Imagine you’re running a startup. You’ve put your heart into building an amazing product. You launch your website and invest in marketing. Ads, socials, emails — you do it all. But here’s the big question: Which part actually worked?
This is where attribution comes in. Attribution tells you what’s driving results. It helps you spend smarter. But if you’re like many startups today, you’re probably worried about privacy. New rules (like GDPR and Apple’s App Tracking changes) make tracking people harder.
That’s a good thing for users, but it makes things tricky for marketers. So what’s the solution?
Enter: MMM (Marketing Mix Modeling)
MMM sounds fancy, but it’s pretty simple. It’s a method from the early days of marketing. No cookies. No personal info. Just data and math.
MMM looks at your marketing channels and compares them with business outcomes over time. It’s like telling a story using numbers. You look at:
- Your spend on different channels (like Facebook ads, TV, influencers).
- Time-based data (like sales or signups per week).
- Other stuff that affects results (like holidays, news or seasons).
The model finds patterns and gives credit to each channel accordingly. It’s magic, but with math. And it’s completely privacy-safe.
Why Startups Should Care
Big companies have used MMM for years. But today, startups can also use it. Thanks to new tools and open-source software, MMM is more accessible than ever.
Here’s why it’s great for startups:
- No user tracking required. You don’t need to worry about consent popups or browser limits.
- Channel-neutral. MMM works the same across paid, owned, and earned media.
- Budget-friendly. You can run MMM with your own team. No need for expensive consultants.
- Get the big picture. Understand what’s working beyond clicks and conversions.
And in a privacy-first world, it helps you stay compliant and competitive.
How It Actually Works
Okay, let’s break it down. This is a simple version of an MMM process for a startup:
- Gather your data. You need at least 12-24 months of weekly data. If you’re newer, even 6 months can give early insights.
- Include all marketing spend. Facebook, Google, Meta, LinkedIn, influencers — all of it.
- Add control variables. Things like seasonality or changes in pricing.
- Build a model. You can use libraries like Robyn (by Meta) to run your first model.
- Analyze the results. The model tells you which channels contribute most to your growth.
It’s not perfect. But it’s super powerful when you get the hang of it.
What Makes MMM Privacy-Safe?
Most tracking methods today collect personal data. Pixels, cookies, IDFA – all of it relies on tracking individuals. That’s what makes it risky for privacy and compliance.
MMM doesn’t use any personal identifiers. It works with aggregated data. That means it looks at trends for the group, not for individuals. It’s like looking at the whole forest, not each tree.
This makes MMM:
- GDPR-safe
- CCPA-compliant
- Post-cookie ready
No legal headaches. No tracking wars. Just insights.
Common Pitfalls (And How to Avoid Them)
MMM is great, but it has its challenges — especially for startups. Here are some common traps to avoid:
- Too little data. The model needs time-based trends. Daily data is noisy. Aim for weekly-level insights over several months.
- Forgetting outside factors. Remember to include changes like a big PR hit, funding round, or pandemic wave. These affect results too.
- Blind trust in the model. MMM is a guide, not a gospel. Use it with judgment and test what it recommends.
If you treat it like a GPS, and not a crystal ball, it’ll take you far.
MMM vs. Other Attribution Models
Let’s compare MMM with other types of attribution:
| Method | Tracks Users | Good for Startups? | Privacy Safe |
|---|---|---|---|
| Last-click | Yes | Not Really | No |
| Multi-touch | Yes | Maybe | No |
| Lift tests | No | Costly | Yes |
| MMM | No | Yes | Yes |
MMM wins big when you can’t track individuals and need reliable insight.
Making MMM Fun (Yes, Really)
If you’re thinking all this talk of data and modeling sounds dry — don’t worry. MMM can actually be fun. Like grown-up detective work.
You start asking questions like:
- “Did our spike in sales come from that influencer post?”
- “Did that podcast ad actually work?”
- “Can we cut TikTok and still grow?”
Then you let the model tell the story. As you uncover patterns, you’ll feel like Sherlock Holmes of marketing.
Tools You Can Use
You don’t need to be a data scientist to start. There are great MMM tools built for marketers and founders.
- Robyn – Open-source tool by Meta. Requires some Python or R knowledge.
- Lightweight MMM – Python-based, beginner friendly, great for small teams.
- Recast – Paid tool offering MMM with easy reports and startup focus.
- Growth Modeling libraries – For those a bit more technical who want flexibility.
Start small. Plug in your data. See what insights come up.
Final Thoughts
The future of marketing is privacy-safe. That’s clear. MMM gives you a way to adapt without losing the insights you need. It’s like night-vision goggles for your data — you see clearly even in the dark.
For startups that want to grow smart and fast, it’s a no-brainer. No need to rely on black-box analytics. No tech giants watching your back. Just you, your data, and smart decisions.
Take a weekend. Open a spreadsheet. Fire up an MMM tool. You might be surprised what you learn!