Step 1: Define the Outcome and Time Granularity
Pick the business outcome the model will explain. Revenue is the default; volume, conversions, or a leading indicator like qualified leads can substitute when revenue has reporting lag or pricing noise. Pick weekly granularity for almost every case; daily produces too much noise, monthly loses too much information.
The outcome series should cover at least 104 weeks (two years) of history. Less than 52 weeks produces models with wide posteriors that cannot reliably distinguish channel contributions; 104 weeks is the practical minimum for production-quality MMM.
Step 2: Inventory the Channels
List every marketing channel that has touched the brand in the modeling window with measurable spend or exposure. Include paid digital (search, social, display, programmatic, retail media), traditional (TV, radio, OOH, print), owned (email, organic content), earned (PR), and emerging (AI search, podcast, influencer). For each channel, document the spend or exposure series at weekly granularity.
Critical: include AI search from day one. The most common MMM build mistake in 2026 is omitting AI visibility because the data feels new or unfamiliar. Models built without AI search start with the dark-funnel debt baked in and are harder to retrofit than to build correctly from scratch.
Step 3: Add Controls
Non-marketing drivers of the outcome that need to be in the model: seasonality (week of year), trend (linear or spline), price changes, promotion calendars, distribution metrics, weather where relevant, macroeconomic indices (consumer confidence, unemployment, category-relevant macro), competitive activity if measurable, holidays and special events.
Omitted controls produce biased channel coefficients. The discipline is to start with a generous control set, then prune based on Bayesian variable selection or posterior contribution analysis.
Step 4: Choose the Framework
For most teams: Robyn (R), LightweightMMM (Python), or PyMC-Marketing (Python). For teams with strong Bayesian capability: build directly in Stan or NumPyro. For teams without dedicated marketing science staff: commercial vendor with templated implementation (Recast, Northbeam, Aryma, Mass Analytics). The choice depends on language, customization needs, and team capability.
Step 5: Set Priors
Adstock priors: geometric half-life ranges by channel based on category benchmarks. AI search: two to four weeks. Paid search: zero to one week. TV: three to six weeks. Saturation priors: Hill function with half-saturation in the middle of the observed exposure range. Coefficient priors: weakly informative, typically normal with mean zero and large variance.
Document the priors as a separate artifact. Future refits and stakeholder reviews need to see what assumptions went into the model.
Step 6: Fit and Validate
Run the inference. For Bayesian frameworks, monitor convergence diagnostics (R-hat, ESS, divergent transitions). For Robyn, inspect the Pareto frontier and select a model balancing NRMSE and DECOMP.RSSD. Hold out the most recent four to eight weeks for validation; the holdout fit (MAPE on the holdout) is the primary quality check.
Inspect channel contributions for plausibility. Channels should contribute proportionally to spend or exposure and the base demand should be defensible. Implausible contributions (negative channel effects, channels with greater than 50 percent share) usually indicate a spec issue.
Step 7: Calibrate Against Lift Tests
If any historical incrementality tests or conversion lift studies exist for the channels, compare MMM coefficients against the test estimates. They should agree within confidence interval. Use the calibration_input argument in Robyn or the equivalent in your framework to enforce the test estimate as a prior on the channel's coefficient.
Step 8: Operationalize
Wrap the model in a refit pipeline that runs monthly or quarterly with new data. Build dashboards for stakeholder consumption. Document the methodology in a governance pack. Plan the calibration cadence: one lift test per quarter on a rolling channel selection. AI search should be in the rotation.
How Presenc AI Helps
Presenc AI provides the AI visibility data layer for new MMM builds. Weekly LLM share of voice, historical backfill of 52+ weeks so the AI variable is informative from the first refit, regional segmentation for geographic lift testing, prior recommendations specific to the AI variable. Building MMM from scratch is the cleanest moment to include AI search; Presenc removes the data-availability blocker that otherwise pushes the AI variable to phase two.