eCloudMobile POS

The Challenge: Scaling Across Operational Complexity

The primary challenge was not simply adding new features—it was reconciling fundamentally different business models within a single system.

For example:

• Beverage / F&B businesses require deep customization (size, sugar level, temperature, add-ons), where each selection dynamically affects pricing
• Retail businesses prioritize speed, SKU clarity, and inventory accuracy, with minimal configuration complexity

Without careful design, the system risked becoming:

• Too rigid → unable to support real-world workflows
• Too flexible → overwhelming users with configuration complexity and increasing the risk of errors

Core Problem: Pricing Complexity at Scale

Through user feedback and competitive analysis, I identified a critical bottleneck: as product configurations increase, pricing logic becomes exponentially more complex.

Store operators often had to:

• Manually calculate prices across combinations of attributes
• Cross-check pricing across different sales channels
• Rely on spreadsheets or mental math to ensure margins

This led to:

• Inconsistent pricing
• High cognitive load during setup
• Risk of costly configuration errors

Ideation: Mapping the System Logic

Before moving into interface design, I mapped the underlying pricing logic to ensure the system could support complex relationships across attributes, channels, and modifiers.

Design Principle

Balance Flexibility with Structured Constraints

Instead of allowing unrestricted customization, I focused on building a system that:

• Supports diverse business models
• Maintains clear structure and predictability
• Reduces the likelihood of user error

Design Decisions

1. Modular Product Framework

I introduced a modular system that separates:

• Required Attributes (e.g., base product, size)
• Customizable Modifiers (e.g., add-ons, variations)

This structure allows:

• F&B businesses to handle complex configurations
• Retail businesses to maintain simple, scalable product setups

The system dynamically adapts based on business needs while keeping data organized and manageable.

2. Visualizing Complexity: Pricing Dendrogram

To address pricing complexity, I introduced a dendrogram-based interface that visualizes pricing relationships as a hierarchical structure.

This transforms pricing from:

• Hidden logic spread across multiple fields → a visual system users can understand and control

Key benefits:

• Clear visibility of relationships between attributes, channels, and pricing
• Real-time calculation of final prices
• Reduced reliance on manual verification

3. Automated Pricing Logic

Users can define pricing using:

• Fixed values
• Percentage-based modifiers

The system automatically calculates final prices across all combinations, ensuring consistency across:

• Product variations
• Sales channels
• Fulfillment methods

This eliminates the need for manual calculations and significantly reduces error risk.

4. Error Prevention: Confirmation Loop

To prevent costly mistakes, I designed a mandatory validation system:

• Each pricing branch must be reviewed and confirmed before publishing
• Incomplete or invalid configurations are clearly flagged

This ensures:

• No accidental $0 pricing
• Higher confidence during product setup
• Improved data integrity for high-volume merchants

Final Interface

The final interface translates complex pricing logic into a structured and manageable workflow, allowing users to configure products efficiently without sacrificing clarity.

Example Scenario: Configuring a Beverage Product

Instead of manually calculating each variation, they:

1. Define base pricing
2. Apply modifiers (e.g., +$10 for large size, +$5 for add-ons)
3. Review the dendrogram to verify pricing relationships
4. Confirm each branch before publishing

Within minutes, a previously complex setup becomes:

• Structured
• Visualized
• Error-resistant

Collaboration & Technical Feasibility

Given the complexity of the system, close collaboration with engineering was critical. I worked with developers to validate:

• Data structure feasibility: ensuring hierarchical pricing relationships could be queried efficiently
• UI scalability: supporting large datasets with hundreds of product variations without performance degradation
• Pricing logic integrity: ensuring modifier rules correctly propagate across channels and attributes

Impact (Pre-Launch)

The redesigned system improves product configuration by:

• Eliminating manual price calculations
• Reducing the risk of pricing inconsistencies
• Structuring complex configurations into manageable workflows

This enables merchants to:

• Set up products faster
• Maintain pricing accuracy across channels
• Operate with greater confidence during high-volume operations

Reflections

This project reinforced that scalability is not about adding more flexibility—it’s about defining the right constraints. By structuring the system around modular logic instead of custom exceptions, we were able to support diverse business needs while maintaining clarity and usability.

Next Steps: Post-Launch Validation

Following launch, the focus will shift to validating real-world performance:

• Operational Efficiency: measuring how quickly merchants can configure products compared to the legacy system
• Error Reduction: tracking pricing-related issues and configuration mistakes
• Scalability Validation: ensuring the system performs reliably across different industries and business sizes