LiftLab vs PyMC-Marketing

LiftLab is a marketing measurement platform for assessing brand and performance media spending and informing budget decisions. Its Two-Stage Agile Marketing Mix Model separates advertising auction cost dynamics from consumer response to estimate channel response curves. The Scenario Planner builds Conserve, Maintain, and Accelerate budget scenarios within constraints such as channel caps, CAC ceilings, and committed spend. Geo experiments in the Incrementality Testing Suite feed causal results back into the model for calibration; supported methods also include Stratified Random Sampling, Synthetic Controls, Switchback tests, Strategy experiments, and Go Dark with Pacing. PlatformSense monitors daily performance changes and uses live signals to inform reallocation recommendations. Miles, an AI-powered analyst, answers questions in plain English using LiftLab’s measurement methodology. LiftLab says it connects with more than 50 paid media platforms, plus analytics, CRM, warehouses, and first-party data sources. Starting data requirements are 12 to 24 months of weekly channel spend, one primary outcome KPI, and a promotional calendar. It is designed for enterprise marketers, including teams spending $5 million or more annually on media. Pricing is on request.

PyMC-Marketing is an open-source Python library for Bayesian marketing analytics, with tools for marketing mix modeling (MMM) and customer lifetime value. Its MMM API supports custom priors and likelihoods, adstock and saturation transformations, time-varying effects, diagnostics and out-of-sample predictions. Models can be calibrated against empirical experiments such as lift tests, and built-in budget optimization allocates spend across channels to maximize ROI. Bayesian outputs include posterior distributions and credible intervals for predictions. The library also includes customer choice, Bass diffusion and alpha-stage predicted incrementality by experimentation models, alongside predictive CLV, cohort analysis and customer segmentation. It runs on Linux and can be self-hosted. Current documentation requires Python 3.12 or later, PyMC 6.0 or later and ArviZ 1.2 or later; installation is available through pip with `pip install pymc-marketing`, without requiring a conda environment. The library is free under the Apache 2.0 license. PyMC Labs and PyMC core developers maintain it, and learning resources include a guided path, example notebooks and an API reference.