Johanna Fay Gan

In my role as a Data Analyst at Google, I led marketing science initiatives, employing data science and ad tech to drive client success. I conducted detailed data analysis using tools like Campaign Manager and Google Analytics, identifying key customer behaviors and presenting actionable insights to non-technical stakeholders. My proficiency in SQL and Python enabled me to extract and transform complex data, while my application of machine learning models and statistical analysis contributed to a 20% increase in revenue. I played a pivotal role in developing and optimizing customer relationship management dashboards, managing data pipelines and addressing ad-hoc requests. Additionally, my contributions to product marketing involved driving impactful data analysis initiatives, optimizing email campaigns, and presenting findings to diverse audiences. My experiences at Google showcase my ability to deliver strategic recommendations, collaborate cross-functionally, and leverage data-driven insights for business success.

Spearheaded initiatives within a team of Marketing Science Leads, harnessing data science and ad tech to assist clients in achieving strategic business objectives.
Conducted exploratory data analysis using Campaign Manager, extracting and organizing relevant data in spreadsheets to compare sales conversions based on dates and digital media platforms.
Led in-depth analysis of customer populations by analyzing and interpreting data from Google Analytics, identifying key differentiating factors between converter and non-converter profiles.
Unearthed critical behaviors of high-value customers, including page pathways, technology usage, demographics, affinity groups, and in-market segments.
Presented findings to non-technical stakeholders, delivering clear and data-driven insights that informed ad targeting strategies, resulting in a 20% increase in revenue.
Applied machine learning and statistical models to predict likelihood of future purchases based on historical sales.
Utilized SQL and Python to extract and transform raw nested backend data.
Leveraged BigQuery Machine Learning to implement various classification models on transformed data.
Achieved a propensity model with 92% accuracy in prediction outcomes.
Enabled the client to isolate the top 20% of the customer population with an 8X conversion rate compared to the standard population.
Assisted team in activating models across Google's advertising platforms, optimizing ad targeting and driving enhanced conversion rates.
Presented findings before a diverse audience of analysts and data scientists, articulating insights and recommendations in practical and actionable steps.

Managed, transformed, and documented patient population health data to cloud server while ensuring data accuracy and reliability.
Collaborated closely with diverse stakeholders, including clinicians, administrators, and technical teams, to understand the intricacies of healthcare data and its impact on patient outcomes.
Regularly generated and analyzed weekly Tableau reports on team performance and data outcomes, providing valuable insights that informed key decision-making processes and improved overall business operations.
Successfully navigated complex and multi-faceted client cases with a high-performing cross-functional team.

Managed and developed data pipelines that produce customer insights in content management and performance dashboards used by 100s of Googlers across Global Business Operations.
Rapidly acquired an understanding of business processes, leveraging this knowledge in strategic planning, execution, and reporting.
Addressed diverse ad-hoc requests, ranging from troubleshooting SQL scripts and pipelines to optimizing the user interface of dashboards, contributing to the seamless operation of critical systems.
Collaborated closely with product managers and engineers to enhance data quality and operational efficiencies, aligning data processes with evolving business needs.
Played a pivotal role in supporting product engineers during the migration and automation of backend workflows, ensuring the preservation of historical data integrity and the accuracy of dashboard outputs.
Developed comprehensive relational diagrams and technical documentation outlining intricate data workflows.
Delivered a strategic recommendation for the long-term management of data processes and scripts, contributing to the overall efficiency and sustainability of data operations.

Drove impactful data analysis initiatives within a collaborative team of product marketers, focusing on assessing and enhancing the effectiveness of email campaigns.
Queried, processed, and validated data using SQL and pivot tables for analysis of product data.
Delved into user behaviors, drawing insightful comparisons between engaged and unengaged users.
Proactively addressed stakeholder needs, swiftly acquiring an understanding of business processes and objectives.
Partnered with data engineers and successfully streamlined data access, leading to a 30% reduction in the time required for campaign output.
Presented findings to both the Growth Marketing and Ads Marketing teams, emphasizing the correlation between email open rates, click-through rates, and product usage trends.
Effectively communicated complex insights to both technical and non-technical stakeholders, employing a clear and concise approach to enhance overall comprehension.
Utilized data-driven insights to refine marketing email campaigns, thereby optimizing $6 billion in ad revenue streams.