Executive Summary

• Traditional Marketing Mix Models promise insights but often deliver misleading ROI estimates due to fixed parameters, static priors, and constant beta coefficients that fail to capture evolving channel effectiveness over time.

• CMOs typically invest $100K-$500K in models that can misallocate millions in marketing spend because they lack causal validation and dynamic adjustment.

• BlueAlpha’s approach integrates time-sensitive incrementality tests and time-varying coefficients into a Bayesian MMM framework, offering more accurate and context-sensitive marketing attribution.

• Our approach not only provides a holistic, multi-channel view but also includes a practical workflow that enables marketing teams to validate model recommendations and adapt strategies in real time.

• Even if our methodology is more complex, BlueAlpha’s solution is built for ease of use – enabling a single, skilled marketing professional to implement and manage the system without requiring a data science team.

MMM Alone Can’t Prove What’s Working

“Give me all of your data and I’ll tell you what you need to do.”

Marketing Mix Models (MMMs) promise a grand unification of marketing insights, turning spend and exposure data into actionable strategies. In theory, this sounds amazing. In practice (especially in a Bayesian context), MMM-based conclusions can diverge drastically from reality. While MMMs offer a great macro-level view, they often need help in capturing true causality at a more granular level. That’s where incrementality tests come into play.

5 Warning Signs Your Current MMM Is Misleading You

1. Lack of Incrementality Testing: If your MMM vendor doesn’t integrate incrementality testing, their model may never fully validate its assumptions.

2. Static Channel Effectiveness: Reports showing unchanging channel effectiveness (static betas) signal that the model isn’t adapting to real market dynamics.

3. Overvalued High-Spend Channels: Models that consistently overvalue high-spend channels might be misleading your budget decisions.

4. Diminishing Returns Despite Recommendations: Following model advice that still leads to declining returns is a red flag.

5. Lack of Trust Within Your Team: If your marketing team questions the model’s outputs, it undermines confidence in the entire strategy.

BlueAlpha’s system is designed not only to address these issues but also to make the transition to a more reliable system smooth and user-friendly.

Below, we’ll break down our key ideas around building a Bayesian MMM, highlight the significance (and dangers) of prior settings, and then explore how incrementality tests can complement MMMs to deliver more causal and temporally sensitive insights.

Unpacking Bayesian Marketing Mix Models

Prior: Powerful But Perilous

In a Bayesian MMM, priors represent your beliefs about model parameters (e.g. channel effectiveness) before observing the data. These priors are then updated via Bayes’ theorem once you incorporate actual marketing performance data (the “likelihood”). When your dataset is robust and spans multiple time periods/channels, the likelihood typically dominates, minimizing the impact of the prior.

However, when data is sparse, overly strong priors can bias the results. In extreme cases, the model may simply return an ROI that’s dictated by the prior rather than actual evidence. That is the fundamental “pitfall” of Bayesian MMM – if you’re not careful, your prior can become your conclusion.

At BlueAlpha, we choose to loosen up priors and give the model a lot of room to calibrate its own priors. Yes, this is computationally more heavy on our servers, but we believe that the model needs a strong degree of freedom to come to the best outcome.

Metrics: Spend, Impressions or GRPs?

What you choose as the marketing input variable can significantly affect how you interpret model outputs:

Spend

• Pros**:** Directly ties to ROI in monetary terms. Available across all channels.

• Cons: Doesn’t account for actual consumer exposure differences across channels.

GRPs (Gross Rating Points)

• Pros**:**  Standardized metric in TV/offline media. Since they are inherently normalized, GRPs facilitate more accurate modeling and comparison of media effectiveness across different markets and population sizes without the complexity of additional adjustments

• Cons: Not directly monetary; not all channels measure GRPs consistently.

Impressions/Clicks

• Pros**:** Reflect actual exposures or interactions, especially useful in digital.

• Cons: Can be inconsistent across channels (video vs. display vs. social).

At BlueAlpha, we choose the metric that aligns best with your measurement system and the business question you need to answer. If your goal is purely financial optimization, spend might be best. If it’s about reach, we consider impressions or GRPs.

Scope: What Are You Modeling?

• Single Product vs. Entire Portfolio: A narrow focus can yield more precise insights on that product, but might ignore cross-product synergies or cannibalization.

• Purchases vs. Signups: If you’re in subscription or lead-gen, signups might better reflect real progress in the funnel. Otherwise, direct revenue or purchase-based models are often more tangible.

Mechanics of Markov Chain Monte Carlo (MCMC)

▸ What Is MCMC?

MCMC stands for Markov Chain Monte Carlo and describes the systematic way of testing different “what if” scenarios for how each marketing channel might be impacting your sales or conversions. Instead of providing just a single “best guess” MCMC generates many plausible scenarios (parameter values) that are consistent with both the data you have and the assumptions you’ve set (known as “priors”).

▸ Why It Matters

Uncertainty Is Quantified

Rather than just saying, “Channel A has a ROI of 2.1“, you get a range of likely ROIs – along with how confident you can be in that range. This helps decision-makers understand risk: for example, if 80% of scenarios show a positive ROI for a channel, you can invest more confidently.

▸ What’s the Trade-Off?

Computational Time

MCMC can require serious computing power (though modern cloud resources and libraries like PyMC and Stan make this easier than it once was).

Setup and Expertise

Designing a good Bayesian MMM (with the right priors and model structure) and interpreting the results requires expertise. But once set up, it becomes a flexible and powerful tool.

The Critical Flaw in Most Marketing Mix Models: Static Betas

In many out-of-the-box MMM tools (like Meridian, Robyn, PyMC, or proprietary vendor solutions), each channel has a single, constant coefficient (beta) across the entire period of your data. That single beta says, for example, “TV drives an extra 10% of sales for every 1% increase in TV spend“.

Time-varying betas go a step further. They say, “TV’s impact might be 10% in Q1, 8% in Q2, 12% in Q3, etc.” The model’s coefficients shift over time to reflect changes in consumer behavior, seasonality, competitor actions, or even macroeconomic factors.

​​▸ Why Do Standard MMMs Usually Use Constant Betas?

Simplicity

A single coefficient per channel keeps the model simpler. Many popular frameworks (e.g., basic PyMC or Robyn implementations) focus on providing quick, stable estimates that are easy to interpret.

Data Requirements

To accurately detect changes in a channel’s effectiveness over time, you need more granular and varied data (e.g., weekly or daily spend data with sufficient fluctuations). If your spend patterns are fairly static or your dataset is small, a time-varying approach might not reliably zero in on each period’s coefficient.

Computational Complexity

Modeling coefficients that change over time can significantly increase model complexity. This leads to longer processing times and a greater need for advanced modeling techniques (e.g., state-space models or dynamic linear models). For some teams, this can be a barrier to implementation.

▸ But Why Might You Need Them Anyway?

Real-World Dynamics

Consumer habits evolve, new competitors enter the market, and campaigns fluctuate in quality. A “one-size-fits-all” coefficient may overlook these shifts, leading to misleading ROI estimates in certain timeframes.

More Accurate Forecasting

If a channel’s impact is declining (or ramping up) month over month, a time-varying model catches that early. You can reallocate spend with a deeper understanding of when specific channels are most effective.

Ongoing Model Updates

If you run the model again next week with fresh data, the time-varying approach will adapt coefficients to reflect the latest period’s performance. By contrast, a constant-beta model may only inch slightly, even if your marketing landscape changed drastically.

At BlueAlpha, we deploy a smart way of integrating incremental time-varying betas that we derive from incrementality tests into our models. Hereby, we overcome the computational and data requirements while still keeping simplicity and speed top of mind.

Where Incrementality Tests Come In

Even with a well-specified Bayesian MMM, you might still wonder: How do I truly prove causality for Channel X? Incrementality tests are designed to answer precisely that question on a more focused, causal basis.

Why Incrementality Tests?

More Causal Insights

• While MMMs can correlate marketing spend or exposure with sales, incrementality tests (e.g., matched-market experiments, synthetic control methods) isolate the effect of a single channel or campaign under near-experimental conditions.

Validation of Single Channels

• You can run an incrementality test for a specific period in a specific geo or audience, providing a sharper understanding of that channel’s true impact.

Temporal Context

• Results are tied to a unique time window, which is critical because channel effectiveness can vary by season, competitor activity, or economic shifts.

Integrating Incrementality Tests with a Bayesian MMM

Traditional MMMs might try to bake in incrementality tests’ results by imposing fixed weights on certain channels. However, our approach takes a different tack:

“We do not apply a fixed weight onto the prior of the MMM. We believe this is inherently wrong because it completely neglects the temporal context.”

Our approach uses incremental time-varying effects. In other words, we incorporate incrementality test results into our model while respecting the fact that recent tests should carry more weight than older ones. That way, an incrementality test run today influences the model more than one from a month or two ago.

Practical Workflow

Build a Baseline MMM

• Start with a Bayesian MMM that uses historical data across all channels.

• Allow the model to generate posterior distributions for each channel’s beta coefficient – initial best guesses.

Run Incrementality Tests

• For Channel A during a specific campaign window, run a matched-market or synthetic control test.

• Obtain a more causal point estimate (and confidence interval) of Channel A‘s effect in that time and market.

Update the MMM with Time-Varying Betas

• Instead of forcing a blanket prior that remains fixed, let the MMM incorporate the test results as a stronger local update in the time period and context tested.

• Over time, your MMM’s betas become a piecewise function – one that is anchored by incrementality test outcomes in certain intervals.

Rinse & Repeat

• Continue this cycle for other channels and markets. Each incrementality test refines the relevant coefficient in the relevant time window.

• The MMM remains the overarching “orchestrator”, but local incrementality studies feed it more precise, causal signals.

Benefits of This Integration

• Faster Iterations: You don’t need to rebuild the entire model from scratch every time you learn something new. Time-varying betas evolve seamlessly with each incrementality test.

• Contextual Accuracy: Causal test results are “slotted in” precisely where they apply (e.g., Channel A in Q4, Channel B in Region X during a holiday campaign).

• Scalable Automation: This cycle can be automated if your data pipelines are well-structured, letting you run frequent incrementality tests and continuously refine the MMM.

Ease of Use Highlight

Every step in this workflow is supported by an automated platform that handles data integration, analysis, and visualization. This means that even if you do not have a dedicated team of data scientists, you can set up and run BlueAlpha with minimal hassle – and our team provides direct technical support to help you every step of the way.

Case Study: Beehiiv’s Channel Performance Shifts
Thanks to our methodology that combines Marketing Mix Models with incrementality testing, Beehiiv discovered that while TikTok was actually performing better than platform metrics suggested, Meta appeared to have a reasonable CPA based on platform reporting, but its true incremental cost was 345% higher. You can read more case studies here.

Competitor Comparison Table on Test Integration

The MMM as an “Orchestrator”

Ultimately, Marketing Mix Models should serve as the orchestrator that ties together the overall picture of how different channels drive outcomes at scale. Within that framework, incrementality tests act like specialized instruments – they deliver higher-fidelity, causal insights for specific channels in specific times.

By calibrating time-varying coefficients using these local tests, you get the best of both worlds:

• A holistic, multi-channel view (MMM).

• Rigorous causality checks (incrementality tests).

• A fast, iterative process that evolves with each test.

Our philosophy is all about “technology to move fast and iterate”. Contrary to the image of a massive, black-box solution requiring entire data science teams, the approach is to be lean. We built this system to be used by a single person to run all their performance marketing operations.

Self-Assessment: Is Your Current MMM Approach Working?

Answer these five questions to evaluate your marketing measurement strategy:

1. Can your marketing team clearly explain how your current model arrives at its recommendations?

2. Have you validated your model’s recommendations with real-world tests?

3. Does your model account for changes in channel effectiveness over time?

4. Can your current solution detect when platform-reported metrics are misleading?

5. Are you confident your marketing budget is allocated to truly incremental channels?

If you answered “no” to any of these questions, it’s time to consider a more robust approach to marketing measurement.
You’re likely overspending for suboptimal results, missing significant growth opportunities.

Schedule a free 30-minute MMM assessment to see how your current model compares to BlueAlpha’s integrated approach.

For Marketing Leaders Researching MMM Solutions

If you’re currently evaluating Marketing Mix Model vendors for the first time, you’re in a powerful position to avoid the pitfalls that plague many organizations.

When assessing potential MMM partners, look beyond fancy dashboards and ask tough questions about causality validation. 

• Does the vendor integrate incrementality testing into their models? 

• Can they explain exactly how they account for changing channel effectiveness over time? 

• Will they provide transparent interpretations of results rather than black-box recommendations? 

The choice you make now will determine whether your organization gains genuine marketing insights or merely receives sophisticated-looking but potentially misleading analytics. The most successful CMOs we’ve worked with understand that the right MMM approach should not only allocate your current budget more effectively but should continuously evolve as your marketing strategies change. Your MMM solution should serve as a decision-making partner, not just another analytics tool that sits unused after the initial implementation.

Technical Addendum: Understanding the Math Behind Time-Varying Effects

For those interested in a deeper technical understanding of our approach to time-varying beta coefficients in marketing mix models, we’re happy to provide more mathematical details upon request.
That being said, rest assured that our system’s complexity is encapsulated in an intuitive interface designed with the busy marketer in mind, ensuring you reap the benefits without getting bogged down in the technical minutiae.

Your Next Steps

Are you a marketing leader stuck with an MMM tool that lacks incrementality testing? Or are you actively searching for a robust MMM solution that provides a full picture of your channel performance? BlueAlpha is engineered for both scenarios:

• For Those Using Basic MMM Tools: Upgrade your insights by incorporating incrementality testing. Move beyond misleading static models, and unlock causal, dynamic, real-time attribution.

• For In-Market MMM Seekers: Discover a user-friendly, automated platform that integrates time-sensitive incrementality tests with Bayesian MMMs, ensuring accurate, actionable insights without the need for a full data science team.