Qihang Zhang and Ruijingya Tang 的作品 Talent Search 艺人发掘工具

设计师 Qihang Zhang and Ruijingya Tang 的作品 Talent Search 艺人发掘工具荣获了极具声望的 2025 A' Design Award 铁奖，竞赛类别为界面、交互与用户体验设计。

铁界面· 2025

Talent Search 艺人发掘工具

设计师： Qihang Zhang and Ruijingya Tang

客户: Chartmetric

界面类别A' Design Award 铁奖获奖者

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验证 #168884 的获奖状态

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关于 Talent Search 艺人发掘工具

Talent Search 是一款基于网页的音乐人才发掘工具。该工具帮助艺人与曲目(A&R)专业人士发现具有强劲成长潜力的新兴艺人。流媒体与社交媒体数据被转化为清晰的交互式可视化内容,包含图表、筛选器与排行榜。评价式色彩系统便于快速比较各项表现指标。Talent Search 减少了对个人人脉与主观判断的依赖。该设计旨在让人才评估更加客观、更加高效,也让新晋艺人获得更多机会。

荣誉认可

关于A' Design Award 铁奖

Talent Search 艺人发掘工具是 A' Design Award 铁奖得主，这是授予满足人们对设计精良的创作所期待的专业与工业要求的优良设计的声望认可。铁奖得主展现出其创作者的高超技艺、高度专业化、专家级的理解力和创造能力。预计Talent Search 艺人发掘工具将成为一件备受推崇、令人钦佩且被珍爱的创作，既实用又创新。这件赢得 A' Design Award 铁奖的设计整合了行业最佳实践，并拥有与专业知识和才华相结合的称职技术特性。Talent Search 艺人发掘工具因其严谨而受到尊重，旨在提供有助于让世界变得更美好的生活品质改善。

设计幕后

媒体资料包

由设计师提供的致谢、团队成员及技术详情。

独特属性: 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.
尺寸与包装: 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.
标签: Talent scouting, emerging artists, music analytics, data-driven A&R, streaming insights, social media analysis, artist discovery, music industry tool, performance metrics, data visualization
生产技术: 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.
设计挑战: 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.
研究: 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.
灵感: 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.
项目周期: 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.
操作流程: 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.
团队成员: Qihang Zhang, Ruijingya Tang
图片版权: 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