Marketing Mix Modeling (MMM) Software

ScanmarQED is a marketing and sales analytics software company serving enterprise brands and media agencies globally. Its portfolio covers multiple measurement disciplines, with Marketing Mix Modeling as a primary specialism. ScanmarQED helps organisations bring MMM in-house on their own terms. Its product suite covers the full MMM workflow: strataQED and MMM Labs for model building and Pulse Planner for budget optimization and scenario planning. ScanmarQED is ISO 27001 certified and GDPR compliant.

strataQED is a complete Marketing Mix Modeling (MMM) platform developed by ScanmarQED. At its core is the Genetic Model Search algorithm testing all viable models and surfaces the best-fit models for each client's specific marketing context. This means teams are guided by AI through the hardest parts of model building and selection.
The practical result is speed and focus. From initial data integration, exploratory models can be live within a couple of hours, so expert time shifts from infrastructure to interpretation, the work that actually drives decisions.
strataQED gives teams full visibility into model construction and assumptions throughout. It is not a black box: every methodological choice can be interrogated, which matters when results need to hold up under scrutiny from finance or senior leadership.

MMM Labs is ScanmarQED's next-generation MMM modeling platform and a core part of its product offering. Where strataQED has been the proven modeling engine for clients since 2006, MMM Labs represents the natural evolution: a cloud-based platform where teams build, run, and validate complete, production-ready marketing mix models across multiple engines — Robyn, Meridian, PyMC, and proprietary — in a single interface.
The multi-engine architecture is not just a comparison tool. It is how MMM should be built. When independent methodologies converge on the same result, you have evidence rather than output, the kind that holds up when a CFO or senior stakeholder challenges the number.
Both products are actively used by clients today. The difference is architectural: strataQED is desktop-based; MMM Labs is cloud-based and built for teams that need defensible, production-ready models at scale.

Yes. ScanmarQED works with global media agencies including WPP Media, Publicis and Initiative Media to deliver MMM across client portfolios at scale. Both strataQED and MMM Labs are reducing per-account setup time significantly — agencies cite speed to working on the model rather than setup as the primary advantage. Our software supports multi-account modeling without proportional headcount increases, making it viable for agencies managing measurement across multiple clients and industries simultaneously.

Using strataQED, exploratory MMM results can be live within a couple of hours of initial data integration. Full production-ready models — including validation, calibration, and stakeholder-ready output — typically take days to a few weeks depending on data readiness, scope, and operating model. ScanmarQED’s onboarding is structured to minimize setup time so analyst resource goes to interpretation from the earliest stage. With MMM Labs, the cloud-based multi-engine platform, teams can reach a first model run without installation or infrastructure setup — it is designed for fast time-to-first-model, with seamless onboarding and no IT provisioning required.

Core inputs include weekly media spend or impressions by channel, the primary business KPI (revenue or unit sales), pricing and promotion data, and distribution metrics. Supporting variables include competitive spend, seasonality indices, holidays and external economical factors. A minimum of two years of weekly historical data is recommended to capture at least two seasonal cycles. Data quality and consistency matter more than sheer volume.

Most MMM platforms commit you to a single modeling engine before you have seen the data. That is a problem, because methodology choice is one of large sources of bias in MMM and a single-engine approach has no way to surface it.
Multi-engine MMM solves this by running multiple methodologies, frequentist and Bayesian, including Robyn, Meridian, PyMC and proprietary engines on the same dataset within a single platform. When independent methodologies converge, you have evidence rather than output.
This is not just a methodological advantage. It is a commercial one. Results that can be triangulated across independent engines are results a marketing team can defend, both in a boardroom and to the CFO.
ScanmarQED's MMM Labs is built around this principle, a full modeling platform where teams build and validate production-ready models across all major engines in one interface, without the overhead of running them separately.

ScanmarQED works with enterprise brands and media agencies across consumer goods, retail, gaming, financial services, telecoms, automotive, and beyond. Fully serviced, cooperative, or fully in-house, ScanmarQED meets organizations where they are, whether that is a first MMM deployment or an established in-house team looking for more scalability and quicker deployment of models. The operating model adapts accordingly while the goal stays the same: build capability rather than dependency, so teams become more self-sufficient over time, not more reliant on a vendor.