Web & UX

Over the last few years, I have worked in web/UX analytics, digital product ownership, data science, research, and predictive analysis. My expertise spans a wide array of topics within this domain, ensuring a comprehensive approach to product development, user experience optimization, and data-driven decision-making. Here’s a breakdown of the diverse areas I’ve delved into:

Product Ownership for Data Products:

• Development and management of data-driven products tailored for specific user needs.
• Collaboration with cross-functional teams to ensure product viability and user-centricity.
• Strategic road-mapping and backlog prioritization for continuous product enhancement.

Analytics and Data Science:

• Utilizing advanced statistical models to derive actionable insights from user data.
• Predictive modeling to forecast user behavior and product interaction patterns.
• Advanced data visualization techniques for stakeholder communication and decision-making.

User Experience (UX) Analytics:

• Comprehensive user journey mapping to optimize touchpoints and enhance satisfaction.
• A/B testing and multivariate testing to refine and improve user interfaces.
• Behavior flow analysis to understand and enhance user navigation patterns.

Research and Predictive Analysis:

• Deep dive into user demographics, psychographics, and feedback to inform product enhancements.
• Predictive analytics to anticipate future trends and user needs.
• Utilizing machine learning models for pattern recognition and anomaly detection.

Web Analytics:

• Monitoring and analyzing website traffic patterns and user engagement metrics.
• Conversion rate optimization to maximize user acquisition and retention.
• Implementing tools like Google Analytics and Hotjar for in-depth web analysis.

Tag Management and Tracking Development:

• Implementing and managing tag solutions for accurate user tracking.
• Collaborating with developers to ensure accurate data capture through tags.
• Continuous refinement of tracking methodologies to capture nuanced user interactions.

Statistical Analysis:

• Employing robust statistical methods to analyze vast datasets.
• Hypothesis testing to validate data-driven assumptions.
• Correlational studies to understand the relationship between different user behavior metrics.

E-commerce Analytics:

• Analysis of user purchase behavior and cart abandonment rates.
• Product page optimization based on user interaction data.
• Price elasticity studies to inform pricing strategies.

Mobile Analytics:

• Monitoring user engagement on mobile apps.
• Analyzing app crashes and user feedback for continuous improvement.
• Funnel analysis to understand user conversion rates.

Each of these areas has presented its own set of intricate challenges and learning opportunities. My holistic approach, grounded in rich industry expertise, has consistently provided immense value to my clients, empowering them to make informed decisions and enhance their digital presence.