2025 - June ASQ Orange Empire, Section 0701, Monthly Meeting

Alfredo will discuss the evolving role of data analytics in decision making and the intersection between marketing, and quality management. While the marketing profession has historically been associated with creativity and intuition, the rise of digital tools and analytics in the implementation of marketing campaigns has made data proficiency a must-have tool. Marketers and customer service professionals must now incorporate statistical methods to optimize performance and improve customer satisfaction. This may scare a good number of marketing professionals, however, integrating quality management principles and statistical tools such as statistical process control (SPC) is not overly complicated. This session will discuss how quality professionals can utilize their knowledge to help businesses make data-driven decisions to improve their marketing metrics.

Alfredo is a subject matter expert in marketing, sales, operations, and product management. He is a Partner at and , where he aims to help organizations grow using data-driven strategies, common-sense problem-solving, and best practices from other industries. Alfredo holds a degree in Economics from Pomona College and an MBA from The UCLA Anderson School. In addition to business consulting, he teaches at the College of Business Administration at Loyola Marymount University.

We analyze weekly U.S. prescription claims data over a 36-month period spanning the COVID-19 pandemic to investigate patterns of overconsumption of the antiparasitic drug Ivermectin (IVM). To quantify the overconsumption of IVM use following the heightened public attention on IVM as a candidate treatment for COVID-19, we adopt a causal framework based on synthetic controls, comparing IVM prescription trends to those of a large set of control medications. We employ a regularized synthetic control (RSC) method using continuous spike-and-slab shrinkage priors to estimate state-level deviations in IVM consumption. This approach offers decision-theoretic guarantees on predictive risk, supporting its application in downstream policy analysis at multiple-time points after the intervention. Its empirical robustness is demonstrated through extensive validation checks. Our findings reveal a modest increase in IVM prescriptions following early reports of its potential therapeutic use, with no significant surge over the subsequent eight months. This was followed by a pronounced increase that coincided with the peak in COVID-19 cases. Strikingly, elevated IVM use persisted even after COVID-19 vaccines became widely available and multiple federal countermeasures were implemented. Furthermore, we find that state-level political affiliation significantly explains variation in overconsumption, even after accounting for COVID-19 incidence rates. These results highlight deep regional disparities in the effectiveness of public health messaging and suggest a need for more targeted and trusted communication strategies.

Gourab is an Associate Professor of Data Sciences and Operations at the University of Southern California's Marshall School of Business. He received his Ph.D. in Statistics from Stanford University. His research interests span statistical theory and applications, with contributions to predictive inference, shrinkage methodology, and empirical Bayes theory. His work has developed efficient methods for large-scale estimation problems and has reported novel empirical phenomena in digital marketing, freemium business models, virology, and healthcare.

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