For: People running their digital media ads or other people’s digital media ads.
Overview
- The Science of Digital Ads: How to Scale Smarter, Not Harder
- How This Lowers CPA & Scales Faster
- Why a p-Value Matters in Digital Ads
- Identify Main Effects
- Identify Interaction Effects
- Get a Clear Decision-Making System
- Final Thought: Fast, Scientific Scaling
The Science of Digital Ads: How to Scale Smarter, Not Harder
Most Media Buyers Are Guessing—You Don’t Have To
I started in clinical medical research, where I spent years studying ANOVA, factor analysis, and statistical best practices—the same frameworks that power scientific breakthroughs and drug trials. Then I hired a media buyer to train me in digital ads—and realized he had no idea about these frameworks.
But here’s the thing: Ad spend can be run like a controlled experiment.
🔹 No more gut-feeling decisions.
🔹 No more “this creative feels right.”
🔹 Just pure, data-backed optimizations that scale.
How This Lowers CPA & Scales Faster
When you test ads scientifically, you:
🚀 Eliminate noise → Stop making calls based on small sample sizes.
🚀 Scale with confidence → Know when a creative has real impact before increasing spend.
🚀 Lower CPA aggressively → Identify the best-performing combinations with certainty.
A digital agency that provides this level of statistical rigour gives clients a real competitive edge. Instead of saying “this ad seems to be working,” you can say: “this ad has a 95% confidence level of outperforming the rest.” That’s the difference between guessing and engineering success.
Why a p-Value Matters in Digital Ads
Most media buyers throw money at campaigns and hope something sticks. You can do better.
When you run ads with a scientific methodology, you’re not just optimizing—you’re running speed science to scale hard while lowering CPA.
✅ Faster iteration cycles → Test, validate, and scale what works without second-guessing.
✅ Lower wasted spend → Kill underperforming combinations statistically, not emotionally.
✅ More confidence in scaling → You know what’s working, and you know why.
But how do you know if what you’re seeing is real and not just noise? That’s where p-values come in.
A p-value tells you whether your test results are statistically significant—meaning, are the differences in performance actually meaningful, or are they just random fluctuations?
Let’s say Creative A has a $15 CPA and Creative B has a $12 CPA.
Which one should you scale?
If you’re guessing, you might be scaling randomness. A p-value tells you:
- If Creative B is actually better (statistically, not just by chance).
- How much confidence you should have before dumping budget into it.
- If you should keep testing or start scaling.
When you run ads this way, you eliminate noise, scale with confidence, and lower CPA aggressively.
Instead of saying, “This ad seems to be working,” you can say, “This ad has a 95% confidence level of outperforming the rest.”
That’s the difference between guessing and engineering success.
Identify Main Effects
You can break down how different factors influence performance:
- Which audience is stronger?
- Which creative is performing best?
- Which visuals and copy combinations work best together?
Creative 01 | Creative 02 | Ave | |
Audience 01 | $11.21 | $8.11 | $9.66 |
Audience 02 | $7.15 | $4.32 | $5.74 |
Ave | $9.18 | $6.22 |
Looking at the differences between averages shows you…
✅ Audience Effect: Audience 02 performs better overall.
✅ Creative Effect: Creative 02 is stronger across audiences.
Identify Interaction Effects
Sometimes, a combination of variables outperforms everything else.
Visual 01 | Visual 02 | Visual 01 | Visual 02 | |||
Copy 01 | Copy 01 | Copy 02 | Copy 02 | |||
Creative 01 | Creative 02 | Creative 03 | Creative 04 | Ave | ||
Audience 01 | $10.13 | $8.11 | $9.11 | $4.11 | $9.12 | Difference between averages shows you main effect of audience. Audience 02 is stronger |
Audience 02 | $8.45 | $16.14 | $10.13 | $3.12 | $12.30 | |
Ave | $9.29 | $12.13 | $9.62 | $3.62 |
Looking at isolated factors, it might seem that:
- Visual 02 is stronger.
- Copy 02 is stronger.
But when you graph everything out, you’ll see an interaction effect:
✅ The strongest performance doesn’t just come from the best visual or copy alone—it comes from Visual 02 + Copy 02 + Audience 02.
This is where ANOVA and factorial analysis shine—helping you find unexpected combinations that massively outperform everything else.
Most agencies don’t even look at this. They just “scale what works” without knowing why.
Get a Clear Decision-Making System
✅ Use my Google Sheet template (linked below) to run your own ANOVA tests.
✅ Graph your data to visualize where the strongest interactions are.
✅ Eliminate guesswork and make scientifically-backed ad decisions.
Final Thought: Fast, Scientific Scaling
When you combine p-values, ANOVA, and factorial testing, you’re not just running ads—you’re running a scientifically optimized marketing machine.
🔥 The result? Lower CPA, faster scaling, and a competitive edge no other agency is offering.
🚀 Test smarter. Scale harder. Get the data that actually matters.
Want help implementing this into your ad strategy? Let’s talk.
Checkout my templates and documentation here:
Notion Operations
Checkout my past experience here:
Completed Work
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