The project aims to understand and model user behavior in digital interfaces, using techniques such as Monte Carlo simulation and A/B testing to predict and analyze different usage scenarios. This includes developing skills in programming, simulation, and statistical analysis to improve the customer experience on websites and apps.
APPLICATION EXAMPLES:
- E-commerce optimization: Using Monte Carlo simulation and A/B testing to analyze user behavior on an e-commerce platform, identifying patterns that influence the purchasing decision.
- Optimizing Lead Capture on Digital Platforms: Implement advanced behavioral analysis techniques to understand and optimize the user journey on digital platforms in order to maximize lead capture.
MANDATORY INFRASTRUCTURE RESOURCES:
- Algorithm developed in the Python language;
- HTML and CSS languages for the front-end;
- PostgreSQL Database;
- Back-end execution environment: Node.js;
- Version control system and project documentation: Github;
- Deployment platform (application publishing): CodeSandbox or similar;
- Prototyping environment: Figma or similar
- Communication environment: Slack
- Miscellaneous file storage environment: Google Drive (Internal Inteli)
