Thomas Wiecki - Solving Real-World Business Problems with Bayesian Modeling | PyData London 2022 скачать в хорошем качестве

Thomas Wiecki - Solving Real-World Business Problems with Bayesian Modeling | PyData London 2022

Thomas Wiecki Presents: Solving Real-World Business Problems with Bayesian Modeling Among Bayesian early adopters, digital marketing is chief. While many industries are embracing Bayesian modeling as a tool to solve some of the most advanced data science problems, marketing is facing unique challenges for which this approach provides elegant solutions. Among these challenges are a decrease in quality data, driven by an increased demand for online privacy and the imminent "death of the cookie" which prohibits online tracking. In addition, as more companies are building internal data science teams, there is an increased demand for in-house solutions. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 0:05 Speaker introduction and PyMC 4 release announcement 1:15 PyMC Labs- The Bayesian consultancy 2:39 Why is marketing so eager to adopt Bayesian solutions 3:49 Case Study: Estimating Marketing effectiveness 6:00 Estimating Customer Acquisition Cost (CAC) using linear regression 7:36 Drawbacks of linear regression in estimating CAC 10:02 Blackbox Machine learning and its drawbacks 11:27 Bayesian modelling 11:52 Advantages of Bayesian modelling 14:12 How does Bayesian modelling work? 16:53 Solution proposals(priors) 17:26 Model structure 19:57 Evaluate solutions 20:16 Plausible solutions(posterior) 22:36 Improving the model 23:38 Modelling multiple Marketing Channels 24:51 Modelling channel similarities with hierarchy 26:13 Allowing CAC to change over time 28:00 Hierarchical Time Varying process 30:05 Comparing Bayesian Media Mix Models 30:47 What-If Scenario Forecasting 31:53 Adding other data sources as a way to help improve or inform estimates 33:00 When does Bayesian modelling work best? 33:35 Intuitive Bayes course 34:38 Question 1: Effectiveness of including variables seasonality? 36:03 Question 2: What is your recommendation for the best way to choose priors? 38:16 Question 3: How to test if an assumption about the data is valid? 39:07 Question 4: Do you take the effect of different channels on each other into account? 41:33 Thank you!