Upgrading Google Meridian: A Constrained Bayesian Approach for Robust Marketing Mix Modelling

Apr 17, 2025·By More Than Data

Introduction

Marketing Mix Modelling (MMM) remains a critical tool for quantifying the impact of media and non-media variables on sales. Google’s Meridian, an advanced Bayesian MMM solution, has gained traction for its scalability and integration with Google’s ecosystem. However, marketers often struggle with its limitations—particularly in calibration, efficiency, and alignment with business demands.

This article explores:

Key shortcomings of Google Meridian
Why a constrained Bayesian approach outperforms traditional Bayesian methods
Real-world applications and success stories

The Problem with Google Meridian: Key Drawbacks

While Google Meridian offers a powerful framework for MMM, several critical issues hinder its effectiveness in real-world marketing scenarios.

Inflexible Media & Non-Media Variable Adjustments

Meridian relies heavily on prior distribution tweaking to align model outputs with marketer expectations. However, this process is:

Unpredictable: Small changes in priors can drastically alter other variables effect and modelling results.
Time-Consuming: Requires multiple iterations to reach acceptable outputs.
Inefficient: Struggles with large datasets (common in enterprise marketing).

Example: A FMCG brand spent weeks adjusting priors in Meridian, only to find that TV ad contributions were still overestimated, while digital channels were undervalued.

Difficulty in Meeting Marketer Demands for Impact Share & ROAS

Marketers need precise control over:

Media Impact Share (% contribution to sales)
Return on Ad Spend (ROAS)

Google Meridian’s unconstrained Bayesian approach makes it difficult to enforce these business requirements. Without constraints, the model may produce unrealistic media effects (e.g., negative ROAS for high-performing channels).

Forward-Thinking vs. Backward-Thinking Methodology

Forward-Thinking (Meridian’s Approach):
- Adjust priors → Run model → Check results → Repeat.
- Highly iterative, slow, and inefficient.
Backward-Thinking (Constrained Bayesian Approach):
- Start with desired ROAS/impact share → Apply constraints → Run model once.
- Faster, more predictable, and aligned with business goals.

Why Backward-Thinking Wins

✅ Reduces rework (no endless tweaking of priors).
✅ Scales efficiently (handles 100+ variables seamlessly).
✅ Guarantees business-aligned outputs.

Computational Inefficiency in MCMC Sampling

Meridian uses Markov Chain Monte Carlo (MCMC) for Bayesian regression, which:

Involves random sampling, leading to slow convergence.
Lacks constraints, making results less controllable.

For large datasets (e.g., 200+ media and marketing variables), this becomes computationally impractical.

The Solution: Constrained Bayesian Regression

To overcome these limitations, More Than Data developed a constrained Bayesian MMM framework that:

Embeds business rules directly into the model.
Uses Gibbs Sampling (MCMC) with modified conditional distributions.
Ensures outputs align with marketer expectations.

How It Works

Applying Impact Share Constraints

Each media / non-media variable is assigned lower and upper bounds for its contribution.
Example:
- TV Ads: Must contribute 8-12% of total sales impact.
- Competitor Discounts: Must have a negative impact (non-positive coefficient).

Modified Gibbs Sampling for Efficient Convergence

Traditional MCMC: Random walks lead to unstable results.
Constrained Gibbs Sampling:
- Truncates distributions to enforce constraints (e.g., only positive coefficients for paid media).
- Speeds up convergence by reducing the sampling space.

Technical Advantage

If a paid ads must be non-negative, its conditional distribution is truncated to exclude negative values.
If competitor media must hurt sales, its coefficient sampling is forced to be non-positive.

Guaranteed Business-Aligned Outputs

Unlike Meridian’s trial-and-error approach, this method:

✅ Delivers ROAS and impact shares within expected ranges.
✅ Eliminates unrealistic media effects (e.g., negative impact and negative ROAS).
✅ Scales to hundreds of variables without losing accuracy.

Real-World Success: A Furniture Brand Case Study

Challenge

A leading online & in-store furniture brand needed to measure:

174 media variables (ATL/BTL, full-funnel channels).
23 promotions (online/in-store).
48 product-level discounts.
12 bundled offers.
9 competitor campaigns
11 key seasonality factors (e.g. mid-year clearance sales)

Google Meridian failed to handle this complexity, producing unstable and unrealistic results.

Solution: More Than Data’s Constrained Bayesian MMM

Defined constraints for each variable (e.g., "Social ads must drive 4-6% of sales").
Applied Gibbs Sampling with truncated distributions.
Delivered actionable insights in days (vs. weeks with Meridian).

Results:

✅ Identified top-performing channels (e.g., integrated TV + Social + Promotions Deliver 3x Returns vs. Single-Channel Ads).
✅ Quantified competitor impact (e.g., a rival’s 20% discount reduced sales by 12%).
✅ Optimized budget allocation, boosting marketing efficiency by 35%.

Testimonial:

"We struggled for months with Google Meridian—spending countless hours tweaking priors only to get unrealistic media contributions. More Than Data’s constrained Bayesian approach was a game-changer. Their model incorporated our business rules from the start, delivering reliable ROAS and impact shares in just one run. For the first time, we had a clear, actionable view of what was really driving sales. This isn’t just an upgrade to Meridian; it’s a fundamentally better way to do MMM."

Conclusion: Why Constrained Bayesian MMM is the Future

Google Meridian has limitations that make it unsuitable for enterprise MMM. By adopting a constrained Bayesian framework, marketers can:

✅ Eliminate guesswork with business-driven constraints.
✅ Speed up analysis (no more endless re-runs).
✅ Handle large datasets (100+ variables effortlessly).

For brands seeking accurate, scalable, and marketer-friendly MMM, the choice is clear: Ditch unconstrained models and embrace backward-thinking Bayesian methods.

Ready to Upgrade Your MMM?

If you’re tired of wrestling with Meridian’s limitations, More Than Data’s constrained Bayesian MMM delivers the precision and efficiency you need.