ScanmarQED

Your data made it into the warehouse. That's just the first step.

Tessa Holzenbosch — Thu, 25 Jun 2026 10:09:00 GMT

Getting data into one place is a solved problem for most FMCG and retail organizations. What happens to its meaning once it gets there is a different matter entirely.

You know this moment. The data has been ingested. The pipelines are running. The dashboards are populated. From the outside, it looks like the hard work is done. And then someone asks a straightforward question: which campaigns drove incremental revenue last quarter, or how a promotion performed net of cannibalization, and the system that looked complete produces an answer nobody can fully defend.

Not because the data is wrong. Not because the engineering is poor. But because the interpretation of that data, what a campaign is, what incremental means, what counts as a promotion, was never formally established. It lives in the assumptions of the analyst who built the report, in a calculation embedded in a dashboard that nobody has reviewed in eighteen months, in a spreadsheet that Finance uses to sense-check numbers that the warehouse was supposed to replace.

This is the interpretation gap. And it sits directly above the data access problem that most organizations have already solved.

Ingestion isn't interpretation

Marketing and commercial data is fundamentally unlike the enterprise data most IT teams are built to manage. ERP systems, CRM platforms, HR databases: these are internally governed, schema-stable, and built around clear ownership. Commercial data is the opposite. It's external, multi-source, and semantically inconsistent by design.

Retailer POS data arrives at a different granularity than media platform data. Brand tracker metrics use survey methodologies that differ by provider and by market. Promotion calendars don't align with media spend cycles. Financial closes happen monthly while retail data refreshes weekly. Each source was built by a vendor for its own purpose, with its own logic, its own hierarchies, and its own definition of the metrics it exports.

Standard data engineering handles the movement of this data well. It was never designed to handle the meaning. Translating a retailer's SKU taxonomy into a consistent product hierarchy across markets, aligning promotion mechanics across different trade calendars, mapping media spend to Finance-auditable cost centres: these are interpretation problems, not ingestion problems. And they don't get solved by getting data into a warehouse. They get solved, or more commonly, deferred in the layer above it.

According to the ScanmarQED 2026 Industry Report, only 10% of organizations have a fully harmonized commercial data platform. The majority have centralized their data without centralizing its meaning. One warehouse, still producing multiple interpretations.

Where interpretation goes when it has nowhere to live

In the absence of a governed place for commercial interpretation to live, it migrates. It moves into dashboard logic, where a calculation built for one purpose gets reused for another without anyone updating the underlying assumption. It moves into analyst conventions, where the person who built the original report made a reasonable call about an edge case, and that call became the de facto standard without anyone formally agreeing. It moves into spreadsheets, where Finance maintains its own version of the numbers because it can't reconcile the warehouse output to the general ledger.

Each of those migration points creates its own version of the truth. None of them are visible to the people relying on the output. And none of them are governed, which means they change without notice, differ across teams, and accumulate over time into a body of commercial logic that no single person in the organization fully understands.

The result isn't a data problem in the conventional sense. The pipelines are intact. The dashboards are live. But the organization is making commercial decisions on interpretations that were never formally sanctioned, and in many cases, never even consciously made.

Only 53% of organizations combine data sources at all, and many of those do so manually. Where integration exists, the semantic layer that makes it interpretable is typically the piece that was never built.

The cost of unsanctioned assumptions

When interpretation is distributed rather than governed, the costs are predictable. Planning cycles slow down because teams can't agree on the numbers underpinning the plan. Analytical work gets repeated because different functions are starting from different baselines. Leadership confidence erodes because the outputs of expensive analytics investments can't be consistently explained or defended.

But it also limits what organizations can responsibly do with their data. Forecasting requires that the definition of what's being forecast doesn't shift between cycles. AI initiatives require data that is interpretable and auditable, not just available. The interpretation gap doesn't just affect today's reporting. It puts a ceiling on every analytical ambition that depends on the data beneath it.

Making interpretation a managed capability

The organizations that close this gap make a deliberate architectural decision. They stop treating interpretation as something that happens implicitly, buried in code and analyst convention, and start treating it as a managed capability. That means establishing a commercial semantic layer: a governed, shared set of definitions, hierarchies, and calculation logic that sits above the source systems and below the analytics outputs.

This layer doesn't require rebuilding what's already in place. It works alongside existing infrastructure, formalizing the interpretation that was previously scattered and making it visible, auditable, and maintainable as the business evolves. New data sources extend the model. Definition changes are governed rather than ad hoc. And the outputs that flow from it can be defended across Marketing, Finance, and IT because everyone is working from the same agreed logic.

It's a shift from data access to data readiness. And it's the step that most organizations have yet to take.

PulseQED is built around this principle. Its commercial semantic layer centralizes interpretation, governs definitions, and ensures that the meaning of commercial data is as consistent and auditable as the data itself. The result is analytics that organizations can act on, not just report from.

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For a deeper look at why the interpretation gap persists and what a governed commercial truth layer looks like in practice, the PulseQED white paper sets out the full picture: From DIY to Trusted Commercial Truth: Building a Data Foundation that Scales.

Written for stakeholders across Marketing, Finance, and IT. Download the white paper →

Promotional Effectiveness Masterclass | Part 1: Why the Market Shift Changes Everything About How You Should Be Planning Promotions

harm.vanderschans@scanmarqed.com (Harm van der Schans) — Thu, 04 Jun 2026 12:27:01 GMT

When did you last fundamentally rethink your promotional strategy? Not the budget, not the mechanics, but the underlying logic? For most FMCG brands, the honest answer is not recently enough.

The market has changed. Have your promotions kept up?

The last few years have put serious pressure on the FMCG landscape. Supply chain disruptions, geopolitical conflicts, energy price increases, and persistent inflation have fundamentally changed how consumers shop.

Prices went up, disposable income went down, and retailers responded by closing stores and pushing private labels harder. Shoppers became far more deliberate about how they spent their money, switching not just between brands, but between channels and formats.

These are not temporary corrections; they represent a structural shift. And yet, when you look at how most companies approach promotional planning, the same strategies are still being applied as though that shift simply hadn't happened.

What is not working anymore?

Historically, FMCG brands have relied on two dominant promotional approaches: Everyday Low Price (EDLP) and deep promotional events, and both are under serious pressure right now.

EDLP made sense when costs were stable and a competitive base price drove consistent volume, but when your cost base has risen significantly, holding an everyday low price becomes a margin erosion exercise rather than a growth strategy.

Deep promotions face a different problem: designed to drive volume spikes and reward loyal shoppers, they now risk subsidizing purchases that would have happened anyway, given how many consumers have already permanently traded down.

The mechanics haven't changed. The consumer responding to them has.

Why most companies are still flying partially blind

Redesigning your promotional strategy requires knowing what actually worked, what didn't, and why and that requires data from multiple sources combined into one coherent picture.

Many companies still rely primarily on internal sell-in data or retailer scan data in isolation, sometimes supplemented by consumer panel data or market research, but with integration that remains largely manual: spreadsheets, PowerPoint decks, and a significant amount of informed guesswork. Decisions involving millions in trade spend are being made on incomplete evidence.

The consequences are predictable. Promotions get rolled over from year to year because there is no clean way to demonstrate they underperformed, budget flows to mechanics with historical momentum rather than proven current ROI, and when volume targets are missed, the response is typically more promotion rather than better promotion. What is remarkable is how many companies accept this situation and simply move on.

Confidence comes from data integration

The companies managing this well share one thing in common: they have built the infrastructure to combine data from multiple sources into a single analytical foundation. Not just data collection, but genuine data integration with the discipline to harmonize inputs and the tooling to generate consistent, comparable outputs.

The numbers make this clear. Companies that combine and harmonize at least two data sources on a dedicated platform report extremely high confidence in their ability to quantify and forecast promotional revenue, with 96% describing themselves as confident or very confident. Among companies working with multiple sources but relying on Excel and PowerPoint for integration, that confidence drops sharply — more than half describe themselves as only somewhat confident, and 12% are not confident at all.

That gap represents real commercial risk. Confidence in forecasting determines how aggressively you can defend a trade spend proposal, how quickly you can course-correct mid-year, and whether your organization is making real decisions or rationalizing them after the fact.

Three things you can do right now

Three practical starting points are worth considering here:

First, audit your data sources. List every source feeding into your current promotional evaluation — internal sales, retailer data, panel, shopper research — and ask honestly whether they are being actively synthesized or living in separate workstreams.
Second, stress-test your assumptions. Take your top three promotional mechanics from last year and ask whether you can demonstrate, with external data, that they drove incremental volume rather than simply moving the timing of purchases.
Third, identify your integration gap. The difference between confident companies and uncertain ones is not accessing data; it is whether that data is being combined into a single decision-ready view. Where does your process break down?

In conclusion

Promotional strategy is one of the highest-leverage decisions a CMO or Finance Director makes. It consumes a significant budget and sends clear signals to both retailers and consumers about how your brand competes.

If the market has fundamentally changed but your strategy has not, you’re likely leaving revenue on the table, or actively eroding margin to defend an approach that no longer reflects the reality of your market. The companies pulling ahead are not necessarily spending more; they are making decisions with greater clarity, and that clarity starts with having the right system in place.

In Part 2 of this series, we dive into the detail of what it actually costs to run promotions without a rigorous evaluation framework and what a fact-based alternative looks like in practice.

If you want to get ahead of that now, our white paper goes deeper on how to build a promotional evaluation system that gives you real confidence in your trade spend decisions.

Your MMM results are only as good as the person who built them

Kenneth Wailes — Thu, 28 May 2026 10:18:44 GMT

The technical quality of a marketing mix model is only one of the things that determines whether its outputs get acted on. The other is whether the people using the results recognize their own business in them (and that is almost entirely a function of what happened before the modeling started).

The assumption gap nobody talks about

Once a client shares their data, the analyst typically takes it from there. Files get processed, variables get constructed, media gets categorized, and the model gets built. The client is consulted at the end, when validation feels like a formality rather than a genuine check. Most of the substantive decisions: how channels are grouped, how cross-sections are handled, which line items get combined, were made by the analyst, based on what the data suggested rather than what the client needs from the output.

This is where the problem starts, and it is less visible than any modeling error. A coefficient can be interrogated. An assumption buried in a data processing step is much harder to surface after the fact.

Media variables are the most common source of misalignment. An analyst might aggregate social video into a single input because it is methodologically defensible. The client runs brand and performance social as distinct activities, managed by different teams with different objectives, and has never mentally treated them as one lever. When the model attributes 14% of revenue to "social," both teams look at the number and feel it has nothing to do with their work. The model is correct; the output is useless.

Why this matters more than most people admit

A model that does not get acted on has no practical value, regardless of its statistical quality. This is not a theoretical risk. In the automation debate in MMM, the trust factor turns out to be decisive: systems that produce technically accurate but organizationally unrecognizable outputs tend to get abandoned. The same dynamic applies at the data preparation stage, and arguably earlier in the project lifecycle.

The cost of misalignment is not usually the rework, though that is real. It is the erosion of confidence in the modeling process itself, which is much harder to recover. A client who has once seen their media plan misrepresented in a model structure will scrutinize every subsequent output more skeptically, and will be slower to act on findings that actually deserve confidence.

Reversing the ownership of data decisions

The conventional workflow asks clients to validate after the analyst has processed the data. The more effective approach starts that conversation earlier and reverses where the ownership sits. Rather than the analyst presenting their categorization choices for client approval, the analyst explains what the model requires structurally: the hierarchies, the grouping logic, the level of granularity, and the client provides their own mapping of how the media should be organized.

The analyst then pushes back where something is not feasible to model, and both sides arrive at a structure that is defensible statistically and recognizable commercially.

This shift reduces rework on the analyst side, but the more significant benefit is what it does to the validation step. When clients have defined their own variable groupings, they understand the data structure well enough to investigate beyond totals. They can check channel subtotals, question specific line items, and identify data integrity problems in the raw files that would otherwise surface as model anomalies weeks later.

The model becomes something they helped build rather than something delivered to them. That distinction matters more than it might seem when results are eventually challenged in a budget meeting.

The methodology earns trust. The process is what makes it possible to keep it.

Your leadership team isn't arguing about strategy. They're arguing about numbers.

Tessa Holzenbosch — Thu, 07 May 2026 10:46:36 GMT

The data exists. The problem is that three functions are looking at three versions of it, and no one can agree which one is right before the meeting ends.

You've been in that quarterly review. Finance presents revenue performance. Marketing presents campaign contribution. Sales presents volume by region. The numbers don't match. Nobody is wrong; each figure is internally consistent. But they can't be reconciled in the room, and the strategic conversation gets pushed again.

We see this pattern consistently across FMCG and retail organizations, regardless of how mature their data infrastructure is. The problem isn't access to data. It isn't analytical capability. It's that each function works from its own data sources: ERP systems owned by Finance, marketing platforms owned by Marketing, sales reporting owned by Sales. Each of those sources carries its own definitions, built for its own purpose. How spend is defined, how revenue is recognized, how promotional lift is measured: these differ not because teams are misaligned in intent, but because the data they work from was never designed to speak the same language. And because departments typically operate within their own systems toward their own goals, those definitions rarely get reconciled into a shared view. The result is fragmented commercial meaning, even when the underlying data sits in the same warehouse.

That fragmentation has a direct cost. And most organizations are paying it quietly, every planning cycle.

One warehouse, multiple truths

The assumption behind most data centralization investments was straightforward: put everything in one place, and the organization works from one set of numbers. In practice, that's rarely what happens.

Marketing defines revenue as attributed spend return, drawn from marketing platform data built around campaign logic. Finance defines it as recognized income against the general ledger, drawn from ERP systems built around financial controls. Sales defines it as closed volume against target, drawn from CRM and sales reporting tools built around pipeline management. Each definition reflects the logic of its source system and the priorities of the function that owns it. None of them produce the same number.

The path forward is straightforward in principle: functions need to align around a common set of definitions, a shared commercial language that sits above the individual source systems and reconciles their different logics into one governed view. That alignment is achievable, but it doesn't happen at the infrastructure layer. It requires a deliberate decision to bring functions together around shared definitions and the right foundation to make those definitions stick.

According to the ScanmarQED 2026 Industry Report, only 4% of organizations report that Marketing, Sales, and Finance always work from the same KPI definitions. For the other 96%, managing the consequences of that misalignment is a recurring operational cost.

This isn't an early-stage problem or a sign of weak data capability. It's the normal operating condition for the majority of organizations in this sector, including those with significant technology investment and experienced analytics teams. The gap isn't in the infrastructure; it's in what sits above it – a shared commercial definition layer that functions can agree on, maintain together, and trust as the single reference point for commercial performance. As the volume and complexity of data continues to grow, that shared layer becomes increasingly more crucial to align but also exponentially harder to maintain. Organizations that operate without one are not just managing today's misalignment, they're compounding it with every new source, market, and planning cycle they add.

The cost shows up in the wrong places

Some of the costs are visible: manual reconciliation hours, duplicated reporting work, planning cycles that run longer than they should. Those are real and they're measurable.

The less visible cost is what happens to decision quality when leadership can't trust that the number on slide four is the same one Finance will recognize on slide twelve. Trade investment decisions get hedged. Promotional strategies get approved on judgment rather than evidence. Budget signoffs require more rounds than the commercial case warrants.

None of that registers as a data cost. It shows up as slower planning, more conservative positioning, and leadership bandwidth consumed by source arbitration rather than strategy.

Only 11% of organizations are very confident in their ability to quantify revenue impact. Seventeen percent are not confident at all. In an environment where margin pressure is rising and promotional ROI is under increasing scrutiny; that's a difficult position to operate from.

The fix isn't technical. It's organizational.

Most organizations don't address commercial data misalignment proactively. They address it when something forces the issue: a planning cycle that breaks down, an MMM initiative that stalls on inconsistent inputs, an AI program that can't be validated against the underlying data.

The instinct at that point is to reach for a technical solution: a new pipeline, a dashboard rebuild, a better integration layer. That instinct is understandable, but it typically doesn't resolve the problem. You can fix the pipeline without fixing what the data means. The result is the same conflict, produced more efficiently.

What actually changes the outcome is a deliberate organizational decision about where commercial definitions live and who owns them. Right now, for most organizations, that responsibility is distributed across team conventions, dashboard logic, individual analysts, and manual workarounds accumulated over years. Concentrating it once, in a governed commercial truth layer that all three functions contribute to and work from, is what produces a durable change.

The evidence on what that shift delivers is worth noting. Organizations that harmonize even two data sources on a dedicated platform are nearly twice as likely to be confident in revenue forecasting as those that don't: 96% versus 47%, according to the ScanmarQED 2026 Industry Report. That gap isn't the result of sophisticated analytics programs. It's the result of getting the foundation right.

Where to start

The organizations that resolve this don't do it all at once. They identify the highest friction use case, typically the point where Finance and Marketing most visibly disagree on commercial performance and establish a shared definition there first. That creates a reference point. From that reference point, the scope expands in a way that's manageable for IT, auditable for Finance, and operationally useful for Marketing from day one.

The modular path matters because it makes the investment defensible at each stage. You're not asking for sign-off on a multi-year platform transformation. You're solving a specific, visible problem and building from there.

PulseQED is designed to support exactly this kind of phased journey. It works as a commercial truth layer within existing enterprise infrastructure, not replacing what IT has built, but establishing the governed semantic foundation that turns centralized data into centralized meaning. Marketing gets a consistent view it can plan from. Finance gets numbers it can defend. IT gets a stable, maintainable system rather than an accumulation of bespoke logic.

If this dynamic is familiar, the full diagnosis and a practical framework for addressing it are set out in our PulseQED white paper: From DIY to Trusted Commercial Truth: Building a Data Foundation that Scales.

It's written for joint buying committees across Marketing, Finance, and IT, because that's where this decision needs to land. Download the White Paper.

The Cost of Methodological Lock-In

Gabriel Mohanna — Thu, 30 Apr 2026 11:59:07 GMT

Most MMM practitioners don’t think of methodological lock-in as a risk — they think of it as a decision they already made. That distinction matters more than it might seem.

The decision that doesn’t feel like a decision

When a team selects an MMM package for a stakeholder project, it rarely feels like a high-stakes moment. There’s a project to deliver, a timeline to hit, and a methodology the team already knows well. The choice gets made quickly, pragmatically, and usually sensibly given the information available at the time.

What follows is gradual and largely invisible. Familiarity deepens, processes form around the tool, and stakeholders develop expectations based on its outputs. New team members are onboarded into its logic. Meanwhile, the field moves on; new engines emerge with stronger priors, better diagnostics, or more appropriate assumptions for a particular stakeholder context. But switching feels increasingly disruptive relative to the effort of staying put. Over time, the package stops being a choice that could be revisited and starts functioning as infrastructure, load bearing in ways that only become apparent when something forces a change.

This is how methodological lock-in works. It is not the result of a bad decision. It is the natural consequence of competence accumulating in one place.

Two costs, only one of which is obvious

The operational cost of switching packages is real and well understood. Teams need retraining, workflows need rebuilding, and methodology changes require careful explanation to stakeholders who have built confidence in a particular approach. These costs are significant, but they are at least visible and therefore manageable.

The second cost is less visible and more consequential. When the toolkit defines what can be tested, it quietly constrains what conclusions are reachable. A team that has worked with one engine for several years does not just have operational investment in it, they have built intuition, developed benchmarks, and constructed stakeholder narratives around its specific outputs. Switching engines does not simply change the tool; it destabilises the interpretive framework the entire practice has been built on.

That is not a software problem. It is a methodological one, and it compounds quietly over time.

Open source is not the problem. Access is.

Some of the most rigorous MMM work being done today is happening in open source. Frameworks like Robyn, Meridian, and PyMC-Marketing are continuously developed and refined by thousands of practitioners and PhD researchers pushing the boundaries of what marketing measurement can do. They are transparent, peer-reviewed in practice, and methodologically serious in a way that proprietary black-box platforms rarely are.

The problem is not the quality of the code. It is the barrier to using it. These frameworks require immense technical expertise to implement correctly; specialist coding skills to set up, configure priors, interpret diagnostics, and maintain over time. That concentrates access in a small number of people, which in turn concentrates analytical capability, limits scalability across markets or stakeholder portfolios, and creates the conditions for a different kind of lock-in: not to a vendor, but to an individual.

Choosing between open-source frameworks is also still a substantive methodological commitment. Robyn, Meridian, and PyMC-Marketing embed meaningfully distinct assumptions around priors, carryover effects, and saturation curves. Picking one and building around it reintroduces the same lock-in dynamic, even with best-in-class code underneath.

The goal, then, is not to avoid open source. It is to make open source accessible at scale, without sacrificing the rigour that makes it worth using in the first place.

A better question to be asking

The standard framing for methodology selection is: evaluate the available options, choose the best one, and build on it. The question practitioners ask is “which package should we use?” and the goal is to answer it well once.

A more useful question is: how do we retain the ability to compare? Methodology selection should be treated as an ongoing empirical question rather than a settled infrastructure decision. The teams that will be best positioned in three years are not necessarily those who identified the superior package in 2024. They’re the ones who built practices flexible enough to test, compare, and adapt as the field continues to evolve. That requires infrastructure designed for comparison, not just execution; one that makes the rigour of open source available to every marketer on the team, not just the specialists who can write the code.

Methodological lock-in is not the cost of making a poor decision. It is the cost of making a good one — and then never questioning it again.

See it in practice

MMM Labs was built on exactly this principle: trusted, transparent open-source engines, delivered through a clickable interface that every marketer can use — no specialist coding required. Run multiple engines side by side on the same data, compare outputs objectively, and select or ensemble the best fit for each business question, without rebuilding workflows or retraining teams every time the methodology evolves.

In our recent webinar, Marketing Mix Modeling is going Multi-Engine — Introducing MMM Labs, we walk through how this works in practice — including a live demonstration of multiple engines running on the same dataset.

The Benefits of a Multi-Engine Approach to MMM

phil.spencer@scanmarqed.com (Phil Spencer) — Thu, 26 Feb 2026 15:35:35 GMT

What is Multi-Engine MMM?

Multi-engine Marketing Mix Modeling (MMM) is an approach where multiple modeling engines run side by side within a single platform. This allows marketing teams to compare results, validate assumptions, and select the best model, or even combine outputs, without committing to a single engine from the start. By running different engines simultaneously, organizations can maintain flexibility, improve confidence in results, and scale their MMM efforts efficiently.

In practice, this means marketing teams aren’t forced to make a high-stakes choice upfront about which engine or methodology to use. Instead, they can explore multiple approaches, see which delivers the clearest insights, and adapt as new tools or techniques emerge.

Stronger, More Reliable Models

The quality of your MMM output directly affects the confidence stakeholders have in marketing decisions. Multi-engine MMM improves reliability by enabling testing multiple engines rather than relying on a single perspective. Different engines have unique strengths and weaknesses and running them side by side shows which approach best fits a given business context.

This approach supports “ensemble-like” thinking, where outputs from multiple engines can be combined to produce stronger conclusions. Cross-validating results across methodologies reduces the influence of individual analyst preferences or skill differences, producing outputs that stakeholders can trust. A two-step pathway - using a Frequentist engine for initial objectivity and a Bayesian engine for stability over updates - can also ensure that outputs are both defensible and resilient over time.

In short, multi-engine MMM can increase confidence in the insights by letting the data guide decisions - not the modeler’s preference or experience.

Flexibility and Optionality Without Lock-In

The marketing analytics landscape is evolving fast. New tools and updates - whether Google’s Meridian, Meta’s Robyn, or other innovations - can shift best practices quickly and have a steep learning curve. Multi-engine MMM provides the flexibility to experiment, adopt new techniques early, and switch approaches without rebuilding workflows or retraining teams.

This agnostic approach ensures organizations can choose the method that best answers each business question, now and in the future. It reduces reliance on a single vendor, methodology, or analyst skillset, while lowering switching costs. Teams can also integrate proprietary models alongside open-source or vendor engines, preserving full strategic freedom.

Multi-engine MMM allows organizations to evolve safely and efficiently with the market, keeping options open and reducing risk of lock-in.

Efficiency, Scalability, and Operational Infrastructure

Running multiple engines has traditionally been operationally challenging. Different engines often require very specific coding efforts to set up properly and handle the outputs, which would create duplicated effort and potentially fragmented reporting if tested in parallel. Multi-engine MMM solves this through platform-level standardization.

A single input format feeds multiple engines, and outputs are harmonized for dashboards, planning tools, and reporting systems. This reduces duplication, allows the right engine to be used for the right question repeatedly, and makes multi-engine workflows feasible at scale. For agencies or global teams, this means operational simplicity, lower costs, and the ability to scale models across markets without adding complexity.

With platform-level standardization, multi-engine MMM becomes operationally practical, commercially viable, and technically robust.

Why Multi-Engine MMM Matters

Choosing the right MMM approach has always been a high-stakes decision. Teams face imperfect information, varying modeler skills, and costly switching if the initial choice is wrong. Multi-engine MMM changes this by providing a platform where multiple engines can be compared objectively, tested thoroughly, and selected—or combined—based on business needs.

For in-house MMM teams, multi-engine MMM delivers:

Stronger, more reliable models from increased choice and cross-validation
Strategic flexibility and optionality without vendor or methodology lock-in
Operational efficiency, scalability, and infrastructure that supports repeated use

By running multiple engines simultaneously, organizations can gain clarity, confidence, and agility in an increasingly complex MMM market. Multi-engine MMM turns what was once a high-risk choice into a controlled, repeatable, and strategic advantage, helping teams make smarter, more informed decisions that drive marketing performance.

ScanmarQED acquires MMM Labs to advance next‑generation, multi‑engine MMM

ScanmarQED — Tue, 20 Jan 2026 10:59:12 GMT

ScanmarQED today announced the acquisition of MMM Labs, a US-based startup known for its modern, multi-engine approach to Marketing Mix Modeling (MMM), allowing teams to work across multiple modeling methodologies without being locked into a single approach.

With MMM Labs, customers gain immediate access to multiple advanced open-source and proprietary MMM engines through a single streamlined interface. Modeling capabilities that traditionally take weeks to set up and require significant data-science effort can now be deployed in minutes, directly within a guided environment designed for marketers and analysts.

The platform enables teams to run multiple modeling engines side by side, including leading frequentist and Bayesian open-source frameworks such as Robyn, Meridian, and PyMC, alongside proprietary or custom-built models. This multi-engine approach allows teams to compare results, test assumptions, and iterate faster, without the operational complexity typically associated with advanced MMM.

By combining PulseQED’s enterprise-grade governance and scalability with MMM Labs’ speed, transparency, and flexibility, ScanmarQED gives marketing teams the freedom to experiment and customize their approach while maintaining consistency and control. The result is a more open, scalable, and practical approach to modern MMM for brands and agencies worldwide.

Marcel van der Kooi, CEO of ScanmarQED Group, commented:
“This acquisition accelerates our vision for PulseQED: combining enterprise-grade MMM measurement and planning with the flexibility of open-source innovation and custom modeling code. For years, companies have been forced into a false choice, pick one modeling engine and live with its limitations. That era is over. Customers can now access and run multiple engines side-by-side, all within a controlled and guided environment. The result is faster cycles, stronger models, and better decisions, without sacrificing governance or expert oversight.”

Gabriel Mohanna, CEO of MMM Labs, added:
“MMM Labs was founded to make marketing mix modeling more accessible, intuitive, and practical for everyday marketers. Joining ScanmarQED allows us to amplify that mission. With ScanmarQED’s scale and shared commitment to innovation, we can push the boundaries of what MMM can be: simpler, smarter, and more powerful. Together, we are redefining how marketing measurement is built and deployed across organizations.”

About ScanmarQED

ScanmarQED is a leading provider of marketing insights and analytics solutions, helping companies measure sales and marketing effectiveness. Our software enables better budget allocation, more accurate demand forecasting, and improved decision-making for future marketing plans. Headquartered in the Netherlands, with offices in London, Prague and Chicago, ScanmarQED serves a global client base across industries including Retail, Healthcare, Food & Beverages, Technology, Media and Market Research.

https://www.scanmarqed.com/

About MMM Labs

MMM Labs is a US-based startup focused on making Marketing Mix Modeling more accessible and actionable for marketers. Their innovative platform combines different modeling techniques with automation and multi-engine support to deliver powerful insights at scale.

https://www.mmmlabs.ai/

About PulseQED

PulseQED is ScanmarQED’s flagship solution for marketing and sales data analysis. It provides up-to-date, decision-ready insights by transforming raw data into predictive outputs that support planning and performance evaluation. It is designed to reduce manual processes and improve speed and accuracy in marketing decision-making.

https://pulseqed.ai/

10 Things Marketers Need to Know About MMM for Better ROI

Gabriel Mohanna — Tue, 20 Jan 2026 10:58:10 GMT

In today's competitive and complex marketing landscape, understanding how each marketing channel contributes to your return on investment (ROI) is more important than ever. Marketing Mix Modeling (MMM) is a data-driven tool that helps marketers measure the effectiveness of their marketing efforts across multiple channels, from digital ads to traditional campaigns. If you're new to MMM, don’t worry—we’ll cover everything you need to know!

1. What is Marketing Mix Modeling (MMM)?

MMM is a statistical analysis method that helps marketers understand how different sales and marketing tactics contribute to revenue. By analyzing aggregated data, MMM allows brands to analyze the effectiveness of marketing campaigns and calculate return on investment (ROI). This helps marketers make better decisions about where to invest their budget for maximum impact.

2. MMM Helps You Understand Marketing Impact Across Channels

When using multiple channels, it can be hard to know which marketing tactics are truly moving the needle. MMM allows marketers to break down the effect of each channel, both online and offline, and measure how they work together to drive sales.

3. MMM Doesn’t Rely on User-Level Data

In a world where privacy regulations like GDPR and CCPA are top of mind, privacy-compliant marketing analytics is essential. Unlike some tools that depend on tracking individual users, MMM uses aggregate, anonymized data. This makes it a future-proof solution for brands looking to measure performance without infringing on user privacy.

4. MMM Can Measure Long-Term Effects

While MMM is great for analyzing short-term marketing ROI, it also excels at measuring the long-term impact of brand-building campaigns. Whether you're running a PR campaign or TV ads, MMM can track how these activities contribute to revenue over time.

5. MMM is Ideal for Both Online and Offline Campaigns

Whether your focus is on digital marketing strategies like PPC ads or traditional channels like print and radio, Marketing Mix Modeling can assess the performance of each. This flexibility makes it the go-to tool for marketers handling a mix of online and offline marketing tactics.

6. MMM Accounts for External Factors

External factors such as seasonality, economic trends, and competitor actions can all influence the performance of marketing campaigns. MMM incorporates these variables, providing a clearer picture of cross-channel marketing performance and how external forces impact your results.

7. It’s a Great Tool for Budget Optimization

By identifying which channels contribute the most to your sales, MMM gives marketers a roadmap for marketing budget optimization. You can allocate resources away from underperforming channels and invest more in high-impact marketing tactics

8. MMM Can Work with Limited Data

Unlike some forms of advanced analytics that require massive amounts of granular data, MMM can provide meaningful insights even with limited historical data. If you're a newer brand or haven't collected a large amount of historical data, don’t worry. MMM works effectively even with limited marketing data , making it a valuable tool for businesses of all sizes.

9. MMM is Privacy-Compliant

As privacy regulations tighten, marketers are looking for ways to measure effectiveness without invading user privacy. MMM's use of aggregated, anonymized data ensures you’re complying with strict privacy laws while still gaining valuable insights.

10. It’s Not a ‘Set It and Forget It’ Tool

While MMM provides invaluable insights, it’s most effective when used as part of an ongoing optimization process. Marketing environments change, and so do consumer behaviors. Regular updates will ensure you maintain a competitive edge in a rapidly changing market.

In a world where marketing budgets are tight and channels are ever-changing, Marketing Mix Modeling (MMM) offers marketers a reliable, privacy-compliant method to analyze and optimize their efforts. Whether you're managing online ads, traditional campaigns, or a combination of both, MMM provides actionable insights that help you make smarter investments and improve overall marketing ROI.

Marketing Mix Modeling is an essential tool for marketers looking to make sense of their marketing efforts, especially in a world where privacy regulations are tightening, and consumer behavior is constantly evolving. Whether you’re running online ads, offline campaigns, or a mix of both, tools like MMM labs provide the insights needed to optimize your marketing strategy.

What Executives Need to Know About MMM (Marketing Mix Modeling)

Gabriel Mohanna — Tue, 20 Jan 2026 10:57:48 GMT

Today’s marketing analysts at agencies understand the value of Marketing Mix Modeling (MMM) solutions. We know that MMM can reliably improve client outcomes, increase efficiency, and boost the agency's competitive position. However, convincing the executive team to sign off, and quickly, can be a challenge.

Here’s how to pitch Marketing Mix Modeling solutions to each executive—and win them over.

Chief Executive Officer (CEO)

Primary Concerns: Company growth, market positioning, and long-term strategy

How to pitch MMM:

Growth Leverage: MMM identifies which marketing activities contribute most to growth, helping us scale efficiently and drive sustained performance.

Strategic Insights: This tool provides insights that inform not just marketing, but also product development and market positioning, based on which channels contribute most to company-wide growth.

Future-Proofing: As consumer behaviors and media trends shift, MMM ensures we can adapt quickly and stay competitive.

Chief Marketing Officer (CMO)

Primary Concerns: Campaign effectiveness, ROI, and marketing budget optimization

How to pitch MMM:

Improved Decision-Making: Our current marketing analytics provide fragmented insights. With Marketing Mix Modeling, we’ll clearly understand the ROI of every marketing channel, allowing us to optimize our marketing budget across paid media, social, and more.
Strategic Allocation: MMM helps us make real-time marketing decisions and reallocate budget based on what’s actually working, versus relying on outdated data.

Competitiveness: Having precise insights into which campaigns drive results will give us a competitive edge.

Chief Financial Officer (CFO)

Primary Concerns: Cost control, return on investment, and financial predictability

How to pitch MMM:

Cost Efficiency: By using Marketing Mix Modeling, we’ll cut wasteful spending and focus on campaigns that are proven to deliver ROI.
Data-Driven Budgeting: MMM provides accurate predictions of future marketing performance, helping us create more justifiable and precise budgets.
Revenue Impact: With MMM, we can directly tie marketing spend to revenue generation, making it easier to defend the budget.

Chief Technology Officer (CTO)

Primary Concerns: Data integrity, system integration, and analytics capabilities

How to pitch MMM:

Data Unification: MMM integrates data from multiple sources, offering a single source of truth for our marketing efforts.
Actionable Insights: Unlike fragmented reports, MMM delivers insights that can feed into other systems like AI and machine learning models for more advanced analytics.
Technology Agility: Built on open-source technology, MMM integrates seamlessly with our existing infrastructure, ensuring flexibility and transparency.

Chief Client Officer (CCO)

Primary Concerns: Customer acquisition, retention, and experience

How to pitch MMM:

Customer-Centric Insights: MMM helps us understand which marketing activities are driving customer acquisition and retention, ensuring we focus on improving the customer experience.
Channel Optimization: By identifying the most effective channels for engaging customers, MMM helps refine our acquisition strategy and reduce churn.

Marketing Mix Modeling (MMM) offers a robust, data-driven solution tailored to meet the needs of C-level executives across the organization. For the CMO, MMM optimizes budget allocation and boosts competitiveness by revealing true ROI. The CFO benefits from cost-efficient marketing and accurate budgeting, while the CEO gains insights that fuel long-term growth and market strategy. The CTO will appreciate the seamless data integration and actionable insights, and the CCO can use MMM to improve both customer acquisition and retention.

At MMM Labs, we know first-hand how these advantages make Marketing Mix Modeling an essential tool for driving sustained business success and securing a competitive advantage. Contact MMM Labs today to learn more and jump into MMM.

What Agency Execs Need to Know About MMM (Marketing Mix Modeling)

Gabriel Mohanna — Tue, 20 Jan 2026 10:57:30 GMT

Here’s how to pitch Marketing Mix Modeling solutions to each agency executive—and win them over.

Chief Executive Officer (CEO)

Primary Concerns: Growth, client retention, and differentiation in a competitive market

How to pitch MMM:

Client Success = Agency Growth: MMM allows us to provide clients with more accurate insights into what’s driving their campaign performance. This leads to better client retention and longer-term relationships. MMM directly improves client ROI, which in turn, grows the agency through referrals and satisfied customers.

Scalable Solutions: With many clients across industries, our current methods are becoming too resource-intensive. MMM automates much of the analysis, allowing us to serve more clients at scale without compromising on actionable insights.

Differentiation in a Crowded Market: Offering state-of-the-art marketing mix modeling technology sets us apart from competitors still relying on less precise metrics like last-click attribution or publisher-provided data.

Chief Marketing Officer (CMO)

Primary Concerns: Client campaign performance, strategic recommendations, and demonstrating marketing effectiveness

How to pitch MMM:

Enhanced Client Reporting: Clients are increasingly asking for transparent, data-backed recommendations. MMM provides the granular insights that clients want, improving their confidence in our strategic recommendations and demonstrating marketing effectiveness.

Optimized Client Campaigns: MMM enables us to adjust tactics mid-flight, optimizing campaigns in real-time rather than waiting until the campaign concludes. This proactive approach enhances client satisfaction and delivers higher ROI.

Cross-Client Learnings: By using MMM, we can identify trends across our client base. These data-driven marketing strategies can be applied across multiple industries, improving client performance and giving us a competitive edge.

Chief Financial Officer (CFO)

Primary Concerns: Profitability, cost control, and resource allocation

How to pitch MMM:

Increased Operational Efficiency: MMM automates marketing analytics, reducing operational costs and improving our ability to handle more clients with the same resources. This aligns with the CFO's focus on profitability and cost control..
Data-Driven Retention Strategies: With more accurate insights from marketing mix modeling, we’ll reduce client churn and retain high-value accounts. This improves client retention and leads to a more predictable revenue stream.

Justifying Client Budgets: Clients often seek to cut marketing costs. With precise ROI data provided by MMM, we can justify the budgets they allocate to us, reducing pricing pressure and ensuring more stable revenue.

Chief Technology Officer (CTO)

Primary Concerns: Integration of new technology, data infrastructure, and scalability

How to pitch MMM:

Seamless Integration: MMM integrates with our existing data infrastructure, pulling in data from client campaigns, ad platforms, CRM systems, and more. Built on open-source marketing technology, MMM offers flexibility and future-proofing for the agency's evolving needs.

Scalable for Multiple Clients: MMM allows us to manage multiple clients’ data simultaneously, providing individualized insights at scale. This capability is crucial as our client base grows and becomes more diverse.

Future-Proofing the Agency: As marketing technology evolves, MMM gives us the flexibility to stay ahead of changes in consumer behavior and media trends by adapting our analytics approach quickly.

Chief Client Officer (CCO)

Primary Concerns: Client satisfaction, retention, and long-term relationships

How to pitch MMM:

Better Client Outcomes: MMM provides detailed insights into which campaigns and channels drive the most value, enabling us to deliver superior results. Satisfied clients are more likely to stick with us, improving client retention.

Personalized Client Insights: MMM allows us to offer customized reports and recommendations for each client, showing we understand their unique needs. This investment in personalized insights strengthens client loyalty.

Transparency & Trust: As clients demand more transparency in how their media dollars are spent, MMM provides an unbiased, data-driven approach. This level of transparency builds trust and strengthens long-term relationships.

Effectively pitching Marketing Mix Modeling (MMM) to agency executives requires tailoring the message to their unique concerns. For the CEO, it’s about agency growth, client retention, and market differentiation. The CMO values enhanced reporting, optimized client campaigns, and cross-client learnings to inform strategy. The CFO is focused on operational efficiency, better client retention, and justifying client budgets with data. The CTO needs to ensure that MMM integrates seamlessly with existing systems and is scalable for growth. Finally, the CCO seeks improved client outcomes, personalized insights, and transparency to enhance long-term relationships.

By addressing these priorities, agencies can position Marketing Mix Modeling as a game-changing solution that drives internal efficiencies and improves client outcomes. Contact MMM Labs for more information on how we can help your agency meet your goals.