clear about how we collect, process, and analyze the information.

1. Data Collection

This report is based on an annual survey distributed to the global Service Design community.

Source: The data is self-reported by professionals working in or with Service Design.
Distribution: The survey is shared through community newsletters, Slack groups, LinkedIn, and word-of-mouth.
Privacy: All responses are anonymous. We do not collect names or contact details linked to the salary data to ensure candor and safety.

2. Data Processing

Raw data is messy and sensitive. Before we visualize it, we perform rigorous security and cleaning steps to ensure privacy and accuracy.

We take your privacy seriously. The raw survey data is stored securely and never made public. Before any data is used in this report, it goes through a strict cleaning process where we remove all personal identifiers and strip away any open-ended text that could accidentally reveal who you are. We only publish aggregated, anonymous statistics.

Full-Time Equivalent (FTE)

To make salaries comparable across different work arrangements, we standardize income to a full-time annual equivalent.

Survey Question: We explicitly ask respondents to calculate and enter what their annual salary would be if they worked full-time (40 hours/week), even if they currently work part-time.
Assumption: We analyze the data assuming that the amount specified is this Full-Time Equivalent (FTE) figure. We do not perform additional extrapolation on the raw numbers.
Freelancers: We ask for annual income, but comparisons between freelancers and employees should always be made with caution due to the different nature of expenses and taxes.

Total Compensation Calculation

When calculating "Median Total Compensation," we sum the Base Salary and the estimated value of Additional Benefits.

Eligibility: To ensure this metric is meaningful, we calculate Total Compensation only for respondents who have provided a valid, positive Annual Base Salary.
Exclusions: Respondents who only provided a Daily Rate (common among freelancers) or whose Base Salary data is missing/zero are excluded from Total Compensation averages. This prevents data points representing "only benefits" or "zero salary" from artificially lowering the median.

Currency Conversion

This is a global report. To allow for international comparison, all salaries are converted to a common currency (USD) using the exchange rate at the time of the report's deployment. This allows us to compare purchasing power and trends globally, though local economic fluctuations can still impact the data.

We use exchange rates sourced from open.er-api.com captured at the time of the report's publication. The rates used for the current version of the report are listed below:

Outlier Removal

We remove extreme outliers—data points that are statistically improbable (e.g., a salary of $10 or $10,000,000)—to prevent them from skewing the results.

3. Analysis Principles

Median vs. Average

Throughout the report, we primarily use the Median rather than the Average (Mean).

Why? Averages can be easily skewed by a few "Service Design Billionaires" (high earners). The Median represents the "middle" person in the group, giving a more realistic representation of what a typical professional earns.

Statistical Confidence (Median 95% CI)

Where available, we provide the option to view the 95% Confidence Interval (CI) for the median. This statistical range indicates where we are 95% confident the "true" median lies for the entire population, based on our sample.

Method: We use a non-parametric method based on the binomial distribution of ranks, which is robust for skewed data like salaries.
Interpretation: Wider error bars indicate less certainty, often due to a smaller sample size ($n$) or higher variability in the data. Narrower bars indicate higher precision.

Minimum Data Thresholds & Privacy Firewall

To strictly protect anonymity while maintaining data integrity, we enforce a Privacy Firewall across the report:

Detailed Insights (Salary, Gender, etc.): We automatically mask specific groups (e.g., "Designers in Portugal") if there are fewer than 5 respondents in that year. These responses are aggregated into an "Other" category to prevent re-identification. This means you may see "gaps" for certain countries in specific years if the sample size was too small to be safely shown.
Global Overview: The "Total Respondents" chart on the Start Here page uses unmasked counts. This allows us to accurately reflect the true scale of our global community participation without exposing sensitive personal data.

4. Limitations and Considerations

While we strive for accuracy, every dataset has its blind spots. Please keep the following in mind while reading:

Diversity and Reach

Our respondents are a subset of the global community. The data is naturally biased towards those active in the specific online communities where the survey is shared (often English-speaking, Western-centric). We are actively working to expand our reach to underrepresented regions and demographics.

Self-Reported Data

The data is subjective. Questions about "Design Maturity," "Minority Status," or "Job Satisfaction" are based on the respondent's perception, not an objective audit.

The Context Gap

Data needs context. A "high" salary in one country might be a "low" salary in another due to the cost of living. Similarly, a lower salary with excellent benefits and work-life balance might be preferable to a high salary with burnout. We encourage you to look at the data through multiple lenses—don't just look at the number; look at the story behind it.

5. License & Usage

We want this data to travel.

You are free to share, copy, and adapt the charts and insights in this report for non-commercial purposes (e.g., internal team presentations, conference talks, LinkedIn posts, blogs), provided you follow these simple rules:

Attribution: You must credit "Service Design Jobs" and provide a link back to this report (report.servicedesignjobs.com).
Non-Commercial: You may not sell this data, use it for commercial market research reports, or put it behind a paywall.
ShareAlike: If you remix, transform, or build upon this material, you must distribute your contributions under the same license as the original.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

6. Disclaimer & Limit of Liability

We do our best, but we are human.

We take great care to ensure the information in this report is accurate, clean, and representative. However, we cannot exclude the possibility of errors in data collection, processing, or visualization.

No Financial or Legal Advice: The insights, salary figures, and career data provided here are for informational and community benchmarking purposes only. They should not be used as the sole basis for financial decisions, salary negotiations, or legal agreements.
"As Is" Basis: All content is provided "as is" without warranty of any kind, express or implied. Service Design Jobs and its contributors are not liable for any actions taken based on this data.

Use this report as a guide, a conversation starter, and a tool for advocacy—but always verify with your own research and local context.