Bayesian Methods in Modern Marketing Analytics with Juan Orduz

Bayesian Methods in Modern Marketing Analytics (Juan Orduz) ## Event Description 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 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 (https://www.pymc-marketing.io/en/stable/) #BayesianMethods #marketinganalytics #datadrivenmarketing #ProbabilisticModeling #MarketingDecisionMaking #StatisticalAnalysis #abtesting #PredictiveModeling #customersegmentation #MarketingOptimization #CampaignEffectiveness #AttributionModeling #BayesianInference #ConjointAnalysis #MultivariateTesting #BayesianNetworks #PriorInformation #BayesianRegression #MarketingScience We have a special offer: * Free 30-minute strategy consultation * In-depth review of your current marketing analytics pipeline * Create a tailored roadmap to bring your analytics to the next level Sign up for your free marketing strategy consultation here: https://calendly.com/twiecki/bayes ## Resources - pymc-marketing Python package: https://www.pymc-marketing.io/en/stable/ - Slides: https://juanitorduz.github.io/html/marketing_bayes.html#/title-slide About the Speakers Dr. Juan Camilo 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/ Dr. 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/ ## Connecting with PyMC Labs - LinkedIn: https://www.linkedin.com/company/pymc-labs/ - Twitter: https://twitter.com/pymc_labs - YouTube: https://www.youtube.com/c/PyMCLabs - Meetup: https://www.meetup.com/pymc-labs-online-meetup/ ## Timestamps 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? ## PyMC Labs - PyMC Labs: https://www.pymc-labs.io #bayesian #statistics #python