{"id":130885,"date":"2026-06-13T09:32:27","date_gmt":"2026-06-13T09:32:27","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2026\/06\/13\/which-marketing-mix-modelling-platform-should-you-use\/"},"modified":"2026-06-13T09:33:55","modified_gmt":"2026-06-13T09:33:55","slug":"which-marketing-mix-modelling-platform-should-you-use","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2026\/06\/13\/which-marketing-mix-modelling-platform-should-you-use\/","title":{"rendered":"Which Marketing Mix Modelling platform should you use?"},"content":{"rendered":"

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In the event you\u2019ve determined to construct a Advertising and marketing Combine Mannequin (MMM), an necessary first step is to decide on what platform you need to use. There are fairly just a few choices, starting from easy to complicated, with totally different advantages to every<\/p>\n

You\u2019ve in all probability come throughout these libraries already: Google\u2019s Meridian, Meta\u2019s Robyn, PyMC Advertising and marketing and a handful of others.<\/p>\n

This put up walks via these major contenders, what they do nicely, the place they fall quick, and components it is best to take into account earlier than selecting.<\/p>\n

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What’s open-source MMM?<\/strong><\/h2>\n

Earlier than we get into the platforms, a fast clarification on what we imply by an open-source Advertising and marketing Combine Mannequin. These are coding libraries and frameworks. You write the mannequin your self (or adapt one), run it by yourself infrastructure, and retain full management over the inputs, assumptions, and outputs. That is categorically totally different from self-serve instruments, the place the mannequin runs behind the scenes, and also you work together via a dashboard.<\/p>\n

Open-source provides you transparency. You may examine each prior, each transformation, each assumption baked into the mannequin. That transparency issues while you\u2019re making an attempt to persuade stakeholders that your channel attribution is credible, or when a consumer\u2019s knowledge has intricacies {that a} one-size-fits-all resolution would paper over.<\/p>\n

The trade-off is that it requires statistical data to implement nicely and is extra time-intensive. However achieved proper, open-source MMM tends to be extra dependable than its automated rival, as you may create one thing actually bespoke in your wants.<\/p>\n

The principle platforms<\/strong><\/h2>\n

Google\u2019s Meridian<\/strong><\/a><\/h3>\n

Google publicly launched Meridian<\/a> in early 2025, and it’s a totally Bayesian framework.<\/p>\n

Meridian\u2019s standout characteristic set consists of geo-level hierarchical modelling, attain and frequency inputs, ROI priors, and a time-varying intercept to seize gradual shifts in baseline<\/a>. These are all genuinely helpful capabilities.<\/p>\n

It additionally comes with a formidable dashboard output and advantages from Google\u2019s documentation and help infrastructure.<\/p>\n

Meridian\u2019s limitations embody a scarcity of help for time-varying media parameters, and prior customisation is constrained to a completely versatile Bayesian system. For companies with uncommon buildings or a robust prior motive to mannequin a channel in another way, these constraints will be irritating.<\/p>\n

When to make use of Meridian: <\/strong>Select Meridian while you desire a totally Bayesian framework with clear dashboard outputs. It’s superb should you want options like attain and frequency inputs or geo-level hierarchical modelling, however don’t require time-varying media parameters or extremely versatile customized priors<\/p>\n

Meta\u2019s Robyn<\/strong><\/a><\/h3>\n

Meta\u2019s Robyn has constructed up a big group of customers since its launch. It was one of many first extensively adopted open-source MMM packages and, for a lot of organisations, it stays the default place to begin.<\/p>\n

Robyn makes use of the simpler Frequentist method instead of a more advanced Bayesian approach<\/a>. It automates the complicated means of tuning a mannequin by shortly biking via 1000’s of configurations (resembling adstock decay and saturation) and mechanically deciding on a set of excellent fashions. That is very useful while you want a fast readout of your advert efficiency and don\u2019t have the time for handbook tuning.<\/p>\n

Robyn additionally produces clear one-page output summaries, together with contributions, saturation curves, and a funds allocator, that are designed with purchasers in thoughts fairly than a knowledge science staff.<\/p>\n

Robyn\u2019s trade-offs are well-known. It makes use of Ridge regression fairly than a Bayesian method, which supplies you rougher uncertainty estimates. For rating channels, this typically isn\u2019t an issue, however it’s much less superb for justifying funds selections. It could actually additionally calibrate in opposition to experimental outcomes, however solely as a degree estimate that nudges the mannequin in the proper route. It doesn’t bake the outcome immediately into the mannequin itself.<\/p>\n

When to make use of Robyn:<\/strong> Robyn is greatest while you want a fast readout of advert efficiency and lack the time for handbook tuning. It’s a nice place to begin for groups with decrease statistical experience who profit from automated mannequin choice and clear, client-ready abstract outputs designed for stakeholders fairly than knowledge scientists.<\/p>\n

PyMC Marketing<\/strong><\/a><\/h3>\n

PyMC Advertising and marketing is the MMM module constructed on high of PyMC, a mature probabilistic programming library for Python. It utilises a completely Bayesian mannequin, permitting for superior capabilities.<\/p>\n

For instance, you may outline prior beliefs about your channel results, run MCMC (Markov Chain Monte Carlo<\/a>) sampling to get posterior distributions, and find yourself with uncertainty estimates on all the pieces, together with contributions, ROAS, saturation curves<\/a> and adstock decay<\/a>.<\/p>\n

The important thing energy of PyMC Advertising and marketing is flexibility. Not like another platforms, it doesn\u2019t impose a set mannequin construction. You may outline your personal adstock transformations, construct hierarchical buildings throughout geographies or product strains, incorporate incrementality check outcomes into the mannequin and prolong the mannequin nonetheless your consumer\u2019s enterprise logic calls for. There are just a few limitations.<\/p>\n

And if PyMC Advertising and marketing\u2019s built-in API nonetheless doesn\u2019t match, you may step exterior it solely and construct a completely customized PyMC mannequin from scratch, defining your personal mannequin construction with no constraints in any respect. Neither Robyn nor Meridian provide something comparable.<\/p>\n

It does require extra statistical literacy than some alternate options, however the PyMC Advertising and marketing web site gives detailed documentation and a rising library of examples, making it more and more accessible.<\/p>\n

PyMC Advertising and marketing has no ties to any promoting platform. For purchasers who’re delicate to the concept of their MMM being constructed by the identical firm operating their paid search, that independence issues.<\/p>\n

When to make use of PyMC: <\/strong>Whenever you want mannequin flexibility, and you’ve got the statistical literacy and Python data to utilize the pliability of PyMC. It could actually construct utterly bespoke fashions that, with sufficient time funding, offers you the very best indication of promoting efficiency attainable.<\/p>\n

Platform comparability<\/strong><\/h2>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
PyMC Advertising and marketing<\/th>\nMeta Robyn<\/th>\nGoogle Meridian<\/th>\n<\/tr>\n<\/thead>\n
Language<\/td>\nPython<\/td>\nR<\/td>\nPython<\/td>\n<\/tr>\n
Inference methodology<\/td>\nFull Bayesian<\/td>\nRidge regression + optimisation<\/td>\nFull Bayesian<\/td>\n<\/tr>\n
Uncertainty quantification<\/td>\nFull posterior distributions<\/td>\nConfidence intervals<\/td>\nFull posterior distributions<\/td>\n<\/tr>\n
Customisable priors<\/td>\nExtremely versatile<\/td>\nN\/A<\/td>\nRestricted<\/td>\n<\/tr>\n
Geo-level modelling<\/td>\nSure\u00a0<\/td>\nNo<\/td>\nSure\u00a0<\/td>\n<\/tr>\n
Time-variant media parameters<\/td>\nSure<\/td>\nNo<\/td>\nNo<\/td>\n<\/tr>\n
Incrementality Check Calibration<\/td>\nSure \u2013 customized priors or via probability\u00a0<\/td>\nSure \u2013 via optimisation<\/td>\nSure \u2013 ROI priors<\/td>\n<\/tr>\n
Funds optimisation<\/td>\nSure<\/td>\nSure\u00a0<\/td>\nSure<\/td>\n<\/tr>\n
Mannequin flexibility<\/td>\nMost<\/td>\nAverage<\/td>\nAverage<\/td>\n<\/tr>\n
Energetic growth<\/td>\nSure<\/td>\nSure<\/td>\nSure<\/td>\n<\/tr>\n
Statistical experience required<\/td>\nExcessive<\/td>\nLow\u2013reasonable<\/td>\nAverage<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

How we select a platform for our purchasers<\/strong><\/h2>\n

We\u2019re platform-agnostic. Once we begin working with a consumer, we work collectively to outline the aims of the MMM initiatives and establish the channels to be thought-about. Utilizing this info, we focus on with all stakeholders and resolve the popular route ahead.<\/p>\n

If most of a consumer\u2019s funds is within the Google ecosystem and they’re eager to understand how attain & frequency have an effect on outcomes, then Meridian may be the best choice.<\/p>\n

If they’ve many various channels and loads of check outcomes to feed right into a mannequin, then PyMC may be greatest, as we are able to create one thing versatile sufficient and actually bespoke.<\/p>\n

If the enterprise is newer and has few channels, then Robyn will be applicable if a fast readout is desired and there’s no want for the extra superior options.<\/p>\n

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