Gumbel - Music Demand Data Dashboard
Connect AI Demand Data to Decisions
UX Architecture
Design System
(Sep-Dec. 2023)
About
Gumbel Demand Acceleration (GDA) is a demand analysis and decision support application, offering insights to assist music workers in effectively marketing their works.
My Role
I independently led design from inception, translate design potentials into prototypes. Collaborating closely with project lead Kobi, I focused on identifying user objectives, establishing the MVP, and facilitating communication through iterative prototyping. I also initiated the Design System documentation based on the brand language.
Challenges
The original product concept, rooted in a technical perspective, centered on "what we provide" rather than addressing "what users need." This misalignment with user requirements posed challenges in empathizing and prioritizing information within the extensive data set. My strategy involved stepping back to concentrate on identifying user objectives and charting user flows, while also considering our technological advantages over competitors.
Problem
Users who lack expertise in professional music marketing and B2B product experience were seeking straightforward insights from extensive data.
Solutions
Connect data and reasonings to decisions:
- • Insight Summary Page tailored to predefined objectives
- • User flow aligned to the decision-making process
1. Insight Summary Page tailored to predefined objectives
2. User flow aligned to the decision-making process
1. Who are the users?
The target users range from expert music directors to independent artists, each with different marketing skills. They vary in their understanding of the best platforms for releasing specific music types and the right amount to invest in marketing across channels. The aim is to enhance music demand data with expert advice, assisting them in making more informed marketing decisions. Additionally, as user familiarity with interpreting data dashboards varies, ensuring the design is straightforward and easily understandable is essential.
2. What technology used?
In addition to identifying user needs for our product, I engaged in multiple discussions with the project lead to grasp the technological aspects. This helped me comprehend our product's capabilities and the unique advantages we offer over competitors.
3. The Mental Model for decision-making
Drawing from my understanding of both user needs and technological capabilities, I developed an information pyramid to illustrate the journey from data processing to decision-making. I communicated it with designs at various stages with stakeholders for iterative adjustments and alignment.
How might we design a music data dashboard to help users easily extract meaningful insights from large-scale professional data to better monitor and market their music?
Working closely with the Project Lead, I developed a flow chart guided by typical user queries, ranging from initial data observation to the decision-making process.
The data presented to users falls into two categories, aligned with their objectives: (1) Analyzing music performance to inform marketing decisions, and (2) Understanding listener demographics to guide music edits pre-launch or to inspire future production. Consequently, the data is divided into two main types: demand data and listener demographic data.
Discussion 1: How to place different data categories
* Click the images to read the details
Final: B + The Summary Page
* Click the images to read the details
Considering that the user paths for the two goals are typically distinct with minimal overlap, users may not need to view both data types simultaneously. Rather than crowding a single page, which could compromise space for valuable insights, we opted to separate them into individual tab pages.
Additionally, to help users focus on essential information, we introduced a 'Summary' page, offering a quick and concentrated overview of the key data.
The next challenge is to enable users to gradually delve deeper from the surface of the data, extracting highlights and combining our industry expertise with AI predictions to gain insightful guidance.
Discussion 2: Calling attention on the Insights
* Click the images to read the details
Final: B + The Summary Page
Based on the mental model, we categorize information into three parts: data highlights, insights on the data, and action suggestions. Research indicates users trust suggestions more when paired with clear data. Our approach: users first see key data in graph titles, then click for more insights, and finally, get action suggestions if applicable.
While developing the prototype, I also started building the design system from scratch. Drawing from our brand's language, I crafted and standardized elements like colors, text, icons, layouts, and various components to ensure a cohesive design.
Next Steps
Due to time constraints, we couldn't test and validate the design thoroughly. Although I completed a design that highlights our technology's key strengths and meets presumed user goals, I wish we had more time for further refinements in:
Customizing the content on the Guidance page for each pre-selected user goal.
Prioritizing the information users most want to see, balancing between straightforwardness and understandability
Enhancing how users interact with filters and its impact on the presentation of data, highlights, and insights
Leading the design on my own was challenging, particularly as I was initially unfamiliar with the industry and technology. Through this experience, I enhanced my communication skills, using prototypes to engage with users and stakeholders when requirements were unclear.
Additionally, I challenged myself visually by creating the design system from scratch, ensuring the documentation was thorough and useful for future applications.
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