Predictive Systems: Identifying High-Fit Partners
The partner discovery experience combines predictive matching with a comprehensive evaluation workspace. Beyond explaining why a partner is recommended, the platform provides audience insights, brand alignment data, industry context, and AI-generated campaign concepts that help teams research opportunities, assess fit, and make informed partnership decisions without leaving the platform.
Through this system, users can:
Discover partners aligned with marketing objectives.
Understand the factors driving each recommendation.
Research audience, brand, and market alignment.
Visualize potential campaigns before initiating a deal.
Role: Founding Product Designer and Director — defined UX strategy, platform architecture, and AI-driven workflows
Explore this Project in Figma
A guided flow through the core experience. Navigate at your own pace.
From Discovery to Confident Selection
A system for identifying and evaluating high-fit partners using predictive signals and structured comparison.
Each stage builds on the last, enabling teams to explore opportunities, assess alignment, and select the right partners with clarity and confidence.
Workflow
Partner evaluation workflow.
→ Discovery
→ Analysis
→ Evaluation
→ Selection
The Problem: No System for Partner Discovery and Evaluation
Before MutualMarkets, there was no system that allowed brands to discover and evaluate entertainment partners across television and film in a structured, data-driven way.
Partnerships were driven by industry relationships, institutional knowledge, and manual outreach, making access limited and inconsistent. Brands could not browse available opportunities, assess alignment with their audience, positioning, values, and products, or understand why a particular partnership was likely to succeed before investing time and resources into a deal.
As a result, identifying the right partners required significant research, guesswork, and coordination, making the process difficult to scale and inaccessible to many brands.
My Role
I designed the partner discovery and evaluation system that enables brands to identify, analyze, and select entertainment partners based on audience alignment, brand compatibility, marketing objectives, and campaign potential.
I defined the platform architecture and interaction model for a machine learning-powered recommendation system. The platform analyzed entertainment properties and brands across multiple dimensions, including audience demographics, behavioral patterns, brand attributes, messaging, and strategic objectives, to identify partnership opportunities with a high probability of success.
One of the key design challenges was trust. Recommendations alone were not enough. Users needed to understand why a partner was being recommended and have access to the information required to evaluate whether the opportunity was worth pursuing.
To address this, I designed a partner evaluation experience that combined explainable AI with structured research and decision-support tools. Rather than presenting a single match score, the platform exposed the factors driving each recommendation, including audience alignment, brand attributes, messaging compatibility, demographic overlap, and performance indicators.
The experience also centralized the information teams typically gathered through manual research, including audience insights, partner details, related news, advertiser activity, and AI-generated campaign concepts. This allowed users to evaluate opportunities, explore potential campaign directions, and make informed partnership decisions without leaving the platform.
The resulting experience transformed partner selection from a relationship-driven process into a structured workflow for discovering, researching, evaluating, and selecting high-fit entertainment partners.
How Ashley Identifies and Selects the Right Partners
Ashley is responsible for identifying entertainment partners that align with her brand, audience, and campaign goals. The platform gives her access to a network of partners and surfaces recommendations based on fit.
Surfaces high-fit partners based on predictive signals.
Shows why a partner is a strong match, including audience and brand alignment.
Enables quick comparison to evaluate options and prioritize effectively.
Reduces reliance on manual research and industry relationships.
Design system components supporting a scalable platform.
Components support interactive prototypes, developer handoff, and consistent implementation across the platform. They are designed for reuse across workflows, enabling faster iteration and scalable implementation.
