[Online] Bayesian Methods in Modern Marketing Analytics

🎙️ Speaker: Juan Orduz, Thomas Wiecki | ⏰ Time: 9am PT / 12pm ET / 6pm Berlin

During the webinar, we will discuss some of the most crucial topics in marketing analytics: media spend optimization through media mix models and experimentation, and customer lifetime value estimation. We will approach these topics from a Bayesian perspective, as it gives us great tools to have better models and more actionable insights. We will take this opportunity to describe our join with PyMC Labs in open-sourcing some of these tools in our brand-new pymc-marketing Python package.

📜 Outline of Talk / Agenda:

• 5 min: Intro to PyMC Labs and speakers
• 45 min: Presentation, panel discussion
• 10 min: Q&A

💼 About the speaker:

1. Juan Orduz
   Mathematician (Ph.D. Humboldt Universität zu Berlin) and data scientist. Interested in interdisciplinary applications of mathematical methods. In particular, time series analysis, Bayesian methods, and causal inference. Currently, working in marketing data science projects such as media mix modeling, customer lifetime value estimation and experimentation.

🔗 Connect with Juan Orduz:

• 👉 LinkedIn: https://www.linkedin.com/in/juanitorduz/
• 👉 Twitter: https://twitter.com/juanitorduz
• 👉 GitHub: https://github.com/juanitorduz
• 👉 Website: https://juanitorduz.github.io/

2. Thomas Wiecki (PyMC Labs)
   Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience.

🔗 Connect with Thomas Wiecki:

• 👉 GitHub: https://github.com/twiecki
• 👉 Twitter: https://twitter.com/twiecki
• 👉 Website: https://twiecki.io/

📖 Code of Conduct:
Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Learn more about pymc-marketing:
🐙 GitHub: https://github.com/pymc-labs/pymc-marketing
📝 Documentation: https://www.pymc-marketing.io/en/stable/

🔗 Connecting with PyMC:
💬 Q&A/Discussion: https://discourse.pymc.io
🐙 GitHub: https://github.com/pymc-devs/pymc
🐦 Twitter: https://twitter.com/pymc_devs
💼 LinkedIn: https://www.linkedin.com/company/pymc/mycompany
📺 YouTube: https://www.youtube.com/c/PyMCDevelopers