Jim Hogan
About Jim Hogan
Jim Hogan serves as the Director of Consumer Revenue Analytics at Condé Nast, a position he has held since 2019. He has extensive experience in revenue analytics, having previously worked as a Manager of Revenue and Yield Analytics and as a Revenue Operations Analyst at the same company.
Current Role at Condé Nast
Jim Hogan serves as the Director of Consumer Revenue Analytics at Condé Nast, a position he has held since 2019. In this role, he focuses on enhancing consumer revenue strategies and analytics within the organization. His work is based in the Greater New York City Area, where he has contributed to the company's growth and profitability through data-driven decision-making.
Previous Experience at Condé Nast
Prior to his current role, Jim Hogan held multiple positions at Condé Nast. He worked as the Manager of Revenue and Yield Analytics from 2013 to 2018, where he managed revenue strategies across various brands. He also briefly served as a Revenue Operations Analyst in 2013. His tenure at Condé Nast spans over five years, during which he developed key analytics practices.
Education and Academic Background
Jim Hogan studied at Virginia Tech, where he earned a Bachelor of Science degree in Economics in Business from 2007 to 2011. He furthered his education at USI Università della Svizzera italiana, focusing on Marketing for 11 months in 2010. His academic background provides a strong foundation for his work in analytics and revenue optimization.
Analytics and Revenue Optimization Initiatives
In his professional capacity, Jim Hogan has led several significant initiatives. He operationalized and developed best practices in A/B testing across ten brands. He also implemented Marketing Mix Models and Multi-Touch Attribution models, which enhance the understanding of consumer behavior. Additionally, he created Subscriber Lifetime Value Models for Paywall Subscribers and a newsletter LTV, contributing to more effective revenue strategies.
Dynamic Paywall Optimization Models
Jim Hogan has developed and launched dynamic paywall optimization models across seven brands at Condé Nast. These models are designed to maximize enterprise profit by analyzing consumer interactions and preferences. His work in this area reflects a commitment to leveraging analytics for improved revenue outcomes.