Short flow
App Store Users
Optimized for speed and comprehension:
- 3-step educational carousel
- Simplified Google / Apple login
- Immediate exposure to first-game selection
- Early “Welcome Reward” → accelerates activation
Behavior-driven earning platform redesigned to increase activation, retention, and user trust through structured progression and friction reduction.
LeapLoot is a mobile-first earning platform where users complete game-based tasks to earn real money.
The product had strong acquisition but weak behavioral performance:
Low activation
Low retention
High confusion
High support volume
The redesign focused on transforming LeapLoot from a transactional interface into a structured earning loop optimized for motivation, clarity, and progression.
Design of a scalable behavioral UX architecture for a reward-based earning platform.
Redesign of core earning, offer detail, and cashout flows to increase activation and retention.
Implementation of progressive disclosure to reduce cognitive load in high-density mobile screens.
Creation of a modular component system to support rapid iteration without a formal design system.
Direct collaboration with Frontend (Vue + Tailwind) to ensure accurate implementation and state consistency.
Understanding the initial product environment, constraints, and structural limitations
Before the redesign phase, the product faced:
The first step was not visual redesign, but restructuring the earning logic and user progression model to align acquisition expectations with in-app behavior and create a coherent behavioral loop.
The information architecture was restructured around a closed earning loop to reduce cognitive load and guide users toward immediate action.
Primary sections were reorganized to reflect behavioral progression (Earn → Activity → Cashout → Referrals → Settings), ensuring users always understand what to do next.
The structure supports scalability for new content types (Games, Surveys, Apps) without breaking progression logic.
Navigation was simplified to prioritize earning actions and reduce friction between discovery and execution.
Content categorization (Featured, Hot, New, Boost, Fast) improves scanability and accelerates decision-making on small screens.
Filtering and sorting patterns were unified to maintain consistency across content types and prevent navigation ambiguity.
Consistent component behavior was introduced across dropdowns, toggles, selectors, and state-based elements.
Clear system states (Running, Paid, Expired, Canceled) reduce ambiguity in task progression.
Disabled states and eligibility messaging were standardized to reinforce clarity in reward and cashout flows.
Offer details were modularized using progressive disclosure to prevent cognitive overload.
Reward logic, recurrent tasks, questlines, and step requirements were separated into structured layers.
Persistent continuation CTAs reinforce forward momentum within the earning loop.
The experience was optimized for ultra-small mobile screens while preserving clarity and hierarchy.
The same earning logic, progression clarity, and motivational feedback were preserved across all mobile flows.