Designed music investment SaaS for Europe fastest-growing startup
Utopia Music is a Swiss Music fintech company levelling up the Music Industry through clear, transparent, and reliable data-based solutions. We support the whole Industry to make more money, faster, with less costs, and less errors.
800 Music Tech professionals; Offices in 10 countries; 213BN consumption data points, collected over 6 years from 100K+ data feeds; 10 patents pending; 6M+ copyrights represented.
Products: Utopia Investor Services; Utopia Design System
Type: B2B, Music Tech, Investment Intelligence, SaaS
Role: Senior Product Designer
Duration: 2021 – 2023
Expertises: Design Leadership, Product Design, Design System, User Research
Outcomes: Commercialised Utopia ForeCast ™ ; Matured and stable Utopia DS 1.0
Intro
I joined Utopia Music in summer 2021 with very little prior knowledge about the music industry. I was surprised by how this massive industry was practically controlled by a few big label companies, and how outdated processes still existed in every aspect of music creation, royalty collection, music publishing, and rights investment. There are practically limitless issues that could be "fixed" and improved with modern technology.
Realizing that we were trailblazers in this new yet old territory, I was determined to tackle the problems for Utopia Music's customers and Utopia's internal infrastructure. Considering my previous experience in the financial sector and design systems, my natural choice at Utopia Music was to become the Design Lead in Utopia Music Investor Services and the Utopia Design System Owner. Taking on two roles at the same time seemed like a daunting task, but I found it very valuable since we were trying to build an airplane while flying it. The Design System helped us build many products at scale, and each product contributed and provided feedback for the Design System. Understanding the requirements and constraints of both products benefited me greatly.
Regarding team setup, the Investor Services team had a dedicated team of project managers, designers, engineers, data scientists, and UX researchers. Meanwhile, the Design System was run on a "virtual team" setup where designers and developers from different product teams contributed and were centered around core members who coordinated the group effort.
Problems & Research
Investing in music rights is a lucrative and stable market where investment funds and big labels spend billions of dollars each year. Although the volumes and values of purchases are very high, there has been very little innovation or automation in the process. Sellers, also known as artists, are required to provide prospective buyers with relevant income spreadsheets. Then, the buyer must manually match this financial data with play count data in order to produce a "semi-reliable" report of the catalog's performance. Ultimately, investment decisions are made based on this report. The whole process often takes weeks and involves local collection societies, lawyers, and accountants. However, the final reports are often not 100% foolproof or updatable.
Our solution to this industry problem was a fully automated process for matching data, generating a better report, and enhancing the report with new capabilities based on big data and AI-trained models for predictive reporting. We harnessed the power of the Utopia platform, which scanned all channels (radio, TV, digital streaming) to identify songs and count their play times. Armed with this information, songs in the target catalog could be automatically matched with income data with superior accuracy. Moreover, we did not require stakeholders to put in extra effort to prepare their data. Our proprietary engine was capable of scanning spreadsheet files, extracting necessary data, and matching it with play count information as mentioned earlier.
Although we had solid plans and the required capabilities, there were a few obstacles in the design and development of this product.
A really small group of target customers: globally, there are around 50-60 firms actively playing in this field. Due to the sensitive nature of purchases and customer identity, we couldn't employ large user research or test the product on the mass market.
Market competition for data: there are many "smaller" competitors in the market who also provide slightly different data sets in terms of accuracy and availability.
Unique features and additional values: creating the data model and training the AI for predictive reporting was not cheap or easy.
Utopia ForeCast™ Design & Development
Development works started after all groundworks researches had been done. Various design directions were considered for the file uploader which were very complex in implementation. In the end, we decided to go with the simplest design where most of heavy lifting were tucked under-the-hood. It meant the tool need to be smart enough to in order to scan and match automatically all consumption data and income data. Due to fragmented and unstandardised nature of the files, we also needed to invent a robust mechanism for manual data correction.
Fortunately there was always silver lining in all obstacles, Utopia Platform project was kicked started around the same time. Our product was able to utilise Utopia Platform in accessing vast copy right database, from which we could compare and match with imported data. Utopia ForeCast and Utopia Platform both helped each other to be more mature in data accuracy and coverage.
Last but not least, all of the work would be in vain if our product which were the final reports were not well received. We were working tirelessly with Utopia Data Visualisation team to create clear and pragmatic graphs, charts, tables, etc. All reports types were compliant to web responsive standard, as well as exportable in various formats. Vigorous usability tests with clients were carried out to produce best-in-class reports.
Here are couple of screenshots of our final product.
Utopia Design System
Utopia DS was built at the same time with Utopia Investor Services and the rest of our product portfolio. Therefore, it was tricky to keep Investor Services UI be in line with Utopia design principles and coherent with other products.
Design system core team consisted of 4 members who were lead designers, front-end developers and managers. This core team supported big group of 7 product teams with total of 50 designers and developers. While main principles and guidelines were provided by core team, all UI components were created for and by each product team. The core team were coordinating requests, commits as well as feedbacks via routine sync-up, slack channel and Github repository.
With this approach, we had managed to grow the number of design token and UI component to 100 and 300 respectively. More than 1000 merge requests in our repository were handled. Thanks to our dynamic yet robust system, visual consistency was maintained across all different products while keeping development cost down.
Utopia ForeCast was launched after 9 months of development, utilizing over 200 data points, reduced turnaround time from weeks to hours.
Utopia DS was being used by 7 product teams, contributed to by over 50 designers and developers, with 100+ design tokens, 300+ react UI components, 1000+ merge requests.