ExperimentDesigner#

class pymc_marketing.mmm.experiment_design.designer.ExperimentDesigner(mmm)[source]#

Posterior-aware experiment designer for marketing lift tests.

Consumes a fitted MMM and recommends which experiment to run based on posterior uncertainty. Supports national-level experiments analysed via Interrupted Time Series (ITS).

Parameters:

mmmMMM: A fitted pymc_marketing.mmm.multidimensional.MMM instance (or the legacy pymc_marketing.mmm.MMM). Must have been fitted (mmm.idata is not None).

Raises:

ValueError: If the MMM has not been fitted.
NotImplementedError: If adstock_first=False (not supported in v1).
NotImplementedError: If the saturation or adstock type is not supported.

Examples

from pymc_marketing.mmm import MMM
from pymc_marketing.mmm.experiment_design import ExperimentDesigner

mmm = MMM(...)
mmm.fit(X, y)

designer = ExperimentDesigner(mmm)
recommendations = designer.recommend(
    spend_changes=[0.1, 0.2, 0.3, -0.5, -1.0],
    durations=[4, 6, 8, 12],
)

Methods

`ExperimentDesigner.__init__`(mmm)
`ExperimentDesigner.from_idata`(idata[, ...])	Create an ExperimentDesigner from a saved InferenceData fixture.
`ExperimentDesigner.plot_adstock_ramp`([...])	Plot adstock ramp fraction vs. experiment duration.
`ExperimentDesigner.plot_channel_diagnostics`([...])	Plot per-channel diagnostic summary.
`ExperimentDesigner.plot_lift_distributions`(channel)	Grid of lift posterior distributions for one channel.
`ExperimentDesigner.plot_power_cost`(...[, ...])	Scatter plot of assurance vs. absolute net cost.
`ExperimentDesigner.plot_saturation_curve`(channel)	Plot the saturation curve with posterior uncertainty.
`ExperimentDesigner.recommend`([...])	Recommend experiments across all channels.