This dissertation is a combination of three papers that use machine learning methods to investigate research questions of interest to marketing managers and regulators. In the first chapter, the authors investigate if firms that face crises have systematically different corporate culture, and whether employee reviews provide early warnings. The authors examine these questions in the Wells Fargo consumer banking crisis context, and compare the culture discussions in online reviews posted by employees of Wells Fargo to those of other banks. They measure two important dimensions of corporate culture – control of employees and stabilityof processes, and focus on competition-based goals and outcomes. They find that sentiment of culture discussions on competition goals, and on rules and stability at Wells Fargo is more negative than other banks, and this is visible as far back as ten quarters prior to the crisis reveal. These are the same causes of crisis identified in a definitive post-mortem report. Additionally, they identify another bank which could potentially be at crisis, and also find similar results for other consumer-harming crises at General Motors in 2014, Chipotle in 2015, and Mylan in 2016. In the second chapter, the authors use social media images data to estimate the impact of taxes on underage vaping. Various states in the US have enacted taxes to discourage E-cigarette usage, especially since underage usage has grown significantly. Data is difficult for underage consumption since it is illegal for them to purchase these products, and estimating tax impact has not been limited due to these constraints. The authors use publicly available user-posted images on social media from Jan 2016 - Dec 2018 to measure the impact of greater taxes on underage posting behavior. These posts are a rough proxy for normalization, and potentially for consumption among underage population. Age and other demographics are detected using an ensemble of image analysis methods - Mask R-CNN (He et al., 2017) and Aggregated Residual Neural Networks (Xie et al., 2017). The authors also develop methods to estimate disguised posting of usage images, given their purported utilization by underage users. With the generalized synthetic controls (Xu, 2017), the authors find that only the states with higher taxes - Pennsylvania and California-saw a decline in underage e-cigarette posts. California’s decline is preceded by an increase in disguised posting, and Pennsylvania’s decline is accompanied by increased engagement for the underage posts. The authors also estimate impact of taxes on posting by race and gender. In the third chapter, the authors examine generative adversarial networks (GANs) as a privacy protecting approach to customer data transfer. As customer privacy becomes increasingly important to marketers, the authors investigate GANs ability to transfer a generative model, instead of data, thereby avoiding the process of sampling and anonymizing customer data for release for use in various analytic use cases. The authors find that GANs excel in preserving desired characteristics of original data and protecting privacy as compared to benchmarks. With real world data, the authors find that GANs achieve double the accuracy as compared to the best benchmark. Additionally, they demonstrate GANsin different marketing contexts of pricing for optimal profits, and customer targeting, and show that a individual GAN can handle multiple problems. Finally, they demonstrate volume and velocity advantages of GANs in handling larger data and real-time data streams.