#23 Bayesian Stats in Business and Marketing Analytics, with Elea McDonnel Feit

If you’ve studied at a business school, you probably didn’t attend any Bayesian stats course there. Well this isn’t like that in every business schools! Elea McDonnel Feit does integrate Bayesian methods into her teaching at the business school of Drexel University, in Philadelphia, US. 

Elea is an Assistant Professor of Marketing at Drexel, and in this episode she’ll tell us which methods are the most useful in marketing analytics, and why.

Indeed, Elea develops data analysis methods to inform marketing decisions, such as designing new products and planning advertising campaigns. Often faced with missing, unmatched or aggregated data, she uses MCMC sampling, hierarchical models and decision theory to decipher all this.

After an MS in Industrial Engineering at Lehigh University and a PhD in Marketing at the University of Michigan, Elea worked on product design at General Motors and was most recently the Executive Director of the Wharton Customer Analytics Initiative.

Thanks to all these experiences, Elea loves teaching marketing analytics and Bayesian and causal inference at all levels. She even wrote the book R for Marketing Research and Analytics with Chris Chapman, at Springer Press.

In summary, I think you’ll be pretty surprised by how Bayesian the world of marketing is…

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

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Om Podcasten

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Estonia. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love election forecasting and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and unlock exclusive Bayesian swag on Patreon!