# Why Your Attribution is Lying and How MMM Fixes It
I’m going to start with a hard truth that most digital agencies in Sydney or Melbourne would rather keep under wraps: Your Google Analytics 4 (GA4) dashboard is probably lying to you.
Not because the software is broken, but because the way we track marketing in 2026 has become a fragmented mess. If you’re a business owner in Brisbane, whether you’re running a multi-location retail chain in Chermside or a growing B2B firm in Milton, you’ve likely felt that nagging suspicion that your "Last Click" data isn't telling the whole story.
You see a sale come through, GA4 says it came from "Organic Search," but you know for a fact that customer saw three of your Facebook ads, read your email newsletter, and heard your spot on Triple M last week.
This is where Marketing Mix Modeling (MMM) comes in. For a long time, MMM was the plaything of Coca-Cola and Westpac—multi-million dollar statistical projects that took six months to complete. But the game has changed. Today, MMM is the only way to get a truthful view of your ROI without getting bogged down in the "privacy-first" tracking nightmare that has rendered cookies almost useless.
In this deep dive, I want to strip away the academic pretension. We’re going to look at the common mistakes businesses make when trying to measure their marketing and how a simplified approach to MMM can actually help you scale your profit, not just your "likes."
The Great Tracking Myth: Why We’re All a Bit Lost
Let’s be real for a second. We were sold a dream ten years ago: "Digital marketing is 100% trackable."
It was a lie then, and it’s a total fantasy now. Between Apple’s iOS updates, the death of third-party cookies, and the fact that people switch between devices more often than they change their socks, the "path to purchase" looks less like a straight line and more like a bowl of spaghetti.
I’ve seen business owners get absolutely paralyzed by their data. They see their Facebook ROAS (Return on Ad Spend) drop from 4.0 to 1.5 and they panic. They cut the budget, only to find that their total revenue drops by 30% two weeks later. Why? Because Facebook was doing the heavy lifting of introducing people to the brand, even if they didn't click the ad and buy immediately.
When you rely solely on attribution software, you are measuring noise instead of actual business impact. You're rewarding the channel that happened to be the last thing the customer touched, rather than the channel that actually convinced them to buy.
Mistake #1: Confusing Correlation with Causation
This is the biggest trap in the book.
Imagine you’re running an EOFY sale. You blast out emails, you ramp up Google Ads, and you put a big banner on your shopfront in Fortitude Valley. Sales go up. Your Google Ads dashboard claims credit for $50,000 in revenue.
You think, "Great! Google Ads is a goldmine!"
But wait. How many of those people would have bought anyway because of the sale? How many saw the shopfront banner and then searched your name on Google to find your hours? Google Ads didn't cause the sale; it just captured it.
MMM is designed to solve this by looking at the big picture. It uses statistical regression to look at your total sales over time and see how they fluctuate alongside your spending in different channels, while also accounting for "base sales" (the sales you’d get even if you spent zero on marketing), seasonality, and even external factors like the weather or interest rate hikes.
Mistake #2: The "Data Graveyard" Syndrome
I see this constantly with Brisbane SMEs. They have access to more data than ever before—GA4, Shopify reports, Meta Ads Manager, HubSpot—but they don't actually use it to make decisions. They’ve built what I call a "Data Graveyard."
If you're spending four hours a week moving numbers from one spreadsheet to another just to see a line graph that doesn't tell you what to do next, you're wasting your time. You need to stop building data graveyards and start looking for the statistical relationships that actually matter.
MMM isn't about tracking every single click. It’s about understanding the incrementality of your spend. If I spend an extra $1,000 on LinkedIn next month, what is the predicted lift in total revenue? That’s the only question that matters to your bottom line.
How Simplified MMM Works (Without the PhD)
Most people's eyes glaze over when you mention "Bayesian statistics" or "Multi-variate regression." So let’s keep it simple.
Think of MMM like a recipe. Your total sales are the finished cake. The ingredients are your marketing channels: $2,000 of Meta Ads $1,500 of Google Search 10,000 Email sends 1 Local Radio spot
But there are also "environmental" ingredients you didn't buy: It was a rainy weekend in Brisbane (so more people were shopping online) It was a public holiday Your competitor ran a 50% off sale
MMM takes all these ingredients over a period of 12 to 24 months and calculates exactly how much each ingredient contributed to the final cake. It might find that for every $1 you spend on Radio, you get $5 back, but for every $1 you spend on Google Search, you only get $1.20 back because you're already ranking #1 organically.
The Three Pillars of a Simplified Model
1. The Base: These are your "autopilot" sales. If you turned off all ads tomorrow, what would you still sell? This is driven by your brand reputation, your physical location, and your existing customer base. 2. The Incremental Lift: These are the sales directly attributable to your marketing efforts. This is what we’re trying to optimise. 3. External Factors: This is the "real world" stuff. For a client of ours in the home improvement space, we found that their sales were more closely correlated with the temperature in South East Queensland than their actual ad spend. When it gets hot, people buy shutters. No amount of Facebook ads can override a cold snap.
Mistake #3: Ignoring the "Adstock" Effect
Marketing doesn't always work instantly. This is where most basic reporting fails miserably.
If someone sees your ad on Monday, they might not buy until Friday. In the world of MMM, we call this "Adstock" or "Carryover Effect." The impact of an ad decays over time.
Digital tracking usually gives all the credit to Friday. MMM recognizes that the "decaying" influence of Monday’s ad contributed to the sale. If you ignore this, you’ll constantly undervalue brand-building activities and overvalue "bottom-of-the-funnel" activities like Remarketing.
I’ve seen businesses kill their brand awareness campaigns because the "direct ROAS" looked low, only to see their entire funnel dry up three months later. It’s a classic mistake, and it’s painful to watch.
Why Brisbane Businesses Should Care About MMM Now
We are currently in an era where first-party data is the only thing you can truly rely on. Privacy laws in Australia are tightening, and the big tech giants are giving us less and less visibility into individual user journeys.
MMM doesn't care about individual users. It doesn't need to know that "John from Indooroopilly" clicked a link. It only needs to know that you spent $X and you made $Y. It is inherently privacy-compliant and future-proof.
The "Small Business" Objection
"But I’m not Suncorp! I don't have a data science team!"
I hear you. And five years ago, you’d be right. MMM was too expensive for a business doing $2M or $5M in turnover.
But today, open-source tools (like Meta’s Robyn or Google’s LightweightMMM) and specialized agencies (like us) have made this accessible. You don't need 50 variables. You can start with a "Simplified Mix Model" using just 4 or 5 key inputs.
Practical Steps: How to Start Small
If you want to move away from lying dashboards and toward a more honest marketing mix, here is the roadmap I give our clients at Local Marketing Group.
1. Clean Up Your Historical Data
Before you can model anything, you need clean data. This means ensuring your sales data is consolidated. If you have an e-commerce store and a physical POS system, they need to be talking to each other.2. Track Your "Non-Digital" Spend
Start a simple spreadsheet. Every week, record what you spent on every channel—even the ones that don't have a "dashboard." Radio, local sponsorships (like the local footy club), print, and even the time your team spends on organic social.3. Identify Your External Variables
What actually affects your business? Seasonality: Are you busier in the lead-up to Christmas? Economic Indicators: Does the RBA interest rate announcement affect your leads? Competition: When your main competitor runs a sale, does your traffic dip?4. Run an Incrementality Test
This is the "poor man’s MMM." Pick one channel—let’s say Facebook Ads—and turn it off in one specific region (e.g., just the Gold Coast) for two weeks while keeping it on in Brisbane. Compare the sales trend. Did the Gold Coast sales drop significantly, or did they stay steady? This gives you a baseline of how much that channel is actually contributing.The "Diminishing Returns" Trap
One of the most powerful insights MMM provides is the "Saturation Curve."
Every marketing channel has a limit. If you spend $1,000 on Google Ads and get a 5x return, it doesn't mean spending $100,000 will give you the same return. Eventually, you run out of people actively searching for your product, and the cost to acquire the next customer skyrockets.
Most agencies will just tell you to "spend more" because their fees are often tied to a percentage of ad spend. A simplified MMM will show you exactly where the "point of diminishing returns" is, so you can stop wasting money and move those dollars to a channel that isn't saturated yet.
Common Industry Lies to Watch Out For
I promised to be opinionated, so here we go.
If an agency tells you they can give you a "perfect, real-time attribution model," they are either lying to you or they don't understand how modern browsers work. There is no such thing as perfection in marketing measurement. There is only "less wrong."
MMM is "less wrong" than GA4 because it accounts for the world outside of the browser.
Also, beware of the "ROAS Obsession." I’ve seen businesses with a 10x ROAS go bankrupt because their margins were thin and their "base sales" were being cannibalized by their own ads. You should be looking at POAS (Profit on Ad Spend) or MER (Marketing Efficiency Ratio).
MER is simple: Total Revenue / Total Ad Spend. It’s a blunt instrument, but it’s often more honest than a filtered Meta Ads report.
Case Study: The Brisbane Retailer Who Thought Radio Was Dead
We worked with a local furniture retailer who was convinced their radio spend was a waste of money. Their digital agency told them to put everything into Google Search because "that's where the sales are happening."
We ran a simplified mix model for them. What we found was fascinating: Every time they ran a heavy radio flight, their "Direct" and "Organic Search" traffic spiked by 40%. When they turned radio off, their Google Ads CPC (Cost Per Click) went up because their brand wasn't top-of-mind, making their digital ads less efficient.
Radio wasn't "dying"—it was the engine driving the digital results. Without MMM, they would have cut the radio budget and wondered why their "efficient" Google Ads suddenly stopped performing.
Conclusion: Moving Toward a Truthful 2026
Marketing in Brisbane is getting more competitive. You can't afford to make decisions based on flawed data or "gut feelings" that are influenced by shiny dashboards.
Marketing Mix Modeling isn't about finding a magic button. It’s about getting a realistic, holistic view of how your business actually grows. It’s about realizing that your email marketing, your local community involvement, and your digital ads all work together in a complex ecosystem.
Stop chasing the ghost of "perfect tracking." Start looking at the relationships between your spend and your total profit.
If you’re feeling overwhelmed by your data, or if you suspect your current reporting is missing the forest for the trees, let's have a chat. We help Brisbane businesses cut through the noise and build frameworks that actually drive growth.
Ready to stop guessing? Contact Local Marketing Group today and let’s look at what’s actually driving your profit.