James Lee
Research Papers
Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing
W. Y. Zou, S. Du, J. Lee, J. Pedersen
User marketing is a key focus of consumer-based internet companies. Learning algorithms are effective to optimize marketing campaigns which increase user engagement, and facilitates cross-marketing to related products. By attracting users with rewards, marketing methods are effective to boost user activity in the desired products. Rewards incur significant cost that can be off-set by increase in future revenue. [...] [PDF]
2020
User marketing is a key focus of consumer-based internet companies. Learning algorithms are effective to optimize marketing campaigns which increase user engagement, and facilitates cross-marketing to related products. By attracting users with rewards, marketing methods are effective to boost user activity in the desired products. Rewards incur significant cost that can be off-set by increase in future revenue. [...] [PDF]
2020
Improve User Retention with Causal Learning
S. Du, J. Lee, F. Ghaffarizadeh
User retention is a key focus for consumer based internet companies and promotions are an effective lever to improve retention. However, companies rely either on non-causal churn prediction to capture heterogeneity or on regular A/B testing to capture average treatment effect. In this paper, we propose a heterogeneous treatment effect optimization framework to capture both heterogeneity and causal effect. [...] [PDF]
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019
User retention is a key focus for consumer based internet companies and promotions are an effective lever to improve retention. However, companies rely either on non-causal churn prediction to capture heterogeneity or on regular A/B testing to capture average treatment effect. In this paper, we propose a heterogeneous treatment effect optimization framework to capture both heterogeneity and causal effect. [...] [PDF]
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019