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

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

FromLearning Bayesian Statistics


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

FromLearning Bayesian Statistics

ratings:
Length:
59 minutes
Released:
Sep 10, 2020
Format:
Podcast episode

Description

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/ (https://bababrinkman.com/) !
Links from the show:
Elea's website: http://eleafeit.com/ (http://eleafeit.com/)
R for Marketing Research and Analytics: http://r-marketing.r-forge.r-project.org/ (http://r-marketing.r-forge.r-project.org/)
Elea's Tutorials & Online Courses: http://eleafeit.com/teaching/ (http://eleafeit.com/teaching/)
Elea on Twitter: https://twitter.com/eleafeit (https://twitter.com/eleafeit)
Elea on GitHub: https://github.com/eleafeit (https://github.com/eleafeit)
Tutorial on Conjoint Analysis in R: https://github.com/ksvanhorn/ART-Forum-2017-Stan-Tutorial (https://github.com/ksvanhorn/ART-Forum-2017-Stan-Tutorial)
Test & Roll app: https://testandroll.shinyapps.io/testandroll/ (https://testandroll.shinyapps.io/testandroll/)
Test & Roll Paper -- Profit-Maximizing A/B Tests: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3274875 (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3274875)
Principal Stratification for Advertising Experiments: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3140631 (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3140631)
CausalImpact R package: https://google.github.io/CausalImpact/CausalImpact.html (https://google.github.io/CausalImpact/CausalImpact.html)
Chapter on Data Fusion in marketing: https://link.springer.com/referenceworkentry/10.1007/978-3-319-05542-8_9-1 (https://link.springer.com/referenceworkentry/10.1007/978-3-319-05542-8_9-1)
Statistical Analysis with Missing Data (Little & Rubin): https://onlinelibrary.wiley.com/doi/book/10.1002/9781119013563 (https://onlinelibrary.wiley.com/doi/book/10.1002/9781119013563)
R-Ladies Philly YouTube channel: https://www.youtube.com/channel/UCPque9BaFV9p0hcgImrYBzg (https://www.youtube.com/channel/UCPque9BaFV9p0hcgImrYBzg)

Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.


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Released:
Sep 10, 2020
Format:
Podcast episode

Titles in the series (100)

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 Paris. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (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 https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (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 https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy