Your Marketing Mix Model is only as good as the library you build it on. But how do you choose between PyMC-Marketing and Google Meridian when the feature lists look so similar? You need hard evidence, not marketing claims. Which library is actually faster on multi-geo data? Do their different statistical approaches (splines vs. Fourier series) lead to different budget decisions?

This talk delivers that evidence. We present a rigorous, open-source benchmark that stress-tests both libraries on the metrics that matter in production. Using a synthetic dataset that replicates real-world ad spend patterns, we measure:

• Speed: Effective sample size per second (ESS/s) across different data scales.
• Accuracy: How well each model recovers both sales figures and true channel contributions.
• Reliability: A deep dive into convergence diagnostics and residual analysis.
• Resources: The real memory cost of fitting these models.

You'll walk away from this session with a clear, data-driven verdict, ready to choose the right tool and defend that choice to your team.

In a rapidly evolving advertising landscape where data, technology, and methodology converge, the pursuit of rigorous yet actionable marketing measurement is more critical—and complex—than ever. This talk will showcase how modern marketers and applied data scientists employ advanced measurement approaches—such as Marketing Mix Modeling (frequentist and Bayesian) and robust experimental designs, including randomized control trials and synthetic control-based counterfactuals—to drive causal inference in advertising effectiveness for meaningful business impact.

The talk will also address emergent aspects of applied marketing science- namely open-source methodologies, digital commerce platforms and artificial intelligence usage. Innovations from industry giants like Google and Meta, as well as open-source communities exemplified by PyMC-Marketing, have democratized access to advancement in methodologies. The emergence of digital commerce platforms such as Amazon and Walmart and the rich data they bring forward is transforming how customer journeys and campaign effectiveness are measured across channels. Artificial Intelligence is accelerating every facet of the data science workflow, streamlining processes like coding, modeling, and rapid prototyping (“vibe coding”) to enabling the integration of neural networks and deep learning techniques into traditional MMM toolkits. Collectively, these provide new and easy ways of quick experimentation and learning of complex nonlinear dynamics and hidden patterns in marketing data

Bringing these threads together, the talk will show how Ovative Group—a media and marketing technology firm—integrates domain expertise, open-source solutions, strategic partnerships, and AI automation into comprehensive measurement solutions. Attendees will gain practical insights on bridging academic rigor with business relevance, empowering careers in applied data science, and helping organizations turn marketing analytics into clear, actionable strategies.

Centraal Beheer werkt al meer dan 10 jaar met verschillende attributie modellen. Tijdens deze sessie neemt Tjaard je mee in de Marketing Attributie reis van Centraal Beheer. Wat zijn de lessons learned in de afgelopen 10 jaar? En wat zijn mogelijke valkuilen? Waar moet je beginnen? En hoe maak je de 'juiste' keuze in de totstandkoming van je modellen? Verschillende technieken komen aan bod, waaronder multi-touch attributiemodellen (MTA), marketing- of media-mix modellen (MMM) en experimenten, en we onderzoeken hoe deze methoden kunnen bijdragen aan het optimaliseren van marketingstrategieën. Een aantal mythes over Marketing Attributie zullen worden ontkracht en er wordt ingegaan op het belang van data, kennis en context bij het bouwen van effectieve modellen.

Synthetic data: it's a fascinating topic that sounds like science fiction but is rapidly becoming a practical tool in the data landscape. From machine learning applications to safeguarding privacy, synthetic data offers a compelling alternative to real-world datasets that might be incomplete or unwieldy. With the help of Winston Li, founder of Arima, a startup specializing in synthetic data and marketing mix modelling, we explore how this artificial data is generated, where its strengths truly lie, and the potential pitfalls to watch out for! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

This talk dives into leveraging GenAI to scale sophisticated decision intelligence. Learn how an AI copilot interface simplifies running complex Bayesian probabilistic models, accelerating insight generation, and accurate decision making at the enterprise level. We talk through techniques for deploying AI agents at scale to simulate market dynamics or product feature impacts, providing robust, data-driven foresight for high-stakes innovation and strategy directly within your Databricks environment. For marketing teams, this approach will help you leverage autonomous AI agents to dynamically manage media channel allocation while simulating real-world consumer behavior through synthetic testing environments.

How can engineers empower marketing teams in the post-cookie era? Discover Bayesian Media Mix Modelling (MMM), a robust data science approach to evaluate multi-channel marketing effectiveness. Learn how to implement MMM and take actionable insights back to your company.

How to judge the performance of marketing campaigns, who shall get the credit and how to allocate budgets? For many years Attribution was a hot topic and many questions and discussions were about how to best attribute conversions. Is this the best solution or are there others such as Marketing Mix Modelling to increase media efficiency?

udging by the number of inbound pitches we get from PR firms, AI is absolutely going to replace most of the work of the analyst some time in the next few weeks. It's just a matter of time until some startup gets enough market traction to make that happen (business tip: niche podcasts are likely not a productive path to market dominance, no matter what Claude from Marketing says). We're skeptical. But that doesn't mean we don't think there are a lot of useful applications of generative AI for the analyst. We do! As Moe posited in this episode, one useful analogy is that thinking of using generative AI effectively is like getting a marketer effectively using MMM when they've been living in an MTA world (it's more nuanced and complicated). Our guest (NOT from a PR firm solicitation!), Martin Broadhurst, agreed: it's dicey to fully embrace generative AI without some understanding of what it's actually doing. Things got a little spicy, but no humans or AI were harmed in the making of the episode. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Hear how JCDecaux, the market leader in out-of-home advertising, utilize advanced analytics to drive brand growth and maximize marketing ROI for clients.

You’ll learn how JCDecaux:

• Drive sales uplift: Use Alteryx to analyse data from Nielsen to drive +18% sales uplift for advertisers near Tesco and Waitrose stores, with an 80% reduction in analysis time.

• Boost ROI: Enhance marketing mix modelling accuracy, resulting in +42% ROI for out of home campaigns

• Leverage AI tools: To identify patterns in marketing results data, optimize results in real time and make more strategic budget decisions.

• Increase brand awareness: Use data to measure the impact of out of home advertising across different geographies, resulting in +19% brand awareness uplift

For those who celebrate or acknowledge it, Christmas is now in the rearview mirror. Father Time has a beard that reaches down to his toes, and he's ready to hand over the clock to an absolutely adorable little Baby Time when 2024 rolls in. That means it's time for our annual set of reflections on the analytics and data science industry. Somehow, the authoring of this description of the show was completely unaided by an LLM, although the show did include quite a bit of discussion around generative AI. It also included the announcement of a local LLM based on all of our podcast episodes to date (updated with each new episode going forward!), which you can try out here! The discussion was wide-ranging beyond AI: Google Analytics 4, Marketing Mix Modelling (MMM), the technical/engineering side of analytics versus the softer skills of creative analytical thought and engaging with stakeholders, and more, as well as a look ahead to 2024! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

In this talk I will present two new open-source packages that make up a powerful and state-of-the-art marketing analytics toolbox. Specifically, PyMC-Marketing is a new library built on top of the popular Bayesian modeling library PyMC. PyMC-Marketing allows robust estimation of customer acquisition costs (via media mix modeling) as well as customer lifetime value. In addition, I will show how we can estimate the effectiveness of marketing campaigns using a new Bayesian causal inference package called CausalPy. The talk will be applied with a real-world case-study and many code examples. Special emphasis will be placed on the interplay between these tools and how they can be combined together to make optimal marketing budget decisions in complex scenarios.

Multi-touch attribution, media mix modeling, matched market testing. Are these the three Ms of marketing measurement (Egad! The alliteration continues!)? Seriously. What's with all the Ms here? Has anyone ever used experimentation to build a diminishing return curve for the impact of a media measurement technique based on how far along in the alphabet the letter of that technique is? Is "M" optimal?! Trust us. You will look back on this description after listening to this episode with John Wallace from LiftLab and find it… at least mildly amusing. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We talked about:

Juan’s background Typical problems in marketing that are solved with ML Attribution model Media Mix Model – detecting uplift and channel saturation Changes to privacy regulations and its effect on user tracking User retention and churn prevention A/B testing to detect uplift Statistical approach vs machine learning (setting a benchmark) Does retraining MMM models often improve efficiency? Attribution model baselines Choosing a decay rate for channels (Bayesian linear regression) Learning resource suggestions Bayesian approach vs Frequentist approach Suggestions for creating a marketing department Most challenging problems in marketing The importance of knowing marketing domain knowledge for data scientists Juan’s blog and other learning resources Finding Juan online

Links: 

Juan's PyData talk on uplift modeling: https://youtube.com/watch?v=VWjsi-5yc3w Juan's website: https://juanitorduz.github.io Introduction to Algorithmic Marketing book: https://algorithmic-marketing.online Preventing churn like a bandit: https://www.youtube.com/watch?v=n1uqeBNUlRM

MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

Every marketer wants to accurately measure the impact of their advertising spend. And “digital” was supposed to make that really easy (especially for digital advertising). But, that promise has rarely been realized---it’s becoming increasingly difficult to track users across touchpoints, thanks to privacy regulations (GDPR, CCPA, etc.) and browser updates that block or aggressively expire cookies. In this session, we will review four different approaches to marketing attribution: heuristic modeling (first touch, last touch, linear, time decay, etc.), algorithmic modeling, media mix modeling (MMM), and randomized controlled trials (RCTs). As part of the review, we will venture lightly, but profoundly, into some foundational statistical concepts: we WILL use the terms 'counterfactual' and 'potential outcome,' and probably even 'unobserved heterogeneity!'

Hey there, mister. That's a mighty nice multi-touch attribution model you're using there. It would be a shame to see it get mixed up with a media model. Or... would it? What happens if you think about media mix models as a tool that can be combined with experimentation to responsibly measure the incrementality of your marketing (while also still finding a crust of bread in the corner for so-called "click attribution")? According to a 2019 paper published by ThirdLove (which happens to have been Michael's last call on our last episode), that's a pretty nice way to go, and we thought it would be fun to see if we could raise Tim's blood pressure by giving him something to vigorously agree with for once. It was. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.