Hedgehog - Prediction Market Product

Hedgehog is an on-chain prediction product that transforms 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, funding rates, fees, block activity, allowing users to participate in short, time-based prediction rounds.

I led the product design end-to-end: owning the system logic, information architecture, interaction flows, design system, and interface delivery across desktop and mobile. My primary challenge was translating mechanics that are inherently technical and abstract into an experience that feels immediate, legible, and engaging, even for users without a trading background.

This case documents not just what was built, but why: the research behind the decisions, the paths that were considered and discarded, and what we learned along the way.

The crypto prediction market space has been growing, Polymarket, Augur, and various DeFi protocols have proven the concept. But engagement is consistently limited to experienced users: people who understand orderbooks, liquidity mechanics, and on-chain concepts.

The fundamental problem isn't that people don't want to predict. It's that every existing platform asks users to understand the system before they can engage with it.

Current landscape problems:

• Interfaces rely heavily on orderbooks and trading concepts that most users never learned
• Decision-making is slowed by excessive information density, charts, spreads, order queues
• Feedback loops are long and tied to external events (elections, sports, price targets)
• Engagement depends on unpredictable timelines, a prediction made today may resolve in weeks
• Weak connection between what the user does and what the system does in response

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

On-chain data moves continuously and is verifiable by anyone. Funding rates, transaction fees, block activity, these signals fluctuate in real time and are not tied to external events that require waiting days or weeks for resolution.

The opportunity: build a prediction product around on-chain signals, with short time windows, so participation is simple, cycles are fast, and outcomes are immediate.

This wasn't about building a simpler version of an existing product. It was about rethinking what participation in a prediction market could feel like if it was designed for engagement rather than financial sophistication.

This was the design challenge. Not just 'simplify the UI,' but redesign the participation model itself so that the mechanics are learnable in seconds and the experience feels rewarding from the very first round.

Main pains to solve:

• High cognitive load for non-expert users, too many concepts required before first participation
• Slow feedback loops, existing platforms offer no immediate gratification cycle
• Lack of continuous engagement, users drop off after one or two experiences
• Weak mental model, users can't easily understand how the system works or why they won/lost
• No clear dominant action, interfaces offer too many choices at the moment of decision

The product needed to serve two distinct user profiles simultaneously, and the tension between them shaped most of the design decisions.

User Type 1 — Curious newcomer

• Interested in crypto but not a trader
• Has heard of prediction markets but never participated
• Needs: immediate understanding of what to do, low risk of 'doing it wrong,' fast feedback
• Drops off if the first experience requires more than 30 seconds of orientation

User Type 2 — Experienced DeFi participant

• Understands on-chain mechanics, funding rates, liquidity
• Looking for fast engagement cycles, not long-term position management
• Needs: visible participation data, position imbalance signals, clear pool mechanics
• Will disengage if the product feels too simplified or lacks depth

''The design challenge: make it simple enough for the newcomer without feeling shallow to the experienced user. The solution was progressive disclosure, simple surface, accessible depth.''

The research approach combined competitive analysis, behavioral benchmarking, and pattern analysis from analogous products, focusing on understanding why existing prediction and trading interfaces fail to scale engagement beyond experienced users.

Competitive benchmarking, 6 platforms analyzed:

• Polymarket — strong event coverage, but interface requires familiarity with orderbooks and liquidity pools
• Augur — decentralized and powerful, but onboarding friction is high; participation requires multiple steps before first prediction
• Drift Protocol — excellent for experienced DeFi users, but information density is overwhelming for newcomers
• Binance Predictions — closest to the fast-cycle model we were targeting; studied for engagement pattern reference
• Azuro — good sportsbook UX but tied to real-world events with long resolution timelines
• Limitless — interesting mechanics but unclear dominant action at the moment of participation

Key insights extracted:

• Insight 1 :Users struggle with trading mechanics, orderbooks, liquidity, spreads. Not because they're unintelligent, but because these systems were built for financial professionals.
• Insight 2 :Decision-making is slowed by excessive information. The more options and data visible at the moment of decision, the longer users hesitate, and the higher the drop-off.
• Insight 3 :Most systems lack clear feedback loops. Users don't understand what happened after their prediction resolves, win/loss communication is buried or delayed.
• Insight 4 :Participation data is a decision signal. Users naturally look at what other participants are doing. Most platforms hide this or present it in a way that requires interpretation.
• Insight 5 :Short cycles create habit. Products with fast, repeatable engagement loops (think: daily games, streaks, immediate outcomes) show significantly higher retention than those with long-term prediction timelines

These insights converged on a clear direction: reduce participation to one decision, shorten the cycle, make outcomes immediate, and expose behavior (not just data) as a decision signal.

The design process moved through four phases: IA definition, interaction design, visual system, and iterative testing. Below are the five most significant design decisions, including the alternatives that were considered and discarded.

Decision 1 — Pooled system (UP/DOWN) vs. orderbook

• Considered & discarded: Simplified orderbook: we initially explored a version that kept the orderbook mechanic but reduced the visible information. Testing with 3 non-technical users showed the same hesitation patterns — the mental model of 'matching' trades still required too much prior knowledge.
• Chosen direction: Pooled positions (UP vs DOWN): users deposit into a shared pool, not against a counterparty. The only decision is direction
• Why: Task completion rate in usability sessions went from ~40% (orderbook v1) to 88% (pool v2). The mental model shift, from 'trading' to 'choosing a side', was the key unlock.

Decision 2 — Time-based rounds vs. continuous market

• Considered & discarded: Continuous market: always-open participation with no defined resolution window. This felt more like traditional trading and created no urgency or clear engagement cycle.
• Chosen direction: Time-based rounds with defined entry, waiting, and resolution phases
• Why: Short cycles create repeatable engagement loops. Users who participated in 3+ rounds had a significantly higher return rate. The defined end state also solved a core UX problem: users always knew exactly where they were in the process.

Decision 3 — Showing participation data publicly

• Considered & discarded: Hiding pool distribution: keeping the UP/DOWN split private to avoid herd behavior and maintain 'market integrity.'
• Chosen direction: Exposing participation data, pool size, capital distribution, top participants, as a first-class UI element.
• Why:Research showed users naturally look for social proof at the moment of decision. Instead of fighting this behavior, we designed around it. Showing the imbalance also creates strategic value for experienced users who can read positioning as a contrarian signal.

Decision 4 — Single dominant action vs. multi-step flow

• Considered & discarded: A confirmation flow with amount selection, direction, and confirm step before entry, modeled on standard DeFi transaction patterns.
• Chosen direction: One-tap participation: the dominant action (UP or DOWN) is the primary element of the entire screen. Amount and confirmation are secondary.
• Why: In sessions with new users, every additional step before the first participation increased drop-off by ~15%. The goal was to get users to their first completed round as fast as possible, because completion is the strongest retention signal.

Decision 5 — Resolution feedback design

• Considered & discarded: Standard modal notification: 'You won X' with a close button.
• Chosen direction: Full-screen resolution state with win/loss visual, payout amount, and an immediate 'Play again' CTA that re-enters the cycle.
• Why: The resolution phase is the highest-emotion moment in the product. A modal dismisses it. A dedicated state celebrates or acknowledges it, and channels that emotion directly into the next engagement cycle. This was directly informed by behavioral patterns from gaming UX research.

The product is structured as a continuous loop across four states. Each state was designed with a specific emotional and functional goal. Every screen exists to move the user efficiently to the next state in the loop.

The loop: Enter position → Wait for resolution → Receive outcome → Enter next position.

The visual system was built to support the product's core principle: clarity under pressure. Prediction markets involve money, time pressure, and uncertainty, the interface can't add visual noise on top of that.

Core principles:

• Strong contrast to highlight primary actions, the UP/DOWN buttons are always the highest-contrast element on screen
• Minimalist layout, every element is either decision-critical or supporting context. Nothing decorative.
• Consistent spacing and grid for readability across states, users should never feel disoriented when transitioning between phases
• Numbers and states over decoration, the product communicates through data, not illustration
• Dark mode as default, reinforces the high-stakes, real-time nature of the product; also reduces eye strain during extended sessions

Design system scope:

• 120+ components built in Figma across desktop and mobile
• 4 core product states fully documented (front, participation, waiting, resolution)
• Full responsive coverage, every screen designed and tested at mobile breakpoints
• AI-assisted workflows used for rapid iteration across visual exploration phases, reduced ideation cycles by ~40%

Product performance:

• Task completion rate: 40% (v1, orderbook model) → 88% (v2, pool model), measured in usability sessions
• Average time-to-first-action: 8 seconds (v1) → under 2 seconds (v2), after reducing primary screen to 3 elements
• 3+ rounds completed in a session correlated strongly with return visits, establishing the target engagement benchmark for the team

Landing page validation:

• Waitlist conversion rate: 35%, average for B2B/Web3 waitlist pages is 2–8%; this was achieved with zero paid traffic through organic distribution in Discord and Twitter communities
• High scroll depth and consistent CTA engagement throughout the page indicated qualified interest, not casual traffic
• User behavior patterns showed clear anticipation of the product loop, validating both the concept and the communication

Design system impact:

• 120+ component design system reduced design-to-dev handoff time by ~35%
• 3 major product releases shipped across the engagement period
• Full desktop + mobile coverage delivered within sprint timelines

UI simplification without system simplification doesn't work.

• In v1, we tried to reduce complexity by simplifying the visual layer while keeping the orderbook structure intact. Usability sessions showed the same drop-off patterns. The problem wasn't visual, it was conceptual. The improvement came when we changed the underlying mechanics (pools instead of orderbooks), not just the presentation. Lesson: when users are confused, look at the mental model before looking at the UI.

Showing behavior is more powerful than showing data.

• The leaderboard and participation distribution screens were almost cut in early product reviews, they felt 'gamey' and potentially manipulative. We kept them because research showed users naturally seek this information. The result was one of the most engaged-with features in the product. Lesson: design around actual user behavior, not idealized user behavior.

The resolution phase is the product's most important screen.

• We spent the least design time on the win/loss state in v1, it was a modal. Adding a dedicated resolution screen with clear visual feedback and a direct next-action CTA was the single highest-impact change for repeat engagement. Lesson: the emotional peaks of a product deserve proportional design investment.

Progressive disclosure requires explicit prioritization decisions.

• The tension between the newcomer (needs simplicity) and the experienced user (needs depth) almost derailed the IA multiple times. The resolution was a clear rule: the product always loads in simple mode, with depth accessible but never forced. Every time we violated this rule in a sprint, usability metrics dropped. Lesson: establish your IA hierarchy explicitly and defend it.

AI-assisted workflows change the speed of exploration, not the quality of decisions.

• Using AI tools for rapid prototyping and visual exploration cut our ideation phases significantly. But the decisions themselves, what to prioritize, what to cut, what the user actually needs, required the same depth of reasoning. AI accelerated the surface; the thinking remained human.

Expand available prediction markets (more on-chain metrics)

• Prioritized based on direct user feedback: the most common request from active users was for more metric options beyond funding rate. Retention data also showed that users who had access to 3+ market types participated significantly more than single-market users, indicating variety as the primary long-term retention driver.

Onboarding flow for first-time participants

• Data from beta showed ~30% of new users completed their first round without guidance, but only ~15% came back for a second. A targeted first-round onboarding experience, explaining the pool mechanic once, clearly, is the highest-leverage intervention for newcomer retention.

Advanced analytics for experienced users

• Experienced users consistently asked for historical performance data and round-by-round analytics. This is the depth layer that keeps sophisticated users engaged long-term. Priority sequenced after onboarding, as the newcomer retention problem has higher volume impact.

Social and competitive mechanics

• Early behavioral signals, users checking the leaderboard before entering, referencing other participants' positions, point toward social engagement as an organic growth vector. A lightweight social layer (persistent leaderboard, season rankings, shareable outcomes) would formalize what users are already doing informally.