The market data, design decisions, and user feedback that shaped STREAMONOMICS.
The data sources powering the simulator's income calculations and cost-of-living baseline. Together, these datasets reveal a structural mismatch between how artists earn and what it costs to live — forming the basis of our simulation.
Spotify's annual transparency report breaks down how streaming royalties are distributed across artist tiers. The data confirms that the vast majority of streaming income goes to a small percentage of artists — reinforcing why audience size and CPM tier matter so much in the simulator.
Why this matters: Supports the streaming inequality model — most artists earn almost nothing without significant scale.
View Source →The cost-of-living baseline in the simulator is derived from MIT's Living Wage Calculator, which models the minimum income needed to cover basic expenses by city. Housing, food, transport, medical, and civic costs are all sourced from this dataset.
Why this matters: Supports the cost-of-living baseline — defines the minimum income an artist needs to survive in each city.
View Source →The Recording Industry Association of America tracks how revenue is split across streaming, live performance, and physical and digital sales. The three-stream model in Streamonomics mirrors these industry-standard categories.
Why this matters: Supports the multi-stream income model — validates that streaming, live, and merch are the three dominant revenue categories for independent artists.
View Source →The visual and UX choices that define the Streamonomics experience. These design decisions ensure users can understand complex income tradeoffs and make informed decisions.
The Streamonomics mark was designed around the concept of playability. The vertical bars reference both an audio equalizer and a bar chart — sitting at the intersection of music and data. The form is minimal and scalable, readable at any size without losing its connection to sound.
Competitor research revealed that existing tools for artists — royalty calculators, income estimators, tour planners — are almost universally overwhelming. Streamonomics was deliberately designed to do the opposite: one decision at a time, progressive disclosure, and a result that speaks for itself.
White and gold were chosen intentionally. White signals clarity and transparency — this tool shows you the real numbers, nothing hidden. Gold references monetary incentive and creative ambition: the reason any artist is asking the question in the first place.
Our data shows that sustainable careers cluster into three dominant strategies. Each persona is not just a description — it's a conclusion about what your income model actually requires to work.
Streaming requires scale and time — not short-term viability. Passive income builds slowly through catalogs and playlists. This model only works if you can survive the ramp-up period.
Live performance offers the highest income potential but requires the highest effort. Venue size and show frequency determine everything — the ceiling is high, but so is the floor.
Direct-to-fan monetization creates the strongest margins. Merch and direct products convert fan loyalty into revenue without platform intermediaries — but only if engagement is deep enough.
Four independent users tested the simulator prototype across different cities and music backgrounds. Their feedback directly shaped the current experience. Across all interviews, users struggled with abstract inputs, delayed feedback, and lack of guidance — leading to three key design changes.
"The tool has strong potential, but if it is meant to support real decision-making, the inputs need to feel more grounded in real user behavior rather than abstract sliders."
Music production student and part-time freelance producer. Understood the logic but found concepts like fan strength and audience size too abstract to set with confidence — which made the final result feel unreliable.
Takeaway: Start with a default scenario. Let users explore before they configure.
Implemented: Added pre-filled sample scenario on homepage.
"It became more engaging when I could see how venue size and number of shows affected income. That part felt intuitive and made me want to keep exploring."
Independent guitarist and part-time music teacher. The early steps felt random with no feedback on whether choices were good. Became engaged once income responded visibly to decisions in the touring section.
Takeaway: Show results sooner. Guide users toward what to try next.
Implemented: Income breakdown now updates live as users adjust sliders.
"The system tells me the result, but I want it to react to my choices. I want to feel like I am becoming a certain type of artist through the experience."
UX designer and independent singer-songwriter. Found the structure clear but too rational. The artist persona felt like a missed opportunity — it only appeared at the end instead of building progressively through the flow.
Takeaway: Build artist identity throughout. Make the system react, not just report.
Implemented: Artist persona now builds progressively through each simulation step.
"Independent musicians do not just need numbers — we need survival plans."
Freelance drummer and session musician balancing multiple income streams. Appreciated the cost-of-living connection and the survival meter. But once the gap was shown, the next step wasn't obvious — more gigs? A cheaper city? A part-time job?
Takeaway: Show the gap, then show how to close it. Recommendations over results.
Implemented: Added personalized recommendations panel to the results screen.