Due to the nature of the project, we combined:

• Domain expertise (provided by the dataset owner)
  
• Behavioral assumptions  informed by existing research
  

Key behavioral insights (literature review)

• Decision fatigue increases with too many options, users struggle to compare multidimensional choices (Hick’s Laws)
  
• Users prefer guided narrowing over open exploration, especially in high-stakes or group decisions
  
• Transparency increase trust in recommendations users need to understand why something is suggested
  
• People anchor on a few key priorities, not all criteria are equally important
  

The dataset provided a unique opportunity: instead of requiring users to manually evaluate multiple resorts, we could leverage pre-scored criteria to identify the closest matches to their preferences.

Instead of returning a broad list, the system prioritizes the most relevant results, often narrowing down to a small set of top matches. This shifts the experience from: browsing and comparing  to  guided decision-making with reduced cognitive load.

User goals:

• Find a resort that satisfies the group
  
• Minimize back and forth decision
  
• Feel confident in the final decision
  
• Save and revisit options for later comparison and sharing
  

Core tasks:

• Select relevant preferences
  
• Compare shortlisted resorts
  
• Save promising options to a profile
  
• Validates the final choice (best match)
  

This experience is structured into three layers: preference input (filtering layer), results ranking (decision layer) and resort details (validation layer) to support a natural decision flow.

Each resort in  the dataset includes scores across multiple criteria, snow conditions, terrain difficulty, family friendliness, apres-ski experiences, weather, sunshine.

Approach:

• Users select relevant criteria
  
• The system filters and ranks resorts based on alignment
  
• Top matches are surfaced (rather than a long list)
  

For the MVP, reducing barriers to entry was a key priority.We chose not to require account creation upfront, allowing users to explore the experience immediately. However, optional account creation was introduced to support saving and sharing resort lists.

This balances:

• Accessibility (no friction to start)
  
• Retention (ability to revisit and compare options)
  

In parallel with the preferences input system, I designed the results and resort  detail experiences to support evaluation and decision making. A key challenge was translating multi dimensional data into a format that is easy to scan and compare. Each result card provides a brief description of the resort and includes a match score for each individual category as well as an overall match percentage. The resort profile template includes an image gallery, the scores for each category (based on the dataset), and a scrolling sidebar that displays key stats.

I also created a wireframe for an example results page and a resort profile template. Each result card provides a brief description of the resort and includes a match score for each individual category as well as an overall match percentage. The resort profile template includes an image gallery, the scores for each category (based on data provided by Pete), and a scrolling sidebar that displays key stats.

Several key refinements were made during the transition to high-fidelity:

• Prioritizing the core action
  The “How it works” section was removed from the homepage to reduce cognitive load and bring immediate focus to the preference input system. This supports faster entry into the experience.
  
• Reducing information noise Tooltips were limited to sub-categories rather than applied globally. This maintains clarity without overwhelming users with excessive microcopy, aligning with progressive disclosure principles.
  
• Improving comparability of resultsNumerical scores and letter grades were replaced with color-coded scales. This allows users to quickly assess relative performance across categories without needing to interpret rigid scoring systems.
  
• Enhancing system feedbackA checkmark indicator was introduced at the tab level to signal which categories have been configured. This provides lightweight progress tracking and supports recognition over recall.
  

The color system was built from the client’s brand palette, using a bold green for active states and structure, balanced with neutral backgrounds to maintain readability. Subtle texture was introduced through a light noise pattern to add depth without interfering with content hierarchy.

Designing for mobile required rethinking the interaction model to maintain clarity within a constrained space. While the desktop experience uses a tab-based system, the mobile version reintroduces an accordion pattern to ensure all categories remain visible and accessible without horizontal scrolling.

Special attention was given to:

• Tap target sizing and spacing to prevent input errors
  
• Maintaining clear hierarchy despite dense option sets
  
• Preserving the ability to quickly edit selections without losing context
  

The mobile experience was designed to feel closer to a native app than a responsive website, ensuring usability in real-world, on-the-go scenarios.

The product was built in Webflow in close collaboration with engineering.

During implementation, I worked directly with the developer to identify edge cases in the matching logic and ensure the integrity of the dataset was preserved across all states. This included validating how incomplete inputs, conflicting preferences, and missing data would impact results.

A full QA pass was conducted to:

• Ensure consistency across breakpoints
  
• Validate scoring and ranking outputs
  
• Identify usability friction points in real interactions
  

This phase was critical to maintaining trust in the system, as even small inconsistencies in results could undermine user confidence.

We delivered a fully functional MVP that transforms a traditionally fragmented planning process into a structured, guided decision experience.

The final product enables users to:

• Quickly narrow down a large set of options into a small number of relevant matches
  
• Understand how each option aligns with their priorities
  
• Make more confident decisions with less back-and-forth
  

By shifting from static browsing to a preference-driven matching system, the experience reduces cognitive load while increasing clarity and trust.

This project highlighted that the core challenge was not visual design alone, but designing for decision-making under complexity.Balancing flexibility with simplicity required careful prioritization of what to show, when to show it, and how to represent it. The final solution demonstrates how thoughtful interaction and visual design can work together to simplify multi-variable decisions without oversimplifying the problem itself.