Qiang Liu 劉強

MKTG Models · Healthcare · AI & Digital Economy

The stream of research develops quantitative marketing models of firm strategy and individual choice. Although many papers in this stream use healthcare and pharmaceutical markets as empirical contexts, their modeling contributions address broader questions in marketing strategy and choice behavior.

This study draws implications for targeting by incorporating switching reasons into a factor-analytic choice model for conducting benefit segmentation. In contrast to survey or conjoint-based studies, our segmentation task relies on a time series of brand choice decisions in real practice to improve external validity. Using physician-level panel data, we estimate a factor-analytic choice model to identify the primary product benefits each physician seeks. The key empirical challenge is that standard prescription data are entirely agnostic about the set of product benefits that underlie drug preferences. To address this issue, we utilize self-reported switching reasons in addition to the observed prescription choices. Accordingly, we extend the standard factor-analytic choice model to incorporate this augmented data and develop a Markov chain Monte Carlo (MCMC) procedure for estimation. Our proposed model enables us to directly identify which physicians are more efficacy, side effects, and/or cost saving oriented, an essential input to conducting benefit segmentation and fine-tuning subsequent targeted marketing promotion activities. We also investigate how misleading statistical inferences from standard factor-analytic choice models can be without the aid of augmented switching reasons data.

Different objectives such as category demand expansion or market share stealing warrant the use of different marketing instruments. To help brand managers make informed decisions, it is essential that marketing mix models appropriately measure their effects. Random Utility Models that have been applied to this problem might not be adequate because they do not allow the effects of marketing instruments of one brand to spillover to preference for competing alternatives. Additionally, they have the Invariant Proportion of Substitution (IPS) property, which in some situations imposes counter-intuitive restrictions on individual choice behavior. Recognizing that effects of marketing instruments can spill across brands in a category, we propose an alternative choice model that relaxes the IPS property: the cross attributes flexible substitution logit model. We apply the model in two very different empirical settings, i.e., consumer choices of brands of refrigerated yogurt, and prescription-writing choices of physicians in the hyperlipidemia category. In both settings the proposed model provides consistent evidence that certain marketing instruments produce sales gains primarily from growing the category pie, while others produce gains from stealing share. By contrast, the random coefficient logit and generalized nested logit models both predict that gains from all marketing instruments would have similar sources.

This paper examines the spillover effects of promotions when products from different firms are consumed in a bundle. Using data from the HIV/AIDS category, a canonical example of combination therapy, we estimate a hierarchical Bayesian logit model across treatment regimens and show that detailing for one drug can increase demand for other drugs that are often combined with the focal drug. Such spillover effects could lead to free riding by the drugs benefitting from the spillover. We investigate the managerial and policy implications of detailing spillover effects via counterfactual policy simulations based on a dynamic oligopoly game of detailing. For managers, we show how firms can internalize the spillover effects and reduce the incentive for free riding. For policy makers, the implications of our findings relate to detailing restrictions that are often proposed on branded drugs. These restrictions aim to increase social welfare by encouraging the use of generic drugs. However, in combination therapies generic drugs may actually benefit from the detailing of complementary branded products. If detailing was curtailed, this could adversely affect generic drug prescriptions as well, which runs counter to policy makers’ objectives of encouraging use of generic drugs.

This paper examines the spillover effects of promotions when products from different firms are consumed in a bundle. Using data from the HIV/AIDS category, a canonical example of combination therapy, we estimate a hierarchical Bayesian logit model across treatment regimens and show that detailing for one drug can increase demand for other drugs that are often combined with the focal drug. Such spillover effects could lead to free riding by the drugs benefitting from the spillover. We investigate the managerial and policy implications of detailing spillover effects via counterfactual policy simulations based on a dynamic oligopoly game of detailing. For managers, we show how firms can internalize the spillover effects and reduce the incentive for free riding. For policy makers, the implications of our findings relate to detailing restrictions that are often proposed on branded drugs. These restrictions aim to increase social welfare by encouraging the use of generic drugs. However, in combination therapies generic drugs may actually benefit from the detailing of complementary branded products. If detailing was curtailed, this could adversely affect generic drug prescriptions as well, which runs counter to policy makers’ objectives of encouraging use of generic drugs.