Hedgehog - Prediction Market Product

Hedgehog is an on-chain prediction product designed to transform complex blockchain signals into fast, repeatable decision loops. Instead of relying on real-world events or traditional trading mechanics, the product focuses on on-chain metrics (such as funding rates, fees, and block activity), allowing users to participate in short, time-based prediction rounds.

I led the product design, structuring both the system logic and the user experience, from interaction flows to interface hierarchy, ensuring the product could translate complex mechanics into a simple and intuitive experience.

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Through benchmarking existing platforms, a few issues became clear:

• Interfaces rely heavily on orderbooks and trading concepts
• High cognitive load for non-expert users
• Slow feedback loops tied to external events
• Lack of continuous engagement cycles
• Weak connection between user actions and system behavior

As a result, participation is either limited to experienced users or becomes too passive.

• Reduces participation to a simple decision
• Operates in fast, repeatable cycles
• Makes on-chain data actionable
• Aligns user incentives through clear mechanics
• Encourages continuous engagement

To address these challenges, I designed a time-based, pool-driven prediction system that removes the need for orderbooks and simplifies participation into a single interaction.

Instead of matching users, the system operates through shared pools (UP vs DOWN), where users deposit into a position within a defined time window.

At resolution:

• The losing side is redistributed to the winners
• Rewards are proportional to position size and timing

From a product design perspective, this required:

• Translating financial mechanics into a clear mental model
• Structuring the interface around a dominant action
• Making system states (prediction → waiting → resolution) explicit
• Designing feedback loops that reinforce participation

Product Decisions

• Removed orderbook mechanics to reduce cognitive load and enable faster participation
• Introduced pooled positions (UP vs DOWN) to simplify the economic model
• Prioritized time-based rounds to create repeatable engagement cycles
• Designed early-entry advantage to incentivize faster decision-making

• Waitlist Conversion Rate: 35% (above B2B SaaS benchmarks)
• CTA Engagement: consistent interaction across hero and mid-page sections
• Scroll Depth: high retention through trust and system explanation sections
• User Behavior: clear pattern of qualified interest and early adoption intent

These signals validated both the product positioning and the clarity of its value proposition.

Behavior Signals

• Users consistently interacted before round closure, indicating time-driven urgency
• Higher engagement observed in rounds with visible participation imbalance
• Repeat interactions suggest early formation of participation loops

Key insights:

• Users struggle with trading mechanics (orderbooks, liquidity, spreads)
• Decision-making is slowed by excessive information
• Most systems lack clear feedback loops
• Engagement depends on long, unpredictable timelines

This informed a key direction:

• Simplify interaction
• Shorten cycles
• Make outcomes immediate and visible

Key principles:

• Strong contrast to highlight primary actions
• Minimalist layout to reduce noise
• Consistent spacing and grid for readability
• Emphasis on numbers and states over decoration

The interface prioritizes function over visual complexity, aligning with the product’s goal of simplifying decision-making.

• Simplifying interaction often requires redesigning the system, not just the UI
• Clear feedback loops are critical for engagement
• Time-based mechanics significantly improve user retention
• Showing behavior (not just data) improves decision-making

• Expand available prediction markets (more on-chain metrics)
• Introduce advanced analytics for experienced users
• Refine onboarding for first-time participants
• Explore social and competitive mechanics