Evidence for better marketing decisions
From data preparation to budget reviews, follow the reading path that fits your question.
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Reviewing the next budget
Evaluating model reliability
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Industry · Featured
The global MMM landscape: providers, products and how to choose
Compare consulting services, open-source tools and SaaS platforms, their audiences, pricing approaches and trade-offs. A decision framework helps you choose.
Methods · Featured
An introduction to MMM: from adstock to Hill saturation
Learn about advertising carryover, nonlinear saturation, bootstrap uncertainty and the steps in the SparkX modeling workflow.
Methods · New
When experiments and MMM disagree: align definitions, then calibrate
An experimental iROAS of 2 and an MMM return of 4 need not conflict. Distinguish average, interval and marginal returns, align estimands, and examine Bayesian calibration, uncertainty and repeated evidence.
Methods
A good forecast is not enough for budget decisions: three dimensions of model selection
Prediction, decision readiness and explainability require separate criteria. Learn why the best predictive model is not automatically suitable for allocation, and why SHAP is not incremental ROI.
Methods
Why channel ROI drifts: structural change, ad fatigue and carryover
An estimate from three months ago may not hold today. Distinguish structural drift, creative fatigue and changing adstock, and identify signals that support retraining.
Methods
The highest ROI is not always the next budget priority
ROI is an estimate with uncertainty. Understand its sources, quantify it with bootstrap or Bayesian methods, and incorporate risk tolerance into allocation.
Methods
Promotions, prices and stockouts: effects mistaken for advertising
Campaign-season sales are not all caused by ads. Examine how promotions, prices and inventory affect estimates, and distinguish confounders, mediators and endogenous variables.
Methods
Is your MMM reliable? Holdouts, rolling backtests and attribution validation
A high training R² does not establish future accuracy or causal channel effects. Build an auditable framework using temporal holdouts, rolling origins and attribution stability.
Methods
Full-funnel MMM: account for brand mediation without crediting everything downstream
Explore two-stage mediation models and indirect effects, then distinguish Google Meridian’s documented full-funnel features from the version currently used by the platform.
Methods
Synthetic controls and power analysis for geographic experiments
How are control weights fitted, intervals constructed and test duration chosen? Explore augmented synthetic controls, block bootstrap and simulation-based power analysis.
Data integration
Bring DTC and Amazon data into one MMM workflow
Align platform definitions and retail outcomes, explore potential spillovers, and assess what symmetric equations and Granger tests can and cannot establish about cross-channel effects.
Engines
LightGBM + SHAP: a nonlinear modeling option in SparkX
Explore adstock search, temporal holdouts, early stopping, SHAP explanations and bootstrap estimates. SHAP describes a predictive model and is not proof of incremental return.
Planning
Five questions MMM can help address in Q4 planning
Review saturation, carryover timing, channel limits, new-channel tests and alternative scenarios before a high-stakes budget review.
Data integration
Amazon MMM API: media and retail data for your modeling pipeline
Explore Amazon’s MMM data export workflow and the SparkX integration roadmap. Provider API availability does not mean the SparkX connector is already deployed.
Product
SparkX MMM v1.0 release archive: engines, Instant Mode and AI insights
Revisit the original workflow and feature announcements. Check current engine availability, plan terms and capabilities against the latest product information.
Industry
MTA and the renewed role of MMM in privacy-aware measurement
Review how platform and privacy changes affect user-level attribution, and why aggregate modeling is increasingly useful for marketing measurement.
Industry
Presenting MMM to the CFO: translating analysis into decisions
Translate model outputs into business questions about budget, outcomes and risk. Explain what the evidence supports and what still needs validation.
Methods
Multicollinearity in MMM: diagnosis, consequences and responses
When channels change spend together, estimates can become unstable. Diagnose correlated inputs and assess their effects on coefficients, signs and ROI.
Industry
MMM and geographic experiments: combining sources of evidence
Explore how aggregate modeling and geographic experiments complement each other, and how aligned experiment estimates can inform calibration.
Methods
How often should you update MMM: weekly, monthly or quarterly?
Balance decision timeliness and model stability. Use data changes, business cycles and validation evidence to guide the update schedule.
Methods
An MMM data-quality checklist: ten checks before modeling
Review historical coverage, granularity, missing values, outliers, controls and other essential inputs before interpreting results.
Industry
Meta Robyn: methods and practical considerations
Explore adstock, Hill saturation, Ridge regression, installation and workflow choices in Meta’s open-source toolkit, with comparisons to SparkX.
Industry
Google Meridian: Bayesian methods for MMM
Examine priors, posterior inference, JAX acceleration and MCMC sampling in Google’s Bayesian framework, alongside the SparkX PyMC approach.
Methods
Geo lift testing: experimental evidence for MMM calibration
Walk through geographic experiment design, sample planning and evidence integration. Calibration requires aligned outcomes, spending definitions and windows.
Methods
Adstock models compared: geometric, Weibull and delayed
Compare the mathematics, use cases and parameter choices behind three approaches to modeling advertising carryover.
Industry
An illustrative DTC workflow: from data to budget review
Follow an illustrative analysis from spend and outcome data to a candidate budget scenario. Example outputs are not verified customer gains.
Industry
MTA and MMM: complementary views of performance
Understand the limits of user-path attribution and aggregate modeling, and combine them without confusing attributed conversions with causal incrementality.
Engines
Ridge, PyMC and Stan: choosing an engine for your question
Compare assumptions, uncertainty estimates, compute requirements and diagnostics. Speed and suitability depend on data and configuration, not engine names alone.
Budget
Allocate a fixed budget or pursue an outcome target
Explore Hill-response optimization, business constraints and what-if scenarios for allocation or assessing the spend associated with an outcome target.
Methods
Preparing MMM data: collection, alignment and quality checks
Build a checklist for history length, spend continuity, KPI definitions and controls. Better input design helps avoid misleading attribution.
Industry
Robyn, Meridian and SparkX: comparing MMM workflows
Compare engine choices, configuration, transparency, AI assistance and pricing approaches, then match the trade-offs to your team.
Budget
Understanding miROAS: marginal returns and budget allocation
Average ROAS describes past efficiency; marginal ROAS addresses expected return from additional spend. Explore Hill-response derivatives and constrained allocation.
Methods
MMM, causality and the role of GeoLift evidence
Correlation alone does not establish causality. Examine modeling assumptions, geographic experiments and the role of independent evidence in calibration.
Methods
STL decomposition: trend and seasonality in MMM
When sales and advertising rise together, models can confound seasonality with media effects. Explore what STL contributes and where judgment is still required.
Engines
Machine learning meets MMM: nonlinear models and their limits
Explore gradient boosting and SHAP for nonlinear patterns and model explanation, and distinguish prediction from causal marketing effects.
Put the ideas into practice
Explore channel comparison and budget simulation with clearly labeled sample data. No registration or upload of business data is needed.