mmm-modeling

name

mmm-modeling

description

Media Mix Modeling with PyMC-Marketing. Use when building MMMs, specifying adstock/saturation transformations, setting priors, fitting multidimensional (geo-level) models, computing channel contributions, ROAS, running budget optimization, calibrating with lift tests, or performing sensitivity analysis. Covers the MMM class, GeometricAdstock, LogisticSaturation, BudgetOptimizerWrapper, and ArviZ diagnostics for marketing models.

Media Mix Modeling with PyMC-Marketing

Bayesian Media Mix Modeling workflow using the PyMC-Marketing MMM class.

PyMC prerequisite: This skill assumes familiarity with PyMC's core modeling API (coords/dims, priors, MCMC diagnostics, HSGP). For foundational patterns, see the pymc-modeling skill.

LLMs understand Bayesian inference, MCMC, and hierarchical models in general. But getting from those concepts to a correctly specified, well-diagnosed, and actionable PyMC-Marketing MMM requires domain-specific knowledge: which Prior to use for saturation beta informed by spend shares, how dims=("geo",) activates multidimensional partial pooling, why the final model must be fit on the full dataset (time-slice CV is only for stability assessment), how add_lift_test_measurements() resolves causal identification, and how BudgetOptimizerWrapper translates posterior uncertainty into optimal allocations.

This skill encodes those patterns. Without it, an LLM might hold out test data for the final fit (wrong -- use all data, validate with time-slice CV), use flat priors on saturation parameters (causes divergences), skip add_original_scale_contribution_variable (then contributions are on scaled space), or call BudgetOptimizer directly instead of BudgetOptimizerWrapper (misses geo-level allocation).

Quick Start

import arviz as az
import numpy as np
import pandas as pd
from pymc_extras.prior import Prior

from pymc_marketing.mmm import GeometricAdstock, LogisticSaturation
from pymc_marketing.mmm.mmm import MMM

# Load data
data_df = pd.read_csv("data.csv", parse_dates=["date"])
X = data_df.drop(columns=["y"])
y = data_df["y"]

# Specify model
mmm = MMM(
    date_column="date",
    channel_columns=["tv", "radio", "social"],
    target_column="y",
    adstock=GeometricAdstock(l_max=6),
    saturation=LogisticSaturation(),
    yearly_seasonality=5,
)

# Build and fit on FULL dataset
mmm.build_model(X, y)
mmm.fit(X=X, y=y, nuts_sampler="nutpie", target_accept=0.9, random_seed=42)
mmm.sample_posterior_predictive(X=X, random_seed=42)

Model Specification

The MMM class is the central entry point:

mmm = MMM(
    date_column="date",
    channel_columns=channel_columns,
    target_column="y",
    adstock=GeometricAdstock(l_max=6),
    saturation=LogisticSaturation(),
    dims=("geo",),                        # multidimensional
    scaling={"channel": {"method": "max", "dims": ()},
             "target": {"method": "max", "dims": ()}},
    model_config=model_config,
    control_columns=control_columns,
    yearly_seasonality=5,
    time_varying_intercept=False,
    time_varying_media=False,
)

Priors are customized via model_config using pymc_extras.prior.Prior:

model_config = {
    "intercept": Prior("Normal", mu=0.2, sigma=0.05),
    "saturation_beta": Prior("HalfNormal", sigma=spend_shares, dims="channel"),
    "gamma_control": Prior("Normal", mu=0, sigma=1, dims="control"),
    "gamma_fourier": Prior("Laplace", mu=0, b=1, dims="fourier_mode"),
    "likelihood": Prior("TruncatedNormal", lower=0, sigma=Prior("HalfNormal", sigma=1)),
}

See references/model_specification.md for full constructor reference, hierarchical prior patterns (partial/full/no pooling), and prior predictive checks.

Data Preparation

Data must have a date column, one or more channel (spend) columns, and a target column. For multidimensional models, include a geo/region column and provide data in long format.

Critical: The final model is always fit on the full dataset. Train/test splits are only used for model stability assessment via time-slice cross-validation.

See references/data_analysis.md for EDA patterns, data format requirements, and spend share computation.

Inference and Diagnostics

Always fit on the full dataset, then run diagnostics:

mmm.fit(X=X, y=y, target_accept=0.9, chains=6, draws=800,
        tune=1_500, nuts_sampler="nutpie", random_seed=rng)
mmm.sample_posterior_predictive(X=X, random_seed=rng)

# Divergences (must be 0)
mmm.idata["sample_stats"]["diverging"].sum().item()

# R-hat (must be < 1.01)
az.summary(data=mmm.idata, var_names=[...])["r_hat"].describe()

# Trace plots
az.plot_trace(data=mmm.fit_result, var_names=[...], compact=True)

Use TimeSliceCrossValidator to assess model stability before the final fit:

from pymc_marketing.mmm.time_slice_cross_validation import TimeSliceCrossValidator

cv = TimeSliceCrossValidator(n_init=163, forecast_horizon=12, date_column="date", step_size=1)
results = cv.run(X, y, sampler_config={...}, yaml_path=...)
cv.plot.param_stability(results, parameter=["adstock_alpha"], dims={...})
cv.plot.cv_predictions(results)
cv.plot.cv_crps(results)

See references/model_fit.md for the full diagnostics checklist, time-slice CV workflow, and common fit issues.

Media Analysis

After fitting, analyze channel contributions, ROAS, and saturation:

# Waterfall decomposition
mmm.plot.waterfall_components_decomposition()

# Contributions over time
mmm.plot.contributions_over_time(
    var=["channel_contribution_original_scale",
         "control_contribution_original_scale",
         "intercept_contribution_original_scale"],
    combine_dims=True, hdi_prob=0.94,
)

# Incremental ROAS (preferred -- accounts for adstock carryover)
roas = mmm.incrementality.contribution_over_spend(frequency="all_time")
az.plot_forest(roas, combined=True)

# Saturation curves
mmm.plot.saturation_scatterplot(original_scale=True)

# Sensitivity analysis
sweeps = np.linspace(0, 1.5, 16)
mmm.sensitivity.run_sweep(
    sweep_values=sweeps, var_input="channel_data",
    var_names="channel_contribution_original_scale", extend_idata=True,
)
mmm.plot.sensitivity_analysis(hue_dim="channel", x_sweep_axis="relative")

See references/media_deep_dive.md for ROAS computation, incremental analysis, saturation/adstock curves, sensitivity analysis, and contribution share plots.

Time-Varying Parameters

The MMM class supports GP-based time-varying intercept and time-varying media multiplier via Hilbert Space Gaussian Processes (HSGP):

from pymc_marketing.hsgp_kwargs import HSGPKwargs

mmm = MMM(
    ...,
    time_varying_intercept=True,
    time_varying_media=True,
    model_config={
        "intercept_tvp_config": HSGPKwargs(m=500, L=188, eta_lam=5.0, ls_mu=5.0, ls_sigma=10.0),
        "media_tvp_config": HSGPKwargs(ls_mu=11.0, ls_sigma=5.0),
    },
)

Use TVP when residuals show irregular, non-repeating temporal variation not explained by seasonality, trend, or controls. The GP is primarily useful for in-sample decomposition; it reverts to the prior mean out of sample.

See references/time_varying_parameters.md for HSGPKwargs reference, parameterization tips, diagnostics, and code examples.

Custom Models

When the MMM class cannot express your model structure (non-standard hierarchies, spline baselines, custom likelihoods), build a custom model by combining PyMC-Marketing components with plain PyMC:

import pymc as pm
from pymc_marketing.mmm import GeometricAdstock, LogisticSaturation

with pm.Model(coords=coords) as custom_mmm:
    channel_data_ = pm.Data("channel_data", channel_scaled, dims=("date", "geo", "channel"))
    adstocked = adstock.apply(channel_data_, core_dim="date")
    channel_contribution = saturation.apply(adstocked, core_dim="date")
    # ... add intercept, controls, seasonality, likelihood

Custom models gain full flexibility but lose built-in scaling, plotting, budget optimization, lift test integration, and save/load.

See references/custom_model.md for standalone component usage, hierarchical prior patterns, spline-based intercepts, custom events, and a complete geo-hierarchical example.

Budget Optimization

from pymc_marketing.mmm.mmm import BudgetOptimizerWrapper

optimizable_model = BudgetOptimizerWrapper(
    model=mmm, start_date=str(start_date), end_date=str(end_date),
)

allocation, result = optimizable_model.optimize_budget(
    budget=budget_per_period, budget_bounds=budget_bounds_xr,
    minimize_kwargs={"method": "SLSQP", "options": {"ftol": 1e-4, "maxiter": 10_000}},
)

response = optimizable_model.sample_response_distribution(
    allocation_strategy=allocation,
    include_last_observations=True, include_carryover=True,
)
optimizable_model.plot.budget_allocation(samples=response)

See references/budget_optimization.md for budget bounds setup, custom constraints, budget sweeps, and channel fixing.

Lift Test Calibration

Lift tests resolve causal identification when channels are correlated:

# After build_model, before fit
mmm.build_model(X, y)
mmm.add_lift_test_measurements(df_lift_test)
mmm.fit(X=X, y=y, nuts_sampler="nutpie", ...)

The lift test DataFrame requires columns: channel, x, delta_x, delta_y, sigma (plus geo for geo-level). When time_varying_media=True, include date so each lift measurement maps to the correct media_temporal_latent_multiplier time coordinate.

See references/liftest_calibration.md for data format, calibrated vs uncalibrated comparison, geo-level patterns, and sigma estimation.

Saving, Loading, and YAML Specification

# Save / load fitted model
mmm.save("mmm_model.nc", engine="h5netcdf")
loaded_mmm = MMM.load("mmm_model.nc")

# Build model from a YAML specification
from pymc_marketing.mmm.builders.yaml import build_mmm_from_yaml

mmm = build_mmm_from_yaml("model_spec.yaml", X=X, y=y)

The YAML builder (build_mmm_from_yaml) enables declarative model specification -- useful for reproducible experiments, TimeSliceCrossValidator integration (via yaml_path), and MLflow tracking.

Incremental Analysis and Summary

mmm.incrementality

Counterfactual analysis with proper adstock carryover handling. Preferred approach for ROAS/CAC computation:

roas = mmm.incrementality.contribution_over_spend(frequency="quarterly")
cac = mmm.incrementality.spend_over_contribution(frequency="quarterly")
marginal_roas = mmm.incrementality.marginal_contribution_over_spend(frequency="all_time")

See references/media_deep_dive.md for details.

mmm.summary

DataFrame generation for key metrics:

mmm.summary.posterior_predictive()       # mean, median, HDI, observed
mmm.summary.contributions()              # per-channel/control/seasonality contributions
mmm.summary.roas()                       # ROAS with HDI
mmm.summary.channel_spend()              # raw spend per channel/date
mmm.summary.saturation_curves()          # saturation response curves
mmm.summary.adstock_curves()             # adstock decay curves
mmm.summary.total_contribution()         # summed contributions by component type
mmm.summary.change_over_time()           # percentage change between periods

mmm.data

Validated access to idata with convenience methods:

mmm.data.get_target()                    # observed target values
mmm.data.get_contributions(original_scale=True)
mmm.data.filter_dates("2024-01-01", "2024-12-31")

Plotting Methods Quick Reference

All visualization methods are accessed via the mmm.plot namespace. See references/plot.md for the complete API with exact signatures.

Method	Description
prior_predictive(var=..., hdi_prob=0.85)	Prior predictive HDI bands
posterior_predictive(var=..., hdi_prob=0.85)	Posterior predictive HDI bands
residuals_over_time(hdi_prob=...)	Residuals with HDI bands
residuals_posterior_distribution(aggregation=...)	Residual distribution
contributions_over_time(var=..., combine_dims=..., hdi_prob=...)	Contributions over time with HDI
waterfall_components_decomposition(split_by=...)	Mean component decomposition (waterfall)
channel_contribution_share_hdi(hdi_prob=0.94)	Channel contribution shares (forest plot)
posterior_distribution(var=..., plot_dim=...)	Violin plots of a posterior variable
channel_parameter(param_name=...)	Posterior of a specific channel parameter
prior_vs_posterior(var=..., plot_dim=...)	Prior vs posterior KDE comparison
saturation_scatterplot(original_scale=...)	Observed spend vs. saturated effect
saturation_curves(curve, original_scale=...)	Smooth posterior saturation curves
sensitivity_analysis(hue_dim=..., x_sweep_axis=...)	Counterfactual response under spend scaling
uplift_curve(hue_dim=...)	Precomputed uplift curves
marginal_curve(hue_dim=...)	Precomputed marginal effects
budget_allocation(samples=...)	Optimal allocation summary
allocated_contribution_by_channel_over_time(samples=...)	Contributions under optimal allocation
cv_predictions(results)	Posterior predictive across CV folds
param_stability(results, parameter=...)	Parameter stability across CV folds
cv_crps(results)	CRPS scores across CV folds

Typical MMM Workflow

EDA & Data Prep
    ↓
Model Specification (priors, adstock, saturation, dims)
    ↓
Build Model (mmm.build_model)
    ↓
Prior Predictive Checks
    ↓
[Optional] Add Lift Test / Cost-Per-Target Calibration
    ↓
Fit on FULL Dataset (mmm.fit)
    ↓
Diagnostics (divergences, R-hat, ESS, trace plots)
    ↓
Posterior Predictive Checks
    ↓
Media Deep Dive (contributions, ROAS, saturation, sensitivity)
    ↓
[Optional] Time-Slice Cross-Validation (stability assessment)
    ↓
Budget Optimization