Brand–Artist Matching:
From Idea to Working System

A team with market insight and a technical gap

A small team came together to address a real market problem: matching brands with suitable music artists for campaigns relies heavily on intuition and lacks transparency, resulting in missed opportunities, even with substantial budgets.

The team brought deep domain expertise, including artist management experience, established relationships with artists and brands, firsthand knowledge of process challenges, and product and frontend engineering skills. However, they lacked the research depth and backend technical infrastructure to evaluate viable matching logic, build the data layer, and clearly present the system to investors. With a research background that includes a PhD in music recommendation, I was a natural fit to join the founding team.

The team had a vision and industry knowledge, while I provided the technical research foundation and designed the system architecture.

A complex matching problem

Deriving a data-driven, algorithmic method to match artists with brand campaigns is a complex problem. Solving it requires understanding the industry's intuitive decision-making process and identifying measurable data signals. Quantitative factors such as audience location, age, and online presence are easy to compare and align with campaign needs, but provide only a partial view. A complete assessment must also include qualitative factors such as style, reputation, and values, which cannot be directly measured.

QuantitativeAudience demographics

Age, gender, geography, and audience reach across streaming and social platforms. Data collected from publicly available APIs.

QualitativeBrand tone compatibility

Alignment between an artist's persona and the emotional tone associated with the brand.

QualitativeVisual language

Color palette, styling, aesthetics. Is the artist's output coherent with the brand's identity?

QualitativeCultural credibility

Is the brand artist pairing culturally aligned, or is it just a business deal? Audiences notice the difference.

QualitativeLong-term positioning

Where is the artist's career heading? Strong partnerships are built on alignment that holds over time.

QuantitativeAudience interest overlap

Do the artist's fans show strong interest in the brand's category and related brands?

Signals also conflict, and none are decisive on their own:

• An artist matches geography perfectly, but fails on cultural tone.
• Aesthetic alignment is strong, but the audience isn't large enough to meet the campaign goals.
• Quantitative signals look excellent, but career trajectory carries reputational risk.

The first challenge was to determine which factors to include, how to compare quantitative and qualitative signals, and how to explain the reasoning behind a match.

Turning market insight into a functional system

My role was to bring the research depth and backend technical capability the team needed. This involved five key areas of work.

Feasibility & prototype scope

Assessed what was realistic for the prototype given the available data sources and technical constraints. We chose to build around a single, data-rich API and one qualitative dimension — artist 'vibes'. I designed a lightweight method to determine and incorporate vibes into the matching logic.

System architecture & backend build

Designed and built the backend architecture, including a data layer to normalize signals from the API, a matching layer for filtering and ranking compatibility dimensions, as well as the database, data collection pipeline, and deployment infrastructure.

Matching logic

Developed the core matching and ranking logic, drawing on music recommendation research to define the key factors of brand-artist compatibility and weight them according to campaign goals. The results are transparent and easy to interpret, enabling brand managers to understand and explain recommendations with confidence, rather than relying on an opaque scoring system.

Data visualization

Designed the first set of visualizations to help users understand the reasoning behind compatibility scores. The goal was to break out each relevant signal and provide clear explanations, so managers can easily see and assess the trade-offs involved in each recommendation.

Technical documentation

Authored the technical white paper for non-technical investors, explaining the system's architecture and data-driven approach to matching brands and artists.

Matching system design

The diagram below illustrates the system design, divided into stages, beginning with data collection and resulting in an explainable, ranked list of artist recommendations. The current prototype demonstrates the platform operating on a simplified matching algorithm with a single data source, while future development will incorporate multiple data sources and more sophisticated recommendation methods.

Where the work stands

This project demonstrates how careful system design can begin to transform what has traditionally been an intuitive, opaque process into one that is transparent and driven by real-world data. Even at the prototype stage, the core matching logic shows that brand-artist compatibility can be assessed in a structured, explainable way, giving both brand managers and artists a clearer basis for partnership decisions.

The pattern behind the work

What this work required

• Applying research expertise to a practical challenge. Building this system required an understanding of recommendation system subtleties, beyond technical skills. It also required the judgment to identify which signals are relevant and to design matching logic that is robust and transparent.
• Making complex logic accessible for different stakeholders. The matching framework needed to be understandable for engineers, practical for brand managers, and credible for investors.
• Translating qualitative concepts into data. I developed the approach to identify and integrate a key qualitative factor, artist vibes, into the matching logic, transforming an abstract idea into a functional element of the system.
• Writing technical documentation for investors. I authored a white paper that explained the system's design and rationale, demonstrating our thorough planning and domain expertise to investors in order to strengthen our case for funding.