More Than Data

The Rise of "Wrapper" MMM SaaS: A Shortcut That Falls Short for Marketers

Apr 13, 2025By More Than Data
More Than Data

The Open-Source Gold Rush in Marketing Mix Modelling

In recent years, a growing number of SaaS businesses have entered the Marketing Mix Modelling (MMM) space by taking a shortcut: wrapping open-source packages like Google Meridian, Google Lightweight, and Meta Robyn into commercial products.

At first glance, this seems like a smart play—why build from scratch when you can leverage free, publicly available code? These companies quickly repackage existing models, add a UI/UX layer, and sell it as a premium SaaS solution. No heavy R&D, no complex algorithm development—just a fast path to market.

But while this approach saves time and cost for the vendor, it fails to solve the real pain points of marketers. The fundamental flaws of these open-source tools—slow computation, tedious parameter tuning, and unintuitive outputs—remain unaddressed. The result? Frustrated marketing teams stuck with inefficient tools, just now in a prettier interface.

At More Than Data, we refuse to take this shortcut. Instead of wrapping flawed open-source models, we’ve built our own marketer-first MMM solution—one that prioritizes speed, usability, and real-world business impact over technical vanity.

The Wrapper MMM SaaS Trend: Fast Money, Lasting Problems

Why MMM SaaS Companies Are Taking the Open-Source Shortcut

  • No Need for Heavy R&D
    1. Google and Meta have already done the core statistical work.
    2. Vendors minimise or even skip algorithm development and focus on UI/UX.
  • Faster Time-to-Market
    1. No need to build a model from scratch—just integrate existing code.
    2. Launch a "new" MMM product in few weeks, not years.
  • Lower Development Costs
    1. Avoid hiring expensive data scientists to design proprietary models.
    2. Focus resources on sales and marketing instead.

The Hidden Flaws of Wrapper MMM SaaS

While this approach benefits vendors, marketers end up with the same underlying problems:

  • Slow & Inefficient Computation
    1. Google Meridian & Lightweight MMM rely on Bayesian MCMC sampling, which is computationally heavy.
    2. Meta Robyn uses evolutionary computing, which can take hours (or days) for large datasets.
    3. Wrapper SaaS products inherit these inefficiencies—adding a UI doesn’t make the models faster.
  • Tedious Parameter Adjustments
    1. Media curves, adstock decay, saturation points—all require detailed tuning.
    2. Non-technical marketers can’t easily modify these—these “simplified” interfaces don't eliminate the need for technical expertise; they merely relocate the problem from code editors to dropdown menus. Frustrating back-and-forth re-runs just to get usable outputs.
  • Opaque "Black Box" Results
    1. Many wrapper SaaS tools hide the model workings behind a sleek dashboard.
    2. Marketers get outputs without understanding how they were derived.
    3. No transparency into why certain channels are deemed more effective.
  • Same Old Problems, Just in Another New Package
    1. If the core model is flawed, wrapping it in SaaS doesn’t fix it.
    2. Marketers still struggle with slow, inflexible, and hard-to-trust outputs. 

The AI Wrapper Parallel: A Cautionary Tale

This trend mirrors what’s happening in the AI SaaS space, where many startups simply:

  • Call OpenAI’s API (or another LLM provider).
  • Build a UI on top.
  • Sell it as their own "AI solution."

The problem?

  • Clients don’t realize they’re just paying for a UI over someone else’s model.
  • No real innovation—just repackaging.
  • When the underlying model has limitations, the wrapper inherits them.

The same is happening in MMM SaaS today.

More Than Data’s Approach: Solving Real Pain Points, Not Just Wrapping Code

We refuse to take the easy path. Instead of repackaging flawed open-source models, we built our own marketer-first MMM solution from the ground up.

How We’re Different:

  • Speed That Matches Marketing Cycles
    1. Proprietary engineering delivers MMM at unprecedented speed.
    2. Get results in minutes, not days.
  • Built for Non-Technical Marketers
    1. No coding or data science skills needed.
    2. Free data prep tools to easily format inputs.
  • Business-Driven, Not Statistically Obsessed
    1. Set your own commercial expectations (e.g., Anchor the model to your business intuition - if TV should drive 12% of sales, we'll deliver results that respect your expertise).
    2. Adjust media impact assumptions intuitively—no Bayesian priors required.
  • Transparent & Actionable
    1. No black-box outputs—see and trust how decisions are made.
    2. Clear, decision-making friendly reporting.
  • Listening to Real Marketers
    1. We don’t self-enjoy as data scientists.
    2. We solve real frontline marketing challenges.
       

Conclusion: The Future of MMM Belongs to True Innovators

The wrapper MMM SaaS trend is a shortcut, not a solution. While it helps vendors make quick money, it does little to advance the field or serve marketers better.

At More Than Data, we believe MMM should:

✅ Be fast enough for real-time decisions.
✅ Empower marketers, not restrict them.
✅ Deliver clear, actionable insights—not just statistical outputs.

We’re not here to repackage old problems in a new UI. We’re here to solve them.

Final Thought

If your MMM provider’s "innovation" is just a wrapper around free open-source code—are you really getting a better solution, or just a more expensive version of the same flaws (The secret sauce? Same free, questionable open-source recipe - now with a 500% markup 😊)?

At More Than Data, we’re building the alternative.