Talent Search Artistoppdagelsesverktøy by Qihang Zhang and Ruijingya Tang

Talent Search Artistoppdagelsesverktøy by Qihang Zhang and Ruijingya Tang is a highly esteemed winner of the very prestigious 2025 A' Design Award i Jern in Grensesnitt-, interaksjons- og brukeropplevelsesdesign Competition Category.

JernGrensesnitt· 2025

Talent Search Artistoppdagelsesverktøy

by Qihang Zhang and Ruijingya Tang

Client: Chartmetric

A' Design Award i Jern Winner in Grensesnitt Category

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Verified Award Winner
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Colors

The Design

About Talent Search Artistoppdagelsesverktøy

Talent Search er et webbasert verktøy for musikktalentscouting. Verktøyet hjelper artist- og repertoarfagfolk med å finne fremadstormende artister med sterkt vekstpotensial. Data fra strømming og sosiale medier omgjøres til tydelige, interaktive visualiseringer med diagrammer, filtre og rangeringer. Et evaluerende fargesystem gjør det enkelt å raskt sammenligne ytelsesmålinger. Talent Search reduserer avhengigheten av personlige nettverk og subjektive vurderinger. Designet har som mål å gjøre talentevaluering mer objektiv, mer effektiv og mer åpen for nye artister.

The Recognition

About the A' Design Award i Jern

Talent Search Artistoppdagelsesverktøy er vinner av A' Design Award i Jern, en prestisjefylt anerkjennelse som tildeles gode design som oppfyller de profesjonelle og industrielle kravene man forventer av veldesignede skaperverk. Jernvinnere viser stor dyktighet, høy spesialisering, ekspertforståelse og kreativ kapasitet hos skaperne. Talent Search Artistoppdagelsesverktøy forventes å være et høyt ansett, beundret og verdsatt skaperverk som er praktisk og nyskapende. Dette designet, som har vunnet A' Design Award i Jern, integrerer bransjens beste praksis og har kompetente tekniske egenskaper knyttet til kunnskap og talent. Talent Search Artistoppdagelsesverktøy respekteres for sin grundighet og er utformet for å gi forbedringer i livskvalitet som bidrar til å gjøre verden til et bedre sted.

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: Talent Search streamlines music scouting by leveraging data-driven insights to identify emerging artists with strong growth potential. Unlike traditional methods reliant on network referrals, it provides quantifiable performance metrics, reducing bias and scaling global talent discovery. Its intuitive design ensures data interpretability, employing accessible visualizations and an evaluative color system to enhance decision-making for industry professionals.
Dimensions & Packaging: Talent Search runs on Chartmetric’s web-based analytics platform, utilizing a scalable backend to process large volumes of streaming and social data in real time. It employs advanced data analytics for trend detection, statistical normalization techniques for data accuracy, and an interactive front-end with dynamic filtering and visualization capabilities. Optimized for efficiency, it ensures seamless navigation through complex datasets, enabling precise and data-driven talent scouting decisions.
Tags: Talent scouting, emerging artists, music analytics, data-driven A&R, streaming insights, social media analysis, artist discovery, music industry tool, performance metrics, data visualization
Production Technology: Talent Search was developed using a user-centered design approach, integrating industry research, stakeholder interviews, and iterative prototyping. Data visualization techniques enhance interpretability, employing a diverging color scale for evaluative metrics and a sequential scale for neutral data. Built within Chartmetric’s analytics platform, it leverages advanced data analytics to process streaming and social data, delivering actionable insights that empower A&R professionals in talent scouting.
Design Challenge: The biggest challenge was translating complex music industry data into intuitive, actionable insights. A&R teams rely on multiple fragmented sources, and making diverse data—from streaming numbers to social engagement—easily interpretable required extensive iteration. Balancing usability with analytical depth was key. We overcame this by refining data visualization techniques, ensuring clarity without oversimplification, and addressing industry bias by prioritizing transparent, equitable metrics.
Research: We conducted mixed-methods research to address inefficiencies in music talent scouting. The goal was to create a data-driven tool that enhances A&R decision-making. We used stakeholder interviews, competitive analysis, and data analytics to understand user needs. Data was collected through industry consultations and Chartmetric’s database. Participants included A&R teams from record labels, streaming platforms, and management agencies.

Findings revealed that traditional scouting is subjective and network-dependent, making it hard to identify emerging artists early. Our research highlighted the need for transparent, interpretable data to support talent evaluation. Based on these insights, we designed Talent Search to present key growth metrics through interactive visualizations, enabling more informed and efficient scouting.

This innovation has reshaped talent discovery by providing objective, scalable insights that reduce bias and expand access to industry opportunities. By democratizing data, Talent Search allows promising artists—regardless of background—to gain recognition based on measurable success. It streamlines business operations for A&R professionals while fostering a more inclusive and data-driven approach to music scouting.
Inspiration: Talent Search was inspired by the need for a scalable, data-driven approach to music scouting. Traditional methods rely on industry connections and live performances, limiting accessibility and objectivity. Our design leverages advanced analytics to surface promising artists early, empowering professionals with actionable insights. Rooted in research on music industry trends and A&R workflows, it ensures clarity, efficiency, and fairness in talent evaluation.
Project Duration: The project commenced in May 2023 in California and concluded in November 2024. After an 18-month development period, the Talent Search tool was officially launched on November 7, 2024.
Operation Flow: Talent Search allows A&R professionals to discover emerging artists by analyzing streaming and social media trends. Users can explore artists through interactive charts, filters, and rankings that highlight growth potential. The tool simplifies complex data into clear insights, helping users make informed scouting decisions. By presenting key performance metrics in an accessible way, it reduces guesswork and streamlines the process of identifying promising talent efficiently.
Team Members: Qihang Zhang, Ruijingya Tang
Image Credits: Image #1: Illustrator Ruijingya Tang, Talent Search Interface, 2024
Image #2: Illustrator Qihang Zhang, Talent Search Interface, 2024
Image #3: Illustrator Ruijingya Tang, Talent Search Interface, 2024
Image #4: Illustrator Qihang Zhang, Talent Search Interface, 2024
Image #5: Illustrator Ruijingya Tang, Talent Search Interface, 2024