Roadmaps / Marketing Discipline
▲ Marketing Discipline

AnalyticsMeasurement & Attribution

Measure marketing the AI-native way: clean tracking, honest attribution, dashboards that drive decisions, and rigorous experiments. Let AI query and summarize the data — keep the judgment about what it means for yourself.

11 Modules 33 Topics ~44 Hours
Prove this skill → Beat Claude on the Analytics challenge
The Path — click any step to jump in
01 Analytics Foundations & GA4 B ✓
02 Tracking & Data Collection B ✓
03 Modern Data Stack & Pipelines I ✓
04 Attribution & Incrementality I ✓
05 Marketing Mix Modeling (MMM) I ✓
06 Cohort, Retention & LTV Analysis I ✓
07 Dashboards & Reporting I ✓
08 Experimentation & A/B Testing A ✓
09 AI for Analytics A ✓
10 Product & Behavioral Analytics A ✓
11 Data Governance, Quality & Trust A ✓
Level:
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Module 1 beginner
Analytics Foundations & GA4
Build the core vocabulary of web analytics and a working mental model of GA4 before you touch a dashboard.
3 topics 4 hours
+
Core Metrics & Funnels
The handful of metrics that actually describe a business, and how they connect into a funnel.
AI-Proof
⚡ AI Workflow
Ask an LLM to define and contrast metrics like sessions, conversion rate, and CAC, then sanity-check the definitions against your own data — the model is a fast tutor, but which metrics matter for your business is your call.
Occam's Razor by Avinash Kaushik → Kill Useless Web Metrics: The "So What" Test → Amplitude: Funnel Analysis →
GA4 Mental Model
How GA4's event-based model replaced sessions-and-pageviews thinking.
Augmented
⚡ AI Workflow
Have an LLM explain GA4's event/parameter schema and translate a Universal Analytics concept into its GA4 equivalent, then verify in your own property — the explanation orients you, the live data confirms it.
GA4: Event parameters (Analytics Help) → GA4: Get started with Explorations → GA4: Recommended events (Google for Developers) →
Reading Reports Without Fooling Yourself
The interpretation discipline that separates insight from noise.
AI-Proof
⚡ AI Workflow
Have AI restate what a report does and doesn't support and flag where a number might mislead, then make the call on what's actually true — the model can caveat the data, but resisting a convenient conclusion is human discipline.
GA4: Get started with anomaly detection → CXL: A/B Testing Statistics — An Easy-to-Understand Guide → Occam's Razor: Web Metrics Demystified →
Module 2 beginner
Tracking & Data Collection
Capture clean, trustworthy data at the source so everything downstream can be believed.
3 topics 4 hours
+
Events & Conversion Tracking
Instrumenting the actions that matter so you can measure outcomes, not just traffic.
Automatable
⚡ AI Workflow
Generate the dataLayer pushes, gtag calls, and GTM tag configs from a plain-English description of the action, then test every fire in real-time debug view before trusting it.
GA4: Conversions vs. key events (Analytics Help) → GA4: Create or modify key events → Google Tag Manager: Server-side documentation →
UTMs & Campaign Tagging
The naming discipline that makes every click attributable.
Automatable
⚡ AI Workflow
Use an LLM to build and enforce a UTM convention and generate tagged URLs in bulk, then keep a single source-of-truth spreadsheet so humans don't drift from the standard.
GA4: Collect campaign data with custom URLs (Analytics Help) → Google Campaign URL Builder → GA4: Default channel groups (Analytics Help) →
Server-Side & Privacy-Safe Tracking
Collecting durable data as cookies, consent, and ad blockers erode the client side.
Augmented
⚡ AI Workflow
Ask AI to outline a server-side tagging architecture and consent-mode setup for your stack, then have an engineer review — the model drafts the design, humans own the privacy and compliance call.
An introduction to server-side tagging (Google for Developers) → Implement consent mode with server-side Tag Manager → Set up consent mode on websites (Tag Platform) →
Module 3 intermediate
Modern Data Stack & Pipelines
Understand the warehouse-centric stack that turns scattered marketing data into one queryable source of truth.
3 topics 4 hours
+
Warehouses & the Modern Stack
The shift from siloed tools to a central warehouse that every report and model reads from.
Augmented
⚡ AI Workflow
Ask AI to map your current tools onto a warehouse-centric architecture and explain where each piece fits, then make the build-vs-buy and platform calls yourself — the model sketches the stack, you own the infrastructure decision.
Google Cloud: What is a data warehouse? → Google Cloud: BigQuery introduction → Fivetran: What is ELT? →
GA4 → BigQuery Export
Getting raw, unsampled event data out of GA4 and into a warehouse you can query freely.
Automatable
⚡ AI Workflow
Have AI write the SQL to flatten and unnest the GA4 export schema and draft the BigQuery queries you need, then verify the row counts and event logic against the GA4 UI before trusting them — the joins are automatable, the reconciliation is human.
GA4: BigQuery Export (Analytics Help) → GA4: Link a property to BigQuery (Analytics Help) → Google Cloud: BigQuery introduction →
ETL/ELT & Transformation with dbt
Turning raw, messy event tables into clean, documented models the whole team can trust.
Augmented
⚡ AI Workflow
Use AI to scaffold dbt models, tests, and documentation from a description of the table you want, then review the SQL grain, joins, and test coverage before it ships — the model writes the boilerplate, you own whether the logic is correct.
dbt: What is dbt? (Introduction) → dbt: About SQL models → Fivetran: What is ELT? →
Module 4 intermediate
Attribution & Incrementality
Move past last-click fiction toward an honest account of what actually drove results.
3 topics 4 hours
+
Attribution Models Compared
How each model assigns credit, and why the choice changes your conclusions.
AI-Proof
⚡ AI Workflow
Have an LLM run the same conversion path through first-touch, last-touch, linear, and time-decay logic to show how credit shifts, then decide which view fits your decision — the math is automatable, the choice is strategic.
GA4: Get started with attribution (Analytics Help) → Google Ads: About data-driven attribution →
The Incrementality Mindset
Asking not "what got credit" but "what would have happened anyway."
AI-Proof
⚡ AI Workflow
Use AI to design a holdout or geo-test structure and crunch the lift math, then own the harder judgment of what counts as a credible counterfactual — the model computes the delta, you decide whether it's real.
Recast: GeoLift Incrementality Experiments → Recast Knowledge Base: Incrementality Experiments → Right Side Up: Guide to Marketing Incrementality Testing →
Multi-Touch vs. Data-Driven
The promise and limits of algorithmic attribution.
Augmented
⚡ AI Workflow
Use AI to summarize what a data-driven model is rewarding across your paths, then probe its blind spots yourself — the model surfaces patterns, but it can't see channels it never tracked.
GA4: About data-driven attribution (Analytics Help) → Recast: Integrating MMM with Multi-Touch Attribution → Haus: MTA vs. MMM — Choosing Between Them →
Module 5 intermediate
Marketing Mix Modeling (MMM)
Measure whole-portfolio impact with top-down statistical models built for a privacy-first, signal-loss world.
3 topics 4 hours
+
Why MMM, and Why Now
The top-down measurement method that came back as cookies, IDs, and click tracking eroded.
AI-Proof
⚡ AI Workflow
Ask AI to explain how MMM differs from user-level attribution and where each method is appropriate, then decide whether your spend and data history justify an MMM at all — the model frames the trade-offs, the strategic fit is your call.
Recast: What is Marketing Mix Modeling? → Measured: What is Marketing Mix Modeling? → Haus: MTA vs. MMM — Choosing Between Them →
Inside the Model — Adstock & Saturation
The two ideas that make MMM more than a naive regression of spend on sales.
Augmented
⚡ AI Workflow
Use AI to explain adstock (carryover) and diminishing-returns curves and to prototype the model in code, then scrutinize the priors and fitted curves for business plausibility — the model fits the math, you check that the shape matches reality.
Meta: Robyn — open-source MMM → Recast: Guide to Marketing Mix Modeling → Google Cloud: BigQuery ML time series forecasting (ARIMA_PLUS) →
Validating & Acting on MMM
Turning a model's coefficients into budget decisions you can defend.
AI-Proof
⚡ AI Workflow
Have AI summarize a model's channel contributions and proposed budget reallocation, then validate it against incrementality experiments and your own intuition before moving money — the model recommends, you own the spend.
Haus: MTA vs. MMM — Choosing Between Them → Recast: Integrating MMM with Multi-Touch Attribution → Right Side Up: Guide to Marketing Incrementality Testing →
Module 6 intermediate
Cohort, Retention & LTV Analysis
Measure how groups of customers behave over time, so you optimize for durable value instead of one-time conversions.
3 topics 4 hours
+
Cohort Analysis Fundamentals
Grouping users by when they joined or what they did, then watching how each group diverges.
AI-Proof
⚡ AI Workflow
Use AI to build the cohort query and chart the curves, then interpret why one cohort outperforms another — the model assembles the grid, but explaining the divergence is the analysis that matters.
Amplitude: Cohort Analysis → Mixpanel: Retention reports →
Retention Curves & the Smile
Whether your product forms a habit, and how to tell from the shape of the curve.
Augmented
⚡ AI Workflow
Have AI compute retention curves across segments and surface where they flatten or decay, then decide which drop-off is a product problem vs. a normal usage rhythm — the model draws the curve, you diagnose it.
Amplitude: User Retention → Mixpanel: Retention reports →
LTV, Payback & Segment Value
Modeling what a customer is worth over time so acquisition spend stays honest.
Augmented
⚡ AI Workflow
Use AI to build an LTV model and CAC-payback calculation from your cohort data, then pressure-test the assumptions (churn, margin, discount rate) before betting budget on the output — the math is automatable, the assumptions are yours to defend.
Amplitude: Customer Lifetime Value → Amplitude: Cohort Analysis →
Module 7 intermediate
Dashboards & Reporting
Turn data into dashboards and narratives that change what people decide to do.
3 topics 4 hours
+
Designing Decision-Driving Dashboards
Building views that answer a question and prompt an action, not just display numbers.
AI-Proof
⚡ AI Workflow
Ask AI to critique a dashboard mockup against the decision it should support and to suggest the chart type for a given comparison, then make the editorial cut yourself — clarity is a judgment call.
Storytelling with Data (Cole Nussbaumer Knaflic) → Tableau: Data Visualization Best Practices →
AI-Assisted Insight & Summaries
Using AI to find and narrate what changed in the data.
Augmented
⚡ AI Workflow
Feed AI an export and ask "what changed and why might it matter," then verify each claim against the source — the model drafts the narrative fast, but you confirm every number before it ships.
GA4: Analytics Insights (Analytics Help) → Storytelling with Data: Blog →
Reporting to Stakeholders
Translating analysis into a narrative executives act on.
AI-Proof
⚡ AI Workflow
Have AI draft the exec summary and tighten the wording, then own the framing, the recommendation, and how you read the room — the model can write the words, but persuading leadership is human work.
Storytelling with Data (Cole Nussbaumer Knaflic) → Occam's Razor: The "So What" Test for Metrics →
Module 8 advanced
Experimentation & A/B Testing
Run experiments that produce trustworthy answers instead of confident mistakes.
3 topics 4 hours
+
Designing Valid Experiments
Setting up a test whose result you can actually believe.
AI-Proof
⚡ AI Workflow
Have AI draft a hypothesis, success metric, and test plan for your idea, then stress-test the design yourself for confounds — the model accelerates the setup, you guard the validity.
CXL: The Complete Guide to A/B Testing → Optimizely: What is A/B Testing? →
Statistical Significance & Power
The statistics you need so a result means what you think it does.
Augmented
⚡ AI Workflow
Use AI to run significance and power calculations and explain the output in plain terms, then interpret what it means for the decision — the computation is automatable, the call to ship is yours.
CXL: A/B Testing Statistics — An Easy-to-Understand Guide → CXL: Statistical Power and How to Calculate It → Optimizely: What is Statistical Significance? →
Avoiding Common Testing Traps
The pitfalls that turn experiments into expensive self-deception.
AI-Proof
⚡ AI Workflow
Ask AI to audit a test plan or result for peeking, multiple comparisons, and other traps, then make the call on whether to trust the outcome — the model flags the known pitfalls, you guard against fooling yourself.
Mida: What Is Peeking in A/B Testing (and Why Not To) → CXL: A/B Testing Statistics → GrowthBook: Where Experimentation Goes Wrong →
Module 9 advanced
AI for Analytics
Put AI to work as your analyst-on-demand while keeping the interpretation human.
3 topics 4 hours
+
Natural-Language Data Querying
Asking your data questions in plain English instead of writing SQL.
Augmented
⚡ AI Workflow
Describe the question and let AI generate the SQL or GA4 exploration, then read and verify the query logic before trusting the answer — fluent output can still query the wrong thing.
AWS: Text-to-SQL Solution Powered by Amazon Bedrock → Amplitude: Funnel Analysis Docs →
Anomaly Detection & Alerting
Letting machines watch the metrics so humans investigate only what's real.
Automatable
⚡ AI Workflow
Configure automated anomaly detection and have AI draft the alert thresholds and a plain-language summary of each spike, then triage which anomalies are signal vs. tracking noise.
GA4: Anomaly detection (Analytics Help) → GA4: Create custom insights and alerts →
Forecasting & Predictive Signals
Using models to project where the numbers are heading and what to do about it.
Augmented
⚡ AI Workflow
Have AI build a baseline forecast and surface predictive signals (churn risk, pacing), then pressure-test the assumptions — a forecast is a hypothesis about the future, not a fact.
Meta: Prophet — Forecasting at Scale → Google Cloud: BigQuery ML Time Series Forecasting (ARIMA_PLUS) → Preset: Time Series Forecasting — A Complete Guide →
Module 10 advanced
Product & Behavioral Analytics
Go beyond pageviews to model how real users move through a product, where they convert, and where they drop.
3 topics 4 hours
+
Event Taxonomy & Tracking Plans
The schema discipline that makes product analytics trustworthy instead of a junk drawer of events.
AI-Proof
⚡ AI Workflow
Use AI to draft a naming convention and a tracking plan from a list of features, then own the taxonomy decisions and governance — the model proposes structure, but a clean, durable event schema is a judgment call that's expensive to redo.
Amplitude: Data planning playbook → Mixpanel: Events and properties →
Funnels & Conversion Analysis
Finding the exact step where intent leaks out of a multi-stage flow.
Augmented
⚡ AI Workflow
Have AI build the funnel, segment it, and surface the steps with the worst drop-off, then form the hypothesis about why users leave — the model finds where conversion breaks, you figure out the reason and the fix.
Amplitude: Funnel Analysis → Mixpanel: Funnels reports →
Path & Session Analysis
Understanding the routes users actually take instead of the one you designed.
Augmented
⚡ AI Workflow
Use AI to summarize the most common paths and unexpected loops in a flow exploration, then decide which deviations are problems worth fixing — the model maps the routes, you judge which ones matter.
GA4: Path exploration (Analytics Help) → Mixpanel: Flows reports →
Module 11 advanced
Data Governance, Quality & Trust
Make your numbers trustworthy and your definitions singular, so the org argues about decisions instead of about whose dashboard is right.
3 topics 4 hours
+
Data Quality & Validation
Catching broken tracking and bad data before it reaches a decision-maker.
Augmented
⚡ AI Workflow
Use AI to generate validation tests and anomaly checks across your pipeline and to triage what failed, then decide which breaks block a release — the model writes and runs the checks, you own the quality bar.
dbt: Add data tests to your DAG → Amplitude: Debugging and validation tools → Monte Carlo: What is data observability? →
Metric Definitions & Single Source of Truth
Ending the meeting where three dashboards show three different revenue numbers.
AI-Proof
⚡ AI Workflow
Have AI help draft and document metric definitions and a data dictionary, then own the governance decision of which definition is canonical — the model can write the spec, but ratifying the one true number is an organizational call.
dbt: Build your documentation → dbt: Introduction to the dbt Semantic Layer → dbt: Model contracts →
Building a Measurement Framework
A North Star and KPI tree that connect every metric to the outcome that matters.
AI-Proof
⚡ AI Workflow
Use AI to draft a candidate KPI tree and pressure-test how inputs ladder up to a North Star, then make the strategic choice of what to actually optimize for — the model structures the options, the metric you bet the company on is leadership's call.
Amplitude: The North Star metric → Avinash Kaushik: Digital Marketing & Measurement Model → Storytelling with Data (Cole Nussbaumer Knaflic) →

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