Marketing Sciences · MMM

Know which dollar actually moved revenue.

Kleyr Modeler is a marketing mix modelling workbench for analysts and operators. Traditional MMM today, Bayesian Structural and a skill-based AI agent next, with the same data spine, the same outputs schema, and a public API to your warehouse.

What's in the box

A complete MMM workflow, web-first.

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Data View

Drop a CSV. We infer column types, surface per-column stats, and let you tag KPI / media / control roles in one click. Connectors for Shopify, Klaviyo, Meta, Google, TikTok, GA4 land post-v1.

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Transforms

Geometric and Weibull adstock for carry-over. Hill, Michaelis-Menten, and S-curve saturation for diminishing returns. Live preview chart so you see the shape before you fit.

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Stepwise regression

OLS or Ridge backbone with forward / backward / bidirectional selection on AIC, BIC, or p-value criteria. Robust HC3 standard errors. Full coefficient table with t-stats and p-values.

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Diagnostics suite

Durbin-Watson, Breusch-Pagan, Ljung-Box, Shapiro-Wilk, VIF, residual plots, ACF, normal Q-Q. The diagnostics aren't an afterthought — they're shown by default.

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Channel decomposition

Per-period contribution per channel. Aggregated totals, share-of-spend vs share-of-effect, response curves. CSV bundle that matches the analyst-grade output schema downstream tools already consume.

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Calibrated by experiments

One click attaches a geo lift estimate from the Experiments module as a Bayesian prior on the corresponding channel. The MMM treats it as a calibration target — exactly the loop that makes enterprise MMM credible.

Methodology

Built on the canon, not on a vibe.

Every method we ship traces back to a paper. Read the references; we'll meet you where the literature is.

PyMC-Marketing
open-source · pymc-labs/pymc-marketing
Bayesian MMM, BTYD CLV, customer choice. Active OSS by the PyMC team.
Google Meridian
open-source · 2024 launch · google/meridian
Hierarchical Bayesian MMM with geo-level pooling. Reference for multi-geo customers.
scikit-learn / statsmodels
standard libraries
OLS / Ridge regression, robust SEs, full diagnostic test suite, stepwise selection.
Foundational paper
Jin et al. (Google, 2017) · Bayesian Methods for Media Mix Modeling
The reference Bayesian formulation. Chan & Perry (2017) covers the failure modes.
Use cases

Three jobs, one model.

Reallocate the media budget
  • Find wasted spend in saturated channels
  • Project the upside of moving £1 between channels
  • Bring it to the CFO with credible intervals
Plan next quarter
  • Forecast revenue under each plan
  • Stress-test against do-nothing
  • Commit a number you can defend
Settle internal debates
  • Brand vs performance
  • Long tail vs the big four
  • Promo cannibalisation
FAQ

MMM, in detail.

Traditional MMM (OLS / Ridge with stepwise selection) is v1 — same shape as the established analyst workflow but on a web platform. Bayesian Structural MMM via PyMC-Marketing lands in v1.5. Both share the same input shape, run lifecycle, and output schema, so switching is a config change, not a re-tooling.
Yes — the Operator tier. A skill-based LLM agent that drives the same Traditional / Bayesian engine on your behalf. Guides you through column mapping, transform choices, model fit, and explanation. Designed to refuse confident answers when the data can't support them — and recommend a geo experiment instead.
A CSV bundle matching the analyst-grade output schema (per-period contributions, aggregated totals, AVM tables, variable details, model details, permutation summary), the model artifact, posterior samples (Bayesian runs), and a reproducibility lockfile. All accessible via the public API on the Pro tier.
Every run snapshots the dataset (immutable, content-addressed), the run config, the Python lockfile, and the random seed. Re-running an old artifact gives the same outputs to the bit.
Methodology is comparable to Recast and Mutinex (proper Bayesian, experiment-calibrated). Pricing is in the £30–80k band — significantly under enterprise vendors. Robyn is Ridge-based and not Bayesian; we use it as a reference but don't run it as our backbone.
✦ Get started

Bring your spend, leave with a model you can defend.