Dan B.

Designed and optimized large-scale SQL data models and analytical frameworks (CTEs,

window functions, advanced aggregations) to process and analyze millions of marketing and

bidding events across user acquisition funnels.

Developed and deployed machine learning models to predict Earn Per Shopper (EPS),

conversion probability, and acquisition performance, enabling more accurate bidding and

traffic allocation decisions.

Developed automated recommendation and decision-making systems for bid optimization,

leveraging key business KPIs such as ROAS, CPB, CTR, BR, QR, and conversion trends to

dynamically adjust acquisition strategies.

Performed deep-dive multidimensional analyses across traffic sources, user segments, states,

devices, and bidding channels to identify high-value opportunities and reduce inefficient

media spend.

Created scalable monitoring systems and executive dashboards using SQL, Looker, Tableau,

and QuickSight, enabling real-time visibility into marketing performance and operational KPIs.

1 Data-Driven Campaign Optimization Leverage in-depth performance data to continuously optimize PPC campaigns across Google Ads and Bing, focusing on improving ROI, CTR, and CPA through actionable insights. 2 Advanced Analytics & Reporting Build and maintain detailed performance dashboards using tools like Quicksight, Tableau, and Excel to monitor KPIs and guide decision-making. 3 AI-Powered Performance Enhancement Utilize machine learning tools and automation features to enhance bidding strategies, audience targeting, and budget allocation. 4 A/B Testing and Experimentation Design and analyze A/B tests on ad creatives and landing pages to validate hypotheses and drive measurable improvements in user engagement and conversions. 5 Cross-Team Collaboration on Data Strategy Collaborate with product, content, and designs teams to align data insights with content strategy and landing page performance, ensuring consistent messaging and improved user retention.

1 Player Data Analysis: Collect, process, and analyze player data to understand their behaviors and preferences. 2 Report and Dashboard Creation: Use BI tools to develop reports and interactive dashboards that visualize game performance and player trends. Python SQL 3 Decision Support: Provide data-driven insights to development and marketing teams to improve game and monetization strategies. 4 User Experience Optimization: Identify opportunities to improve the user experience Tableau/ Quicksight based on collected data and propose concrete solutions. Excel/ Office 5 KPI Tracking: Monitor key performance indicators (KPIs) to evaluate the success initiatives and marketing campaigns.