Agentic measurement is both overhyped and genuinely useful — when handled correctly. This is the workflow I run my own MMMs, attribution and experiments through, with a coding agent in the loop and the output good enough to put in front of a CFO.
Nine self-paced modules · 11+ hours of content. Not “prompt better and the agent writes the MMM” — a workflow.
The agent spits out a Meridian model in twenty minutes. It looks plausible. You ship it. The CFO finds a leakage you didn’t catch, and you’re back to defending a number you don’t actually understand. The model is fine — your relationship to it isn’t.
You don’t trust the agent, so you do everything by hand. Two weeks per MMM. You miss the next experiment cycle. The “we can’t move that fast” meetings start.
The Agentic Measurement Loop is the path between. The agent owns the implementation. You own the structure, priors, diagnostics, and readout. Learn → Replay → Verify → Capture → Encode → Eval. That is the entire course in one sentence.
You know adstock, saturation, and Meridian. You have not yet figured out how to run the workflow through an agent without giving up the rigor that makes the output defensible.
You can model. You are now expected to ship measurement work with an AI assistant, and you want a project setup that works on day one instead of three weeks of trial and error.
Small team, large ad budget, growing list of asks. You need a repeatable agentic loop for EDA, MMM, and experiments so one person can credibly cover the work.
The Agentic Measurement Loop turns ad-hoc prompting into a repeatable workflow your team can rely on — the same six stages on every dataset, every model, every readout.
Install project instructions that define good marketing-measurement work before the first prompt.
Run EDA, MMM, Meridian, experiment, and attribution workflows on realistic marketing data.
Use grading harnesses and review checks to catch wrong columns, broken grain, weak diagnostics, and bad claims.
Keep the prompts, notebooks, eval scripts, and templates for the next dataset or team process.
Reviews of Gui’s teaching — from his live Marketing Science Bootcamp on Maven and readers of his book, Data Analytics for Marketing. This course brings that same teaching to the agentic workflow layer.
Students come from
Same teaching, now packaged for the agentic workflow layer.
Enroll now
Led measurement for €100M+ in ad spend at Kindred Group & Just Eat Takeaway. Author of Data Analytics for Marketing (Packt, 2024). Teaches the Marketing Science Bootcamp on Maven — 5.0★.
Across in-house roles and enterprise clients at Pinemarsh Consulting, Gui has built measurement models from scratch and managed the vendors who sell them — both perspectives are taught here. This course is the operating model he runs his own measurement work on, every week.
“The job is not ‘use the agent more.’ The job is to set up the agent so the work it produces survives a peer review on the first read. This course teaches that setup, in marketing measurement, end to end.”
Previously at
Nine modules take you from first agent setup to reusable measurement workflows. The first five are available now; the final four follow on the 15th of July.
Available now
You can set up AGENTS.md or CLAUDE.md for marketing analytics, pick the right harness for the job, and validate agent output without trusting it blindly.
The operating model behind agentic measurement work, what project instructions actually do, and a side-by-side comparison of Claude Code, Codex, and pi.dev. Includes the seven-question validation checklist you will reuse in every later module.
60 min video, project-instructions template
Available now
You can hand an agent a fresh marketing dataset and get back an EDA you would defend in a stand-up.
Have an agent profile a marketing dataset end to end: column roles, time grain, missingness, seasonality, structural redundancy. Self-grading harness catches the mistakes agents most often make on real marketing data.
90 min video, graded notebook
Available now
You can ship an OLS MMM in an afternoon and explain every parameter choice the agent made.
Fit the simplest credible MMM, national-aggregate OLS, and read the diagnostics honestly enough to reject it. The rejection is the bridge to Meridian: it writes down the structural hypotheses (seasonality, trend, per-region) that hierarchical priors answer. You also ship your first reusable skill file.
90 min video, graded notebook
Available now
You can prepare data for Meridian, get a fit that converges, and produce a diagnostics-clean readout.
Fit a Meridian MMM with agent assistance: InputData construction, ModelSpec and priors, MCMC sampling, convergence diagnostics, ROI and response curves. Meridian for the agent-friendly API surface; PyMC-Marketing notes included.
75 min video, graded notebook
Available now
You can take a CMO question and return a power-analysed experiment design plus a defensible readout.
Design and analyse marketing experiments with an agent in the loop: hypothesis, estimand, power analysis, MDE, geo-lift design, readout. Bridges back to MMM as the calibration signal.
90 min video, graded notebook
Available now
You can produce an attribution read, state its assumptions, and explain where it can and cannot be trusted.
Build a defensible attribution read with an agent, from multi-touch and data-driven attribution to its limits, and why it disagrees with your MMM. The agent assembles the pipeline; you adjudicate which signal to trust for which decision.
75 min video, graded notebook
Available now
You can reconcile three measurement methods into one defensible recommendation.
Bring MMM, attribution, and experiments into one coherent story. Use experimental lift to calibrate MMM priors, and triangulate a recommendation when the three methods disagree.
60 min video, graded notebook
Available now
You can ship an eval suite that flags when an agent’s measurement output drifts or breaks.
Build evals so your agentic measurement workflow stays correct as data, models, and prompts change. Turn the validation checklist into automated checks that run every time.
60 min video, graded notebook
Available now
You can roll out a consistent agentic measurement workflow across a team, not just your own machine.
Turn your personal agentic setup into something a team can run. Package project instructions, skills, and evals so measurement work is consistent across people and repos.
60 min video, templates
One purchase, all nine modules, every future update included. No upsell email later.
All nine modules live. $899.
Enroll nowLess than half what the live Marketing Science Bootcamp on Maven runs ($1,249), for content the cohort doesn’t cover (the agentic workflow). And a rounding error next to a single $25k MMM engagement the course helps you deliver. All nine modules are live, so the price is $899. Every future module is included, no upsell later.
Through 14th of June
$539
40% below the $899 full price · 1:1s for the first 20
Closed
Open to everyone, no seat cap, the lowest price this course was offered. The first 20 buyers also got 1:1 time with Gui.
15th of June to 14th of July
$674
25% below the $899 full price
Closed
First 5 modules are live the day you buy; the final 4 ship 15th of July. The self-paced bundle without the founding 1:1s.
From 15th of July
$899
All 9 modules shipped
Active right now
Everything is live. No calendar to track — the complete, self-paced course.
Every drop on this list is included in the active price. No surprise upsells.
What you’re paying for, anchored honestly: the live cohort runs at $1,249. A single hour with Gui as a consultant exceeds a quarter of the founding price. One good budget reallocation the course teaches you to defend is worth more than the course, many times over. We don’t do “Total value $24,000” stickers — anchor it yourself.
Refund: a standard 14-day window from purchase, no questions.
Most learners expense this. It is professional training that pays for itself the first time you defend a budget decision with it, and plenty of people get it approved even without a formal training budget. We have written the approval email for you, fill in the brackets and send it to your manager.
The full course $899: all 9 modules live now, lifetime access, certificate, and private Discord. A standard 14-day refund from purchase applies.
No. All nine modules are live and $899 is the full price. Anyone who bought earlier keeps every module, no upsell.
No. There is no seat cap at any tier, buy as a team if you like. The only thing ever limited was the founding 1:1s, the first 20 founding buyers, and the founding tier closed on 14th of June.
A standard 14-day refund window from purchase, no questions, no forms, just email.
Yes, and most learners do. This is professional training, so it usually goes through a learning and development or team budget, and plenty of people get it approved even without a formal one. The "Ask your company to pay for you" section above has a ready-to-send email template: copy it, fill in the brackets, and send it to your manager.
Five modules go live on 15th of June: Agentic Foundations, Agentic EDA, Building MMMs with an Agent, Bayesian MMMs in Meridian, and Experimentation with an Agent. The final four — Attribution, Calibration & Triangulation, Evals, and Reusable Team Workflows — ship on 15th of July. Both dates are public, so you can hold the calendar to this page.
Two reasons. Honest one: it lets the product start delivering sooner instead of waiting for everything to be recorded. Pedagogical one: most learners cannot consume ten hours of dense MMM video in one weekend. The split lets you apply the first five modules on your own data before the rest land.
$899, the full price now that all nine modules are live. Earlier buyers keep every module they were promised, no upsell email.
Yes. This course assumes you have built a Marketing Mix Model before, or at least know what adstock, saturation, contribution, and DECOMP.RSSD mean. If you do not, start with Gui’s live Marketing Science Bootcamp on Maven first.
You need access to at least one of them to follow along. The course shows how to set up project instructions for all three so you can pick the harness that fits your workflow and budget. None of the three are bundled with the course.
Every module ships with a self-grading harness for the agent’s output and a seven-question validation checklist you can rerun manually. The validation habit is the spine of the course, not an afterthought — module 08 turns it into automated evals.
The course uses a synthetic multi-geo marketing dataset that mirrors the shape of real spend, revenue, and seasonality data. Practitioner-scenario exercises include messy variants so you have practice before you point an agent at your own data.
Guilherme Diaz-Berrio — author of Data Analytics for Marketing (Packt, 2024), 15+ years in marketing analytics, and instructor of the Marketing Science Bootcamp on Maven (rated 5.0 from 7 reviews). He has led measurement for €100M+ in ad spend.
Build defensible EDA, MMM, experiment, attribution, and calibration workflows with an agent in the loop. All nine modules are in your library the day you buy, no upsell later.