Dear All,
Our next Virtual Quant Marketing Seminar is Monday, January 22, at Noon ET. The speaker will be Sanjog Misra.
The zoom link is below, note that this is a new link.
https://hbs.zoom.us/j/93651241772?pwd=N2dTTC9RZHdoTjhXUjdPOXpkRkNDdz09
Title: Deeply Personal Marketing
Abstract: The emergence of machine learning, and in particular deep learning, has had a significant impact on science and society. Typically, deep learning models are used for various forms of prediction but they have the potential to be used in alternative ways in the context of the economics and marketing. One particular use case is the approximation of individual heterogeneity. The key idea being that with the abundance of unit-level data we should be able to construct measures that describe individual differences flexibly and accurately. As a natural next step this heterogeneity can then be used to inform and design personalized policies.
Deep learning models are particularly well suited to this task because of their flexibility, their compatibility with economic structure and the availability of scalable, easy to use software. In this talk I will introduce a framework to learn heterogeneous effects of economic decisions using deep learning. The proposed framework takes a standard economic model and recasts the parameters as flexible functions which afford the capture of heterogeneity across agents based on high dimensional or complex observable characteristics. These parameter functions retain the interpretability and economic meaning the original parameters and can be used in much the same way scalar parameters are to construct measures, conduct counterfactuals or design policies of interest. In addition, the framework also provides an automatic inference engine based on a computationally viable influence function approach. This automatic approach leverages recent developments in automatic differentiation engines to allow the researcher to conduct statistical inference without the need for any additional conceptual effort.
Following this introduction, I will demonstrate the implementation of this approach using a series of applications including optimal targeting, price personalization and the generation of text based policy interventions. The applications will aim to showcase the choices that need to be made in the implementation of this framework, the viability of this approach, it advantages, as well as inconveniences and limitations. The use of such ML-based personalization approaches also open up a set of concerns and questions about downstream consequences. I will conclude the talk by offering some discussion on these from the perspective of consumers, firms and policy makers.
This presentation is based on a series of projects with various co-authors. Details on the framework are contained in https://arxiv.org/pdf/1809.09953.pdf and https://arxiv. org/abs/2010.14694.