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!
Timestamps for: Thomas Wiecki - Solving Real-World Business Problems with Bayesian Modeling | PyData London 2022
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!