AUX - Automated UI/UX Experience Evaluation

Intelligent evaluation platform that automatically generates user personas and performs thousands of behavior simulations based on design mockups to assess conversion intent

Client Baozun E-commerce (Internal)
Completion Date 1/15/2024
UI/UX EvaluationUser PersonasBehavior SimulationConversion AnalysisTest Automation

Project Overview

Project Overview

AUX (Automated UI/UX Experience Evaluation) is an automated UI/UX experience evaluation system developed for Baozun E-commerce’s internal use. The system automatically generates diverse user personas through AI technology, performs large-scale user behavior simulations based on uploaded design mockups, and quantitatively evaluates the performance of design solutions under specific goals (such as order conversion).

Core Features

  • Intelligent User Persona Generation: Generate multi-dimensional user personas based on historical data and user research
  • Large-scale Behavior Simulation: Each persona performs thousands of simulation operations, covering various user behavior paths
  • Conversion Goal Assessment: Quantitative analysis for specific business objectives (orders, registrations, browsing, etc.)
  • Visualization Reports: Generate detailed evaluation reports and optimization recommendations
  • A/B Testing Support: Support comparative evaluation of multiple design solutions

Technical Architecture

AI Core:

  • User behavior prediction model trained on extensive historical interaction data
  • User persona generation algorithm covering multi-dimensional features like age, gender, consumption habits
  • Conversion path analysis engine identifying key conversion nodes

Simulation Execution:

  • Selenium automation framework driving browser operations
  • Multi-threaded concurrent execution supporting large-scale simulation testing
  • Intelligent operation decision-making adjusting strategies based on user personas

Data Analysis:

  • Real-time data collection and processing
  • Statistical analysis and visualization display
  • Continuous optimization of machine learning models

Workflow Process

  1. Design Upload: Upload UI design mockups or prototypes for evaluation
  2. User Persona Configuration: Select or generate target user personas
  3. Goal Setting: Define evaluation objectives (conversion events, key metrics, etc.)
  4. Batch Simulation: System automatically executes thousand-level user behavior simulations
  5. Result Analysis: Generate detailed evaluation reports and optimization recommendations

Innovation Highlights

  1. Large-scale Testing: Single evaluation can simulate real behavior of thousands of users
  2. Multi-dimensional Personas: Combine demographic, psychological, and behavioral characteristics
  3. Intelligent Decision-making: Operation decisions during simulation based on real user behavior data
  4. Real-time Feedback: Quick quantitative evaluation results for design solutions
  5. Continuous Learning: System continuously learns from real user data to improve prediction accuracy

Business Value

  • Reduce Trial and Error Costs: Identify design issues in early development stages, avoiding late-stage modifications
  • Improve Decision Quality: Make design decisions based on data rather than subjective judgment
  • Accelerate Iteration Speed: Quickly validate design assumptions, shorten product launch cycles
  • Optimize User Experience: Identify user pain points and provide targeted optimization recommendations

Technologies

PythonTensorFlowSeleniumReactD3.jsPostgreSQLRedisDocker

项目信息

Client Baozun E-commerce (Internal)
Completion Date 1/15/2024
Category User Experience

Challenge

Traditional UI/UX evaluation relies on manual testing, which is inefficient and cannot simulate large-scale user behavior or accurately predict design conversion effects

Solution

Build an automated UI/UX evaluation system that generates user personas through AI and simulates real user behavior to quantitatively assess the conversion potential of design solutions

Results

1000x improvement in evaluation efficiency
70% reduction in design iteration cycle
85% accuracy in conversion rate prediction
80% reduction in labor costs
90% improvement in data-driven design decisions