Loyalteez
Open loyalty platform that cures loyalty fatigue and empowers users
The Problem
Traditional loyalty is transaction-based and fragmented. Users are involved in ~19 programs and care about less than half of them. Loyalty fatigue is real-accumulated debt across systems nobody uses.
The Process
Designed an open platform using behavioral science principles. Users own their engagement history. Brands compete for attention by offering real value, not trapping points. Used AI to rapidly prototype dashboard layouts (5 variations in 30 mins).
The Outcome
Live at loyalteez.app. Currently in early stage-building toward first partners. The platform infrastructure is ready; now focused on go-to-market.
Design Decisions
| Decision | Why | Engineering Tradeoff |
|---|---|---|
| Open-loop model | Actions earn tokens, redeemable anywhere in ecosystem | Cross-platform token management complexity |
| Platform-agnostic connectors | Discord, Telegram, X integration without lock-in | Multiple API integrations to maintain |
| Event-triggered rewards | Purchase-event triggers, not just logins | Webhook infrastructure required |
STAR Summary
| Situation | Traditional loyalty is broken. Users are enrolled in ~19 programs and care about less than half. Transaction-based, fragmented systems create loyalty fatigue and accumulated “debt” across programs nobody uses. |
| Task | Cure loyalty fatigue. Eliminate loyalty system debt. Create an open platform that empowers users-they own their engagement history, brands compete for attention by offering real value. |
| Action | Designed open-loop model with behavioral science principles. Built reusable engagement modules (streaks, badges, leaderboards). Used AI to generate 5 dashboard variations in 30 minutes. Created platform-agnostic connectors across social, web, and on-chain touchpoints. |
| Result | Live at loyalteez.app. Platform infrastructure complete. Currently building toward first partners. |
AI-Accelerated Design
| Step | Action | Outcome |
|---|---|---|
| 1 | Described requirements to Claude | Clear constraints defined |
| 2 | Generated 5 layout approaches | 30 minutes vs. days |
| 3 | Evaluated against user needs | Identity-first pattern emerged |
| 4 | Refined winner iteratively | Gallery shows evolution |
Key Insight: AI helped identify cleaner patterns-show who the user is first, then balances, then business context.
Engagement Modules
| Module | Mechanic | Purpose |
|---|---|---|
| Streak Trackers | Daily check-ins | Habit formation |
| Achievement Badges | Milestone recognition | Progress visibility |
| Leaderboards | Competitive ranking | Social motivation |
| Reward Triggers | Event-based distribution | Immediate gratification |
Each module is configurable across traditional retail, Web3, or hybrid integrations.
Gallery
Early iteration: Analytics & Performance dashboard with reward distribution charts