Discover the innovative application of Bayesian methods in the realm of modern marketing analytics. This article offers a fresh perspective on how these advanced techniques are reshaping the landscape of data-driven marketing strategies.
Bayesian methods have gained significant popularity in modern marketing analytics due to their ability to handle uncertainty, incorporate prior knowledge, and make accurate predictions. Unlike traditional statistical approaches, Bayesian methods provide a flexible framework that enables marketers to make data-driven decisions by combining observed data with prior beliefs or assumptions.
In this webinar we 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 approach these topics from a Bayesian perspective, as it gives us great tools to have better models and more actionable insights. We 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 PyMC Marketing.
00:00 Welcome
02:03 Webinar starts
02:32 Webinar's objective
03:04 Outline
04:05 Applied Data Science
05:12 Bayesian Methods
06:49 Geo-Experimentation
08:27 Time-Based Regression
10:26 Regression model in PyMC
12:04 Marketing measurement
13:34 Media Transformations (Carryover (Adstock) & Saturation)
15:50 Media Mix Model Target
16:24 MMM Structure
16:53 Media Contribution Estimation
17:13 Budget Optimization
18:18 PyMC-Marketing
19:25 PyMC-Marketing- More MMM Flavours
20:00 Customer Lifetime Value (CLV)
21:47 Continuous Non-Contractractual CLV
22:57 CLV Estimation Strategy
24:31 BG/NBD Assumptions
27:14 BG/NBD Parameters
27:50 BG/NBD Probability of Alive
28:40 Gamma-Gamma Model
29:12 BG/NBD Hierarchical Models
31:14 Causal Inference (Synthetic control)
32:10 Causal Inference (Difference-in-Differences and Regression Discontinuity)
32:39 Instrumental Variables
34:46 Cohort Revenue-Retention Modelling
38:21 Retention and Revenue component
41:02 Cohort Revenue-Retention Model
42:34 Revenue-Retention Predictions
43:11 References
44:25 Connect with PyMC Labs
44:50 Marketing analytics strategy consultation
47:36 PyMC Applied Workshop
48:58 Q/A There are so many parameters in MMM which are not identifiable ...
53:00 Q/A In the MMM how do you encode categorical control variables?
54:10 Q/A How to deal with latent variables?
57:34 Q/A If you observe the baseline uplift...How do you measure it in a Media mix model...?
59:15 Q/A How does it solve the cold start problem?
Curious how these tools can be most effectively used in your particular situation? In this free 30-minute strategy consultation with us, we will:
If you are interested in seeing what we at PyMC Labs can do for you, then please email info@pymc-labs.com. We work with companies at a variety of scales and with varying levels of existing modeling capacity. We also run corporate workshop training events and can provide sessions ranging from introduction to Bayes to more advanced topics.