Date of Award
Doctor of Philosophy (PhD)
In this dissertation, we study referral programs and preference estimation in two essays. In the first essay, we propose that a firm can enhance the effectiveness of its referral program by promoting better matching between referred customers and the firm. We develop three treatments aimed at promoting better matching, including (1) offering current customers a gift before inviting them to refer friends, (2) notifying current customers about the value that they have received from the firm before inviting them to refer friends, and (3) rewarding referring customers based on the value of their referred customers. We test these three treatments by conducting two field experiments in collaboration with a Chinese online financial services firm. We find that all three treatments substantially enhanced the effectiveness of the focal referral program, measured for each current customer as the total value of his referred customers. We also find that the enhancement was primarily driven by the acquisition of higher-value new customers rather than the acquisition of more new customers. In addition, we investigate customer heterogeneity in treatment effects and explore the mechanisms through which these treatments impacted customer referrals. In the second essay, we develop a new model for effective modeling of consumer heterogeneity in choice-based conjoint estimation. Assuming that most variations in consumers' partworth vectors are along a small number of orthogonal directions, we propose that shrinking the individual-level partworth vectors toward a low-dimensional affine subspace that is also inferred from data can be an effective approach to pooling information across consumers and modeling consumer heterogeneity. We develop a low-dimension learning model to implement this information pooling mechanism that builds on recent advances in rank minimization and machine learning. We evaluate the empirical performance of the low-dimension learning model using both simulation experiments and field choice-based conjoint data sets. We find that the low-dimension learning model overall outperforms multiple benchmark models in terms of both parameter recovery and predictive accuracy. While addressing two different marketing topics, both essays share a common theme - careful modeling of consumer heterogeneity plays a key role in understanding consumer behavior and developing effective marketing strategies.
Chen, Yupeng, "Essays On Referral Programs And Preference Estimation" (2018). Publicly Accessible Penn Dissertations. 2831.