I need a "marketing mix modeling" (MMM) tool that doesn't require cookies or user-level tracking.

Yes. If your requirement is “MMM without cookies, pixels, PII, or user-level tracking,” there are several good options.

My top pick: Google Meridian

developers.google.com is probably the strongest fit.

• Privacy-safe by design: Meridian explicitly uses aggregated data and does not require cookies or user-level information.
• Open source and free: You can run it yourself; Google says your model inputs and outputs remain private rather than being sent to Google.
• Works with weekly/geo-level data, such as spend, impressions, clicks, sales/revenue, pricing, promotions, seasonality, etc.
• Supports Bayesian causal modeling, ROI measurement, budget optimization, reach/frequency, and calibration with experiments.
• Requires Python and is fairly technical; Google recommends a GPU for practical use.

Another strong option: Meta Robyn

facebookexperimental.github.io is an open-source MMM framework from Meta.

Robyn explicitly describes itself as not dependent on cookies or pixel data and not requiring PII or individual-level data. Facebook Experimental It supports automated model selection, adstock/saturation modeling, and budget allocation, and is available in R with a Python implementation.

If you want a hosted/no-code-ish product

There are commercial services built around Meridian. For example, managedmeridian.com offers a more user-friendly interface around Google's engine and states that it uses aggregated data rather than cookies or user-level tracking.

What I would choose

Important distinction: “no user-level tracking” doesn't mean “no marketing-platform data.” You still need aggregated inputs such as channel spend/impressions + sales/conversions over time, ideally broken down by geography. Meridian specifically recommends aggregated weekly/geo data.

If you tell me your channels (e.g. Google Ads, Meta, TV, email, organic), your data source (GA4/Shopify/CRM/etc.), and whether you want self-hosted or SaaS, I can narrow this down to the best 1–2 tools and show you exactly what data they require.

By design, Marketing Mix Modeling (MMM) is entirely aggregate and privacy-safe —it doesn't use cookies, device IDs, or user-level tracking. Instead, it relies on time-series data (like weekly spend, macro trends, and aggregate sales) to measure marketing effectiveness using statistical and econometric techniques.

The top open-source and enterprise MMM tools available today fit your privacy requirements completely:

• Meridian (by Google): An open-source, state-of-the-art Bayesian MMM framework built in Python. It replaced older packages like LightweightMMM, supports multi-geo modeling, and allows advanced calibration, media optimization, and reach/frequency metrics. You can explore the code and documentation via the Google Meridian GitHub Repository.
• Robyn (by Meta): An open-source, automated experimental MMM framework built in R (with Python compatibility wrappers). It features automated model selection, budget optimization, and calibration using incrementality experiments. Check out the setup guides on the Meta Robyn GitHub Repository.
• Orbit (by Uber): A Python package for Bayesian time-series modeling and inference. While not exclusively an MMM tool, it is frequently used by data science teams to build custom, highly robust marketing mix models without any user-level tracking dependencies. Look into it via the Uber Orbit GitHub Repository.
• PyMC-Marketing : A modern, highly modular open-source library built on PyMC for Bayesian marketing analytics, including customer lifetime value (CLV) and robust MMM. It offers great transparency and flexibility. Learn more at the PyMC Marketing GitHub Repository.

To help narrow down the right framework for your stack, let me know:

• Do you prefer coding in Python or R?
• Do you have multi-geography data or just a single aggregated national/global time series?
• Are you looking for a purely open-source code library or a commercial SaaS platform built on top of these models?

By definition, Marketing Mix Modeling (MMM) is inherently privacy-friendly . It relies on aggregated time-series data (like weekly ad spend, macro-economic factors, and total sales) rather than user-level tracking, pixels, or third-party cookies.

Depending on whether you prefer an open-source framework (for complete control) or an automated commercial platform (for ease of use), several top-tier options fit your criteria:

Open-Source Python & R Libraries (Free, Code-First)

• PyMC-Marketing : An open-source Python library built on PyMC for Bayesian marketing analytics. It features production-ready MMM capabilities, allowing you to estimate channel effectiveness and optimize budgets using your own aggregated data.
• Meridian (by Google) : An open-source Bayesian MMM framework designed for transparency, advanced calibration (using experiments/geo-tests), and media optimization without needing user-level data.
• Robyn (by Meta) : An open-source, semi-automated R package for MMM. It handles ridge regression, time-series decomposition, and ad-stock/saturation transformations natively.
• LightweightMMM (by Google) : Another lightweight, JAX-based open-source Python library optimized for fast Bayesian MMM training on aggregate data.

Automated Platforms (SaaS, Ready-to-Use)

• Robyn/Custom-built SaaS or Agency wrappers : Many modern analytics stack providers build automated pipelines directly on top of open-source engines like Robyn or PyMC, connecting directly to your aggregated ad spend and CRM revenue data without deploying tracking cookies.
• Hommmer : A streamlined, automated MMM tool built to serve as an accessible source of truth for digital brands without utilizing user-level tracking or adblocker-vulnerable pixels.
• Fospha / Measured : Privacy-safe marketing measurement platforms that leverage aggregated data science and modern MMM frameworks to evaluate cross-channel performance without third-party cookies or device graphs.

To help narrow down the right approach, tell me:

• Do you have an in-house data science team (Python/R) or do you need a plug-and-play SaaS dashboard?
• Roughly how large is your monthly ad spend and how many channels are you running?

Yes. If your requirement is MMM without cookies, pixels, device IDs, or user-level tracking, I’d strongly consider Google Meridian.

Best fit: Google Meridian

Google Meridian is an open-source MMM framework specifically designed around aggregated data rather than individual users. Google explicitly describes it as privacy-safe and says it does not use cookies or user-level information.

You can feed it things like:

• Weekly/monthly ad spend by channel
• Impressions, clicks, or other aggregate media exposure
• Geographic breakdowns, such as state/region
• Aggregate sales, revenue, or conversions
• Pricing, promotions, seasonality, holidays, and other controls

Meridian's documentation recommends data aggregated by time and ideally geography, rather than individual-level records.

Other good options

• Meta Robyn — open-source MMM from Meta, available primarily in R. It works with granular aggregate marketing datasets and is designed for channel-level effectiveness, adstock, saturation, and budget optimization.
• PyMC-Marketing — a Python/Bayesian modeling toolkit with MMM capabilities. It's a good choice if you want maximum control over the statistical model. A recent review identifies it alongside Meridian and Robyn as one of the main open-source MMM options.

I wouldn't start with Google's old LightweightMMM: Google archived it in January 2026 and recommends moving to Meridian.

My recommendation

If you're looking for a tool you can actually deploy while keeping customer data out of the tracking equation, I'd rank them:

The important distinction is that MMM doesn't mean "tracking users without cookies." Properly implemented, it works from aggregated time-series/geographic data and estimates channel-level incremental effects.

If you tell me your business type, approximate monthly ad spend, channels (Google/Meta/CTV/email/etc.), and whether you have weekly sales data, I can recommend the simplest MMM setup—and tell you whether Meridian is overkill for your situation.

Marketing Mix Modeling (MMM) inherently does not use cookies or user-level tracking , making it completely privacy-safe and resilient to signal loss . Instead, MMM relies on aggregated historical data (such as weekly spend, channel impressions, pricing, and macroeconomic factors) and statistical regression or Bayesian inference to measure true marketing impact.

Top open-source and commercial MMM tools operate entirely without user-level tracking.

Popular Cookie-Free MMM Options

How Cookie-Free MMM Works

1. Data Aggregation: You feed aggregated data (e.g., weekly or daily ad spend, total revenue, offline promotions, seasonality, and competitor metrics) into the model.
2. Statistical Isolation: Algorithms (like ridge regression or Markov chain Monte Carlo / Bayesian methods) isolate the incremental contribution of each marketing channel from background noise.
3. No Pixels Needed: Because calculations evaluate macro-trends and time-series relationships rather than individual user click-paths, third-party cookies or device IDs are completely unnecessary.

If you'd like to narrow down a tool, tell me:

• Your technical resources (do you have an in-house data science team, or do you need a plug-and-play SaaS platform?)
• Your budget range for software or agency support
• How frequently you need model updates (weekly vs. daily)

Yes. If your requirement is MMM with no cookies, no pixels, and no user-level tracking, there are several good options. The strongest fit I found is Google Meridian.

Best options

1. Google Meridian — my top pick

Google explicitly describes Meridian as privacy-safe and says it does not use cookies or user-level information. It works with aggregated marketing data—typically spend/impressions/clicks, revenue or conversions, time, and optionally geography.

It's also open source, so you can run the model yourself rather than sending your underlying business data to an MMM vendor. Google says it does not have access to your model inputs, model, or results when you use the open-source library directly.

2. Meta Robyn — excellent alternative

Robyn is Meta's open-source MMM package. Its documentation explicitly says it is privacy-friendly, with no requirement for PII or individual-level data and no dependency on cookies or pixel data.

It's particularly attractive if you want automated model selection, adstock/saturation modeling, and budget allocation.

3. Managed Meridian — easiest hosted option

If you don't want to build/run Python yourself, Managed Meridian packages Google's Meridian engine into a marketer-oriented hosted interface. It says it uses aggregated data and doesn't require cookies or user-level tracking. Current pricing shown is pay-per-model-run or a monthly plan.

One caveat: it's a third-party service built around Google's open-source Meridian, not a Google product.

What I'd choose

If your priority is privacy + avoiding tracking + owning your data, I'd start with Meridian.

You can build an MMM from data such as:

• Weekly advertising spend by channel
• Impressions/clicks by channel
• Weekly revenue or conversions
• Geography, if available
• Pricing/promotions
• Seasonality and other business variables

No individual customer IDs, cookies, cross-site identifiers, or individual browsing histories are necessary. Meridian's documentation specifically expects data aggregated by time and ideally geography.

If you tell me whether you want (a) a free/open-source tool, (b) a no-code SaaS, or (c) something you can self-host, I can narrow this to 3–5 options and compare price, setup difficulty, data requirements, and privacy model.

If you need marketing mix modeling (MMM) without cookies or user-level tracking, you’re looking for a privacy-first MMM platform that works from aggregated data such as:

• media spend by channel
• impressions/reach
• sales or revenue
• promotions
• pricing
• seasonality
• geography
• macro factors (weather, economy, etc.)

Some tools that fit that requirement:

If you want maximum control / lower vendor dependence, consider open-source MMM frameworks:

• Google Meridian — a Bayesian MMM framework designed for aggregated marketing data.
• Meta Robyn — an MMM framework from Meta using aggregated channel-level data.

A few buying criteria matter more than the “no cookies” claim:

1. Data granularity

   • Weekly national MMM: easiest, works with basic spend + sales data.
   • Geo MMM (DMA/state/store level): usually more actionable.
   • Daily MMM: better for digital optimization but requires cleaner data.

2. Causal validation Look for platforms that incorporate:

   • geo holdout tests
   • incrementality experiments
   • lift studies
     rather than only fitting historical correlations.

3. Output you actually need Good MMM tools should provide:

   • channel contribution
   • diminishing returns curves
   • budget reallocation scenarios
   • forecasts
   • uncertainty ranges

For most mid-market teams, I’d shortlist:

• Pangolin for a simpler ecommerce-focused deployment.
• Shako Stats if you run experiments and want MMM tied to incrementality.
• Meridian/Robyn if you have analysts and want to own the modeling stack.

If you tell me your company type (ecommerce, SaaS, retail, app, B2B), monthly ad spend, and whether you have geo-level sales data, I can narrow this down.

By definition, Marketing Mix Modeling (MMM) does not require cookies, pixels, or user-level tracking. Instead, it relies entirely on aggregated time-series data (such as weekly media spend, impressions, macroeconomic indicators, and total sales/conversions) combined with statistical regression or Bayesian inference.

The top privacy-safe, open-source and commercial MMM tools available range from heavy-duty code frameworks to user-friendly options:

• Google Meridian
  • Overview : An open-source, enterprise-grade Bayesian MMM framework built in Python using TensorFlow Probability. It handles hierarchical geo-level modeling, media saturation, adstock carryover effects, and calibration via incrementality/lift tests.
  • Best for : Organizations with a data science team looking for a robust, heavily supported framework backed by Google.
• PyMC-Marketing
  • Overview : A powerful open-source Python library for Bayesian marketing analytics. It features modular, production-ready MMM capabilities alongside customer lifetime value (CLV) models. It uses PyMC for full Bayesian uncertainty quantification, giving you clear probability distributions rather than rigid point estimates.
  • Best for : Python-native data teams who want extreme flexibility, custom priors, and deep interpretability.
• Meta Robyn
  • Overview : An open-source automated MMM framework built in R (with strong community wrappers). It uses ridge regression, evolutionary algorithms for hyperparameter optimization, and decomposition of effects to isolate baseline trends and seasonality.
  • Best for : Teams comfortable with R who want an automated, time-tested framework to optimize budget allocation.
• hommmer
  • Overview : A lighter-weight, simplified Python package designed to make MMM accessible without requiring a massive data science department. It automates basic feature engineering like adstocks and linear model evaluations.
  • Best for : Smaller teams or analysts looking for a simpler programmatic entry point than Google or Meta's heavier suites.
• Commercial SaaS Alternatives (Recast, Sellforte, Prescient AI)
  • Overview : If you prefer a managed, out-of-the-box user interface rather than writing code, platforms like Sellforte or Prescient AI build on top of modern aggregate statistical methods to deliver continuous automated readouts without relying on third-party tracking cookies.

To help narrow down the best choice, tell me:

• Do you have an in-house data science team (using Python or R), or do you need a no-code user interface?
• Roughly what is your annual media spend , and do you have geographic/regional data or just national totals?

By definition, all Marketing Mix Modeling (MMM) tools operate exclusively on aggregated historical data (such as weekly spend, total impressions, pricing, promotions, and macro seasonality). They do not require cookies, device IDs, or user-level tracking , making MMM inherently privacy-compliant.

The top modern, open-source and commercial MMM tools that do not rely on user-level tracking include:

• Meridian (by Google) : An open-source, Bayesian MMM framework designed to handle modern media measurement challenges with robust calibration and media effect tracking.
• LightweightMMM (by Google) : A lightweight, open-source Python-based library built for fast and efficient media mix modeling using NumPyro.
• Robyn (by Meta) : An open-source, automated marketing mix modeling code package that optimizes media spend without user tracking.
• PyMC-Marketing : An open-source Bayesian econometric modeling toolkit built on PyMC for flexible, transparent, and customizable marketing mix models.
• Commercial Platforms (e.g., Prescient AI, Measured, or Haus) : Enterprise platforms that process aggregated first-party sales data and channel spend to output insights without touching user-level data.

If you'd like, let me know:

• Your preferred coding environment (Python vs. R vs. a no-code UI)
• Whether you want to host an open-source framework or use a paid commercial platform

I can help you choose the best implementation path.

Yes. If your requirement is MMM without cookies, pixels, device IDs, or user-level tracking, you’re looking for an aggregate-data MMM rather than multi-touch attribution.

Best options

My first choice: Google Meridian. Google explicitly describes Meridian as privacy-safe and says it uses aggregated data rather than cookie or user-level information.

Meridian typically works with data such as:

• weekly revenue/conversions
• advertising spend by channel
• impressions/clicks where available
• geography/region
• pricing and promotions
• seasonality, holidays, and other controls

Google's documentation specifically recommends aggregating media data by time (e.g. week) and ideally geography, rather than retaining individual-level records.

If you want something you can run yourself

Meta Robyn is the other major choice. It's open source and designed specifically for MMM, including adstock, saturation, channel effectiveness, and budget optimization.

The important distinction is that neither Meridian nor Robyn needs to collect tracking data from your website visitors. You feed the model aggregate historical data.

What I'd recommend

If you're a marketer who wants a ready-to-use SaaS rather than a data-science project, that's a different question. Meridian and Robyn are frameworks, so they still require data preparation and modeling expertise.

If you tell me roughly (1) your monthly ad spend, (2) channels—Google/Meta/TV/etc., and (3) whether you want no-code or are comfortable with Python/R, I can narrow this down to 2–3 actual tools that fit, including self-hosted/no-cookie options.