The $10M+ Attribution Model Framework

Welcome back to the 133rd edition of Nord Media

​​Attribution is one of those levers that quietly controls millions of dollars in scaling decisions.

Brands doing $5M a year tend to default to whatever model their ad platform was showing. That’s fine until they cross $20M, $50M, $100M+.

At that level, a 5–10% reporting discrepancy can mean pulling spend from the channel actually driving growth.

The models look simple on paper:

Linear, Time Decay, Position-Based, U-Shaped, W-Shaped.

But choosing the right one comes down to aligning model logic with your customer journey, not gut feeling.

In this email, we’ll break down:

• How to analytically test attribution models using your real customer journey data

• An implementation guide for structured model A/B testing

• Strategic considerations for final model selection based on purchase cycle length, AOV, and channel mix

Let’s get into it:

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Aligning Model Logic With Purchase Behavior

Every attribution model is just a weighting formula applied to your customer journey. The mistake people make is assuming there’s a default “right one.”

If you sell skincare with a 3–5 day purchase cycle, a Position-Based model tends to reflect reality. First touch and last touch are both important.

If you sell home appliances with a 30–45 day cycle, Time Decay or W-Shaped models capture those layered research and decision touchpoints better.

At $100M+ in spend, brands are not choosing models based on platform defaults. They’re mapping it against real journey data:

• How long is the median time from first touch to purchase?
  

• How many channels are in play?
  

• What percentage of purchases happen within 1 day vs. 30 days?
  

If you have access to GA4, Rockerbox, or a customer data platform, pull those timelines first. That baseline shapes your model testing.

Structuring Attribution Model A/B Tests

Choosing attribution isn’t a one-click swap. It’s an analytical process. 

Here’s the structure top brands use:

• Set two attribution models to run side by side.
  For example: Linear vs. Time Decay, or Position-Based vs. U-Shaped.

• Keep all campaign variables stable: budget, targeting, creative, and landing pages.

• Run the comparison for at least 30–60 days to smooth out short-term noise.

• Compare outcomes beyond platform-reported ROAS. Look at backend revenue, repeat purchase rates, and customer LTV.

The goal is not to pick the model with the prettiest dashboard. It’s to pick the one that holds up across reporting tools and correlates with cash collected.

Tools like Rockerbox and Wicked Reports make this easier, but the process still requires manual alignment across teams.

Building a Repeatable Model Selection Framework

Once the test period is done, it comes down to making a choice and documenting it as part of your growth system.

Here’s how larger brands approach it:

• Short Cycle, Low AOV: Skincare, supplements, and apparel brands tend to favor Position-Based or U-Shaped. Those models balance first and last touch effectively.

• Long Cycle, High AOV: Furniture, appliances, and B2B products lean toward Time Decay or W-Shaped. They weigh mid-funnel touches properly without over-crediting last-click.

• Omnichannel Mix: Brands layering retail, online, and paid channels often customize their model weights entirely. Tools like Rockerbox can handle that, but it requires deeper setup.

Important: Model selection is not static. 

Re-check it every 6 to 12 months, especially if product mix or customer journey timelines shift. Treat attribution like media buying. It needs optimization.

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Final Thoughts

Attribution is rarely exciting. It doesn’t get the same attention as creative testing or offer development, but the brands that win consistently at scale are quietly running these types of systems behind the scenes.

The difference between a channel looking like it is capped and realizing there is still 20–30% more to unlock often comes down to model accuracy.

Struggling to grow? My team and I can help.

HOW??

We Use Strategies and Systems that Produce Consistent Results.

We currently work with a small handful of brands from early-stage DTC startups to global brands scale smarter, grow faster and get profitable. Whether your goal rapid growth, consistency at scale or just to be profitable again, we've built systems and strategies to achieve that for hundreds of brands over the years.

The results? You get reliable and consistent growth without sacrificing your profitability.

Sound interesting and worth a conversation? Book a call

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Thank you for reading! I appreciate you.

Sincerely,
Kody

Disclaimer: Special thanks to Patrol & Customers.ai for sponsoring today’s newsletter.