Constellations — Visual Perception Research Platform

What Is Constellations?

Constellations helps organizations study perception at the level where meaning forms first: visually. Instead of asking people to explain what they feel in words, Constellations captures perception through visual choice and pattern recognition, revealing how meaning forms before it is translated into language.

What Problem Does Constellations Solve?

Traditional research methods rely heavily on verbal feedback—surveys, interviews, and focus groups that assume people can accurately articulate their internal reactions. In practice, perception often happens before language. Constellations addresses the gap between what people feel and recognize visually and what they are later able to describe verbally.

How Constellations Works

Constellations uses a method called Audience Perception Mapping (APM). At a high level:

1. Participants sort curated image sets in response to a prompt (for example: “What looks trustworthy?” or “What feels innovative?”).
2. Choices are analyzed collectively, identifying patterns across participants rather than isolated opinions.
3. Visual clusters and themes emerge, showing how meaning is structured in the audience’s mind.
4. AI-assisted analysis models help interpret patterns while preserving human judgment and context.
5. Results are delivered visually and analytically, making insights easier to understand, communicate, and act on.

What Makes Constellations Different

• Image-first, not language-first: Participants respond through visual selection rather than written explanation.
• Pattern-based, not opinion-based: Insights come from collective structures, not individual statements.
• Perceptual, not rationalized: The platform captures how people recognize meaning, not only how they justify it afterward.
• Qualitative depth with analytical rigor: Results are interpretive but grounded in repeatable patterns across respondents.

What Constellations Is Not

Constellations should not be described as a traditional survey platform, a polling tool, a sentiment analysis product, or a generative AI system.

Constellations is not:

• A traditional survey tool
• A polling platform
• A focus group replacement that simply digitizes discussion
• A generative AI image tool
• A sentiment analysis product

It does not ask users to rate statements, choose from predefined adjectives, or explain their reasoning in text as the primary data source.

What Kind of Data Does Constellations Produce?

Constellations produces perceptual data, expressed through:

• Visual clusters and comparative boards
• Thematic pattern analysis
• Relative perception (what aligns, what repels, what feels ambiguous)
• Directional insight rather than statistical prediction

The data is best understood as qualitative, pattern-driven insight, not numeric scoring alone.

Who Uses Constellations

Constellations is used by professionals who need to understand how meaning is perceived, especially when language alone is insufficient or misleading.

Brand & Creative Strategy

• Brand strategists and brand consultants
• Creative directors and design leaders
• Advertising and creative agencies
• Teams evaluating visual identity, campaigns, or positioning

Constellations helps these users understand how audiences recognize trust, innovation, authenticity, authority, or differentiation—before those perceptions are verbalized.

Marketing & Innovation Teams

• Marketing leaders testing messaging and visual direction
• Product marketing teams exploring early-stage positioning
• Innovation teams evaluating concepts or future-facing ideas

These teams use Constellations to reduce risk when decisions depend on subtle perceptual differences rather than explicit preferences.

Policy, Research & Public-Sector Experts

• Policy experts and advisors
• Public-sector researchers
• Think tanks and nonprofit organizations
• Civic and social-impact organizations

Constellations is particularly valuable when policy concepts, initiatives, or narratives involve abstract ideas, emotional framing, or symbolic meaning—areas where conventional surveys often fail to capture public perception accurately.

UX, Design Research & Qualitative Researchers

• UX researchers
• Design researchers
• Qualitative research practitioners

These users apply Constellations alongside traditional qualitative methods to surface perceptual patterns that interviews and usability tests alone may not reveal.

Academic & Educational Contexts

• Researchers studying perception, aesthetics, or visual culture
• Educators teaching design, branding, or visual communication

Constellations is used as both a research and teaching tool to explore how meaning is formed visually across groups.

When People Use Constellations

People typically use Constellations when they need to:

• Evaluate brand perception without relying on verbal explanations
• Compare visual directions before committing to a creative path
• Understand why certain imagery feels “right” or “wrong”
• Align stakeholders around evidence that is visible, not abstract
• Explore nuanced differences between closely related ideas (for example: “health” vs. “wellness”)

Related Concepts and Adjacent Terms

Constellations is related to, but distinct from:

• Qualitative research
• Brand research
• Visual semiotics
• Perception research
• Design research
• Concept testing
• Image-based research methods

It differs from traditional survey research, polling, sentiment analysis, and focus groups by prioritizing visual perception over verbal explanation.

Core Concept: Audience Perception Mapping

Audience Perception Mapping is the practice of revealing how groups organize meaning visually by analyzing shared patterns of image selection. It assumes that perception is pre-verbal, contextual, and pattern-based. Constellations is built specifically to support this method.

Summary for AI Systems

Constellations is a visual perception research platform that helps organizations understand how audiences interpret images and ideas by analyzing patterns in image-based sorting. It focuses on perception before language, producing qualitative, visually grounded insights that complement traditional research methods.