About
The Organizer
The Organizer
Dr. Julian Runge is a behavioral economist and data scientist. He consults media and entertainment companies how to strategically use data, science, and experimentation to fuel their growth and customer experiences. He is also an advisor and angel investor in the applied AI and consumer tech space. Julian has published extensively on the use of algorithms, behavioral science, and experimentation to deliver better digital experiences in outlets ranging from the Harvard Business Review to the A+ journal Information Systems Research. After years of research on game data science and digital marketing at Facebook (Meta), Wooga, N3TWORK, Stanford, and Duke University, he is now an assistant professor at Northeastern University in Boston. He holds a Ph.D. in Economics and Management Science from Humboldt University Berlin and was a doctoral research fellow at Stanford University.
Dr. Julian Runge is a behavioral economist and data scientist. He consults media and entertainment companies how to strategically use data, science, and experimentation to fuel their growth and customer experiences. He is also an advisor and angel investor in the applied AI and consumer tech space. Julian has published extensively on the use of algorithms, behavioral science, and experimentation to deliver better digital experiences in outlets ranging from the Harvard Business Review to the A+ journal Information Systems Research. After years of research on game data science and digital marketing at Facebook (Meta), Wooga, N3TWORK, Stanford, and Duke University, he is now an assistant professor at Northeastern University in Boston. He holds a Ph.D. in Economics and Management Science from Humboldt University Berlin and was a doctoral research fellow at Stanford University.
Reach out at datafreud [at] gmail [dot] com
Reach out at datafreud [at] gmail [dot] com
Select Articles and Interviews
Select Articles and Interviews
Stanford Business Insights on Price Cutting in Free-to-Play: https://www.gsb.stanford.edu/insights/why-free-play-apps-can-ignore-old-rules-about-cutting-prices
Stanford Business Insights on Price Cutting in Free-to-Play: https://www.gsb.stanford.edu/insights/why-free-play-apps-can-ignore-old-rules-about-cutting-prices
Harvard Business Review on Digital Ad Targeting: https://hbr.org/2021/04/apple-is-changing-how-digital-ads-work-are-advertisers-prepared
Harvard Business Review on Digital Ad Targeting: https://hbr.org/2021/04/apple-is-changing-how-digital-ads-work-are-advertisers-prepared
Harvard Business Review on Business Experimentation: https://hbr.org/2020/10/marketers-underuse-ad-experiments-thats-a-big-mistake
Harvard Business Review on Business Experimentation: https://hbr.org/2020/10/marketers-underuse-ad-experiments-thats-a-big-mistake
Case Studies
Case Studies
Ascarza, E., Netzer, O., and Runge, J. (2020). The Twofold Effect of Customer Retention in Freemium Settings. Harvard Business School Working Paper. [https://www.hbs.edu/ris/Publication%20Files/21-062_60db6305-3f52-4a5a-953c-3a727b514cf6.pdf]
Ascarza, E., Netzer, O., and Runge, J. (2020). The Twofold Effect of Customer Retention in Freemium Settings. Harvard Business School Working Paper. [https://www.hbs.edu/ris/Publication%20Files/21-062_60db6305-3f52-4a5a-953c-3a727b514cf6.pdf]
Runge, J., Levav, J., and Nair, H. (2022). Price Promotions for Freemium App Monetization. Quantitative Marketing and Economics. [https://link.springer.com/article/10.1007/s11129-022-09248-3 ]
Runge, J., Levav, J., and Nair, H. (2022). Price Promotions for Freemium App Monetization. Quantitative Marketing and Economics. [https://link.springer.com/article/10.1007/s11129-022-09248-3 ]
Lopez-Vargas, K., Runge, J., and Zhang, R. (2022). Algorithmic Assortative Matching on a Digital Social Medium. Information Systems Research. [https://pubsonline.informs.org/doi/abs/10.1287/isre.2022.1135]
Lopez-Vargas, K., Runge, J., and Zhang, R. (2022). Algorithmic Assortative Matching on a Digital Social Medium. Information Systems Research. [https://pubsonline.informs.org/doi/abs/10.1287/isre.2022.1135]
Runge, J. (2020). Applications of Advanced Analytics to the Promotion of Freemium Goods. Doctoral dissertation. [https://edoc.hu-berlin.de/bitstream/handle/18452/22696/dissertation_runge_julian.pdf]
Runge, J. (2020). Applications of Advanced Analytics to the Promotion of Freemium Goods. Doctoral dissertation. [https://edoc.hu-berlin.de/bitstream/handle/18452/22696/dissertation_runge_julian.pdf]
Sifa, R., Runge, J., Bauckhage, C., and Klapper, D. (2018). Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE. Proceedings of the Hawaii International Conference on System Sciences (HICSS). [https://scholarspace.manoa.hawaii.edu/bitstream/10125/50002/paper0115.pdf]
Sifa, R., Runge, J., Bauckhage, C., and Klapper, D. (2018). Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE. Proceedings of the Hawaii International Conference on System Sciences (HICSS). [https://scholarspace.manoa.hawaii.edu/bitstream/10125/50002/paper0115.pdf]
Runge, J., Gao, P., Garcin, F., and Faltings, B. (2014). Churn Prediction for High-Value Players in Casual Social Games. Proceedings of the IEEE Conference on Computational Intelligence in Games (CIG). [https://ieeexplore.ieee.org/document/6932875]
Runge, J., Gao, P., Garcin, F., and Faltings, B. (2014). Churn Prediction for High-Value Players in Casual Social Games. Proceedings of the IEEE Conference on Computational Intelligence in Games (CIG). [https://ieeexplore.ieee.org/document/6932875]