Digital Yield Analyst

About The Role

As a Yield Analyst on our Yield & Analytics team, you will own the review of client performance data to drive support for media mix and maximize KPIs. You will also be responsible for building and managing reports on different ad types and content categories, provide insight into unfulfilled budgets, investigate and resolve discrepancies, and oversee daily monetization KPIs to ensure rapid identification and resolution of issues that could impact revenue. Additionally, you will compare revenue performance against forecasts, and identify variances to refine forecasting models.

The salary range for this role is $70,000 - $90,000. Pay offered may vary within the posted range based on a number of factors including but not limited to job-related knowledge, skills, experience, and location.

WHAT YOU’LL TACKLE  

• Provide stakeholders with 100% placement-level data visibility by building reports to provide key performance metrics across different ad types, categories, and client KPIs.
• Increase average revenue per landing page by 15% by creating critical weekly reporting paired with data-driven insights and recommendations
• Review unfulfilled budgets to identify common factors and offer visibility into adjustments needed to support full delivery.
• Reduce revenue-impacting issue resolution time by 50% by implementing a system to oversee the identification and resolution of issues impacting monetization KPIs.

HOW YOU’LL MAKE AN IMPACT

• Regularly review client performance data (GPM, C2C and KPI's) to drive support for media mix to maximize KPIs
• Build and manage reporting on different ad types, content categories at the placement level for client KPIs, impressions, clicks, CTR, CTC, revenue, cost
• Build and manage reports on each client KPI: eCPA, account opened, account funded, bank CPM.
• Compare actual revenue performance against forecasts to identify variances and refine forecasting models.
• Build and manage reports to review monetization by landing page and offer, month-over-month, compare to rankings decisions and provide insights into a ranking structure to improve monetization.
• Review unfulfilled budgets from YTD to identify common factors and propose adjustments (pricing, CTR, targeting, ect.) to support full delivery.
• Investigate and resolve discrepancies in reporting, campaign delivery, and platform integrations.
• Actively monitor trends to identify opportunities to maximize revenue.
• Build data-driven forecasts and models to anticipate future trends and needs.
• Prioritize tasks based on impact and urgency, focusing on initiatives that drive the highest returns.
• Conduct in-depth analysis of advertising performance, understanding patterns, and identifying actionable insights.
• Communicate complex data findings in a clear, concise, and actionable way to stakeholders.
• Oversee daily monetization KPIs and ensure rapid identification and resolution of issues within the ad stack.

WHAT YOU’LL NEED

• Bachelor’s degree in Business, Economics, Marketing, Data Science, or a related field.
• 1–3 years of experience in analytics, candidates in digital advertising preferred
• Proficiency in analytics tools (e.g., Google Analytics, Tableau, Excel).

HOW YOU’LL LEVEL UP  

• Results-Oriented Approach. A track record of achieving quantifiable improvements, such as increased revenue and C2C. Evidence of setting and meeting ambitious performance benchmarks.
• Exceptional Analytical Skills. Proficiency with tools like Excel, SQL, Tableau, Power BI, or Python for data analysis. Ability to draw actionable insights from metrics such as CTR, C2C, conversions, and revenue.
• Problem-Solving Expertise. A structured approach to diagnosing issues and proposing data-driven solutions. Creativity in developing strategies to improve revenue or resolve technical problems.
• Adaptability and Continuous Learning. A demonstrated ability to learn new tools, platforms, or strategies quickly.
• Meticulous Attention to Detail. A track record of accuracy in reporting and analysis. An ability to spot discrepancies or anomalies in complex datasets.
• Proactive Mindset. Examples of taking initiative to address challenges or improve processes. A forward-thinking approach to problem-solving and optimization.