The Great MMM Expectation Gap: Why Marketers Get Frustrated (And How to Fix It)

Introduction: The Magical Thinking Trap

Picture this:

A marketer walks into a dimly lit room where a hooded figure—the MMM Modeller—sits behind a glowing screen. The marketer drops a USB drive on the table and says:

"Here’s my data. Tell me exactly how much ROI my TikTok ads are driving. And make it snappy."

The modeller sighs. "If only it worked that way."

This, dear readers, is the Great MMM Expectation Gap—the chasm between what marketers think MMM does and what it actually requires.

In this article, we’ll explore:

✅ Why MMM isn’t a magic "ROAS calculator" (Sorry, no crystal balls here.)

✅ The dirty secret: Human tweaks make the model "work"

✅ Why you (yes, YOU) must lead the MMM project

✅ How bad data habits sabotage your results

✅ Dashboards vs. MMM: Why surface metrics lie

Buckle up. We’re about to align expectations with reality—with a few laughs along the way.

Chapter 1: "MMM Should Just Automatically Give Me Perfect Answers!"

The Marketer’s Dream

"I uploaded my data. Where’s my perfect ROAS breakdown?"

Many marketers assume MMM is like a vending machine:

• Insert data.
  
• Press button.
  
• Receive flawless insights.

Reality check: MMM is more like baking sourdough.

• You need the right ingredients (clean data).
  
• You must knead the dough (tweak model parameters).
  
• Sometimes, it flops (and you start over).

Why MMM Isn’t Magic

Non-linear transformations aren’t automatic.

• • Adstock rates? Someone (a human) must set them.
    
  • Diminishing returns curves? Also human-tuned.

Models need guardrails.

• • If TikTok’s ROAS comes back as "500%," the modeller knows that’s nonsense and adjusts.

Garbage in = Garbage out.

• • If your data is messy, the model will be too.

The Fix:

• Accept that MMM requires iteration.
  
• Demand transparency on how parameters are set.

Chapter 2: "I’ll Just Send the Data and Check Out Mentally."

The Marketer’s Hope

"I’ve got a modeller on the case. My work here is done."

Reality: You’re the project lead.

The modeller is your sous-chef—not the head cook.

Why You Can’t Ghost Your MMM Project

You know your business better than the modeller.

• • Was there a supply chain hiccup in Q3? Tell them.
    
  • Did you test a new creative in June? Flag it.
    

Your expectations shape the model.

• • Example: *"TV’s impact should be 20-40%, not 5%."*
    
  • Without guidance, the modeller flies blind.
    

The Fix:

• Set clear boundaries for key metrics (e.g., ROAS ranges).
  
• Review interim outputs—don’t wait for the final report.

Chapter 3: "If the Model’s Wrong, It’s the Modeller’s Fault!"

The Marketer’s Defense

"I didn’t build this thing. Blame the nerds!"

Reality: You signed off on every step.

Shared Accountability in MMM

Classic Failures (And Who’s Really to Blame):

• "The model says billboards have 0.001% impact!" Did you tell the modeller billboards were a branding play? (Your fault.)
  
• "Why is ROAS so volatile?" Did you share channel budget changes? (Also your fault.)

The Fix:

• Treat MMM as a collaboration.
  
• Document all inputs and feedback.

Chapter 4: "Data Quality Isn’t My Problem."

The Marketer’s Attitude

"Here’s a messy Excel file. Good luck!"

Reality: Bad data = Bad decisions.

The 3 Data Sins Marketers Commit

• Sending unaggregated logs: "here’s 4M rows of raw records!" → Modellers weep.
  
• Ignoring missing data: "we didn’t track LinkedIn spend in Q1. Oops!"
  
• No metadata: "promo_Code_47" means nothing. Label your columns.

War Story:

A client once sent "media spend" data where TikTok was labeled "TT." The modeller assumed it meant "TV." The model was… creative.

The Fix:

• Use consistent naming conventions.
  
• Provide a data dictionary.

Chapter 5: "Dashboards Are Enough. MMM Is Overkill."

The Marketer’s Argument

"My dashboard shows CTRs! What more do I need?"

Reality: Dashboards are rearview mirrors. MMM is GPS.

Vanity Metrics vs. MMM Insights

Why MMM Digs Deeper:

• Adstocking → Captures long-term effects.
  
• Diminishing returns → Finds saturation points.
  
• Baseline estimation → Separates marketing impact from organic growth.

The Fix:

• Use dashboards for monitoring, MMM for strategy.

Conclusion: Bridging the Expectation Gap

The New MMM Mindset

• MMM isn’t magic. It’s a tool that needs tuning.
  
• You’re the project owner. Modellers are your allies.
  
• Data quality is YOUR job. Don’t dump and run.
  
• Dashboards ≠ Insights. Demand deeper analysis.

Final Thought: MMM Is a Superpower (If You Use It Right)

The best marketers don’t just consume MMM—they command it.

So next time you kick off an MMM project, ask yourself:

"Am I treating this like a vending machine… or a high-performance engine?"