Why Google Meridian and Meta Robyn Fall Short in Marketing Mix Modelling Tuning and Tweaking

Introduction

Marketing Mix Modelling (MMM) remains a cornerstone of marketing analytics, helping brands measure the effectiveness of their campaigns and optimize budgets. However, the process of tuning and refining MMM models is notoriously complex, requiring deep statistical expertise and patience.

Two of the most prominent open-source MMM tools—Google Meridian (a Bayesian MMM framework) and Meta Robyn (an evolutionary computing-based MMM solution)—promise to simplify this process. Yet, in practice, both tools suffer from inefficiencies, uncontrollable tuning mechanisms, and time-consuming iterations that frustrate data scientists and marketers alike.

In this article, we’ll dissect why these tools fail to deliver a smooth MMM tuning experience, the technical and practical challenges they introduce, and how real-world users struggle with them.

The Problem with Google Meridian: A Bayesian Nightmare

Google Meridian adopts a Bayesian approach to MMM, leveraging Markov Chain Monte Carlo (MCMC) sampling to estimate media effects. While Bayesian methods offer flexibility, Meridian’s implementation introduces several pain points:

Prior Distribution Tweaking is Inefficient and Unpredictable

• "Spray and Pray" Methodology: Adjusting prior distributions (e.g., for media coefficients) feels like a blind optimization process. There’s no clear guidance on how changes will impact the final model.
  
• Unstable Adjustments: Modifying one prior often unexpectedly shifts other variables, destabilizing the model. Without constraints, coefficients can drift into unrealistic ranges (e.g., negative ROAS for TV ads).
  
• No Guarantee of Marketing-Friendly Outputs: Bayesian models prioritize statistical fit over business logic. Marketers expect ROAS and contribution shares to align with spend—but Meridian often produces counterintuitive results that require manual overrides.
  

MCMC is Slow and Uncontrollable

• Random Sampling ≠ Controlled Optimization: MCMC explores parameter space randomly, meaning convergence is slow—especially with large datasets.
  
• No Constraints on Realism: Unrestricted sampling can yield implausible coefficients (e.g., a tiny digital ad spend driving massive sales). Users must manually enforce constraints (e.g., via half-normal priors), but this is trial-and-error.
  
• Re-Runs Are Painfully Frequent: Each adjustment requires full re-sampling, leading to hours or days of wasted computation just to test a single hypothesis.

Real-World Modeller’s Frustration

"I had to pre-calculate coefficients using constrained OLS and feed them into Meridian as priors—otherwise, the Bayesian model would give me nonsense. Even then, it took weeks of tweaking before stakeholders accepted the results."

This workaround defeats the purpose of using a Bayesian framework—if you need OLS to guide your priors, why not just use OLS?

Meta Robyn: Smarter in Theory, Lazier in Practice

Meta Robyn uses evolutionary computing to optimize MMM, framing model selection as a "survival of the fittest" problem. While this sounds innovative, real-world usage reveals critical flaws:

Evolutionary Computing is a Black Box

• No Mid-Process Adjustments: Once Robyn starts evolving models, you can’t tweak media contributions manually. You’re stuck waiting for generations of models to evolve—hoping one aligns with business expectations.
  
• Fitness Trade-Offs Hurt Practicality: Robyn optimizes for two conflicting objectives:
  
  • Model accuracy (minimizing data fitting error).
    
  • Spend-to-impact alignment (ensuring media contribution shares match spend shares).
    
  • Since these are competing goals, the final model is always a compromise—rarely satisfying marketers.
    

Adstock & Diminishing Returns Tweaking is Back-and-forth

• Manual Parameter Overrides: If results are unrealistic, users must adjust Adstock decay rates or saturation curves and re-run the entire evolution—another time sink.
  
• No Guarantee of Improvement: Changing parameters doesn’t guarantee better outputs. Many users report running Robyn for weeks only to find none of the evolved models are usable.
  

Real-World Modeller’s Struggle

"I spent weeks running Robyn, but the marketing team kept rejecting the outputs. The evolutionary algorithm doesn’t let me enforce business rules—it just ‘hopes’ a good model emerges. Most of the time, it doesn’t."

The Common Failure: Lack of Control & Efficiency

Both tools share three critical shortcomings:

• Blind Optimization:
  
  • Meridian relies on random MCMC walks, while Robyn uses evolutionary trial-and-error.
    
  • Neither allows direct intervention to enforce business logic mid-process.
    
• Time-Consuming Re-Runs:
  
  • Every tweak requires full computation, leading to days or weeks of wasted effort.
    
• Marketing-Unfriendly Outputs:
  
  • Bayesian and evolutionary methods prioritize statistical fit over realism.
    
  • Marketers need controllable, explainable models—not black-box approximations.

A Better Way Forward

Given these frustrations, what should MMM practitioners do?

Hybrid Approaches (Pre-Optimization + Bayesian / Evolutionary)

• Use constrained regression (OLS/LASSO) first to get reasonable priors before running Meridian.
  
• Feed business-logic bounds into Robyn’s fitness function to reduce unrealistic outputs.

Custom Constraints & Post-Hoc Adjustments

• Enforce ROAS floors/ceilings and contribution sanity checks after model runs.
  
• Manually override implausible coefficients when necessary (even if it breaks "pure" statistical rigor).

Alternative Tools with More Control

• Reimplementing core algorithms with constrained sampling (e.g., Hamiltonian Monte Carlo with boundaries) can help.

Conclusion: The Need for Marketing-Centric MMM Tools

Google Meridian and Meta Robyn are powerful in theory but inefficient in practice. Their lack of control, slow iterations, and unrealistic outputs make them frustrating for real-world MMM projects.

Until these tools introduce:

✅ Real-time tuning during model runs
✅ Stricter business-logic enforcement
✅ Faster convergence with guided sampling

MMM practitioners will continue to bleed time and sanity in the trenches of model tweaking.

The industry needs better solutions—ones that respect both statistics and marketing reality. Until then, modellers will keep resorting to manual hacks, workarounds, and hybrid approaches just to deliver usable results.