Practical Guide to eCommerce Attribution in 2025

Is spending on ads growing your business, or simply oozing money into the Facebook and Google void?

Every founder I talk to is wrestling with this same demon. 

Right now, you're probably making decisions about where to spend your marketing budget based on what looks good in your dashboard. 

But what if you could know, with confidence, which efforts are truly moving the needle versus just claiming credit for sales that would have happened anyway?

This is where attribution comes in. At its core, attribution is about understanding which of your marketing efforts are actually driving real growth.

But attribution isn't just some boring technical detail you can delegate to your marketing team. 

It's the difference between scaling to 8 figures and slowly bleeding your business dry while convincing yourself "the brand awareness is worth it."

The good news? 

Once you understand how to look at attribution the right way, you can build a feedback loop that actually tells you:

• Which ads are making you money (for real, not just claiming credit)
• Where you're leaving money on the table
• How to scale what's working without kidding yourself

Think of attribution like a detective story. 

Your customer touched 7 different marketing channels before buying - so who gets the credit?

The first ad that caught their eye? The last click before purchase? That influencer they follow?

We're going to unpack all of this, but not in the usual "you're doing it wrong" way. 

Ready to get your hands dirty with some real talk about attribution for eCommerce?

Prefer to watch? Check out the full video breakdown here:‍

Ready to get your hands dirty with some real talk about attribution? 

What Attribution Really Means (And Why It's Not Working Like You Think)

Let's start with a truth bomb: At its core, attribution is about figuring out which of your marketing moves are actually driving real growth. 

Without it, you're basically throwing darts in the dark.

A Simple Example That Changes Everything

Your brand is crushing it with $100,000 in monthly revenue, all organic. No ad spend. Life is good.

Then you decide to dip your toes into the ad game. You throw $10,000 at some campaigns, and your revenue bumps up to $105,000.

On paper, your media efficiency ratio (MER) looks fantastic - it's 10.5. Most founders would pop champagne over those numbers.

But hold up. 

In reality, you just spent $10,000 to make an extra $5,000. That's a 0.5 ROAS. 

Not so champagne-worthy anymore, right?

Why Real Life Is Even Messier

If only it was that simple in the real world. Your baseline revenue is bouncing around like a ping pong ball, and you're running ads across more platforms than you can count on both hands.

This is where attribution tries to save the day. It uses a cocktail of:

• Cookies and pixels
• UTM codes
• Fancy modeling
• Platform tracking

How Attribution Should Work (In Theory)

The platforms use a combination of tech wizardry to track users:

• Pixels on your website
• Tags in ad URLs
• Platform-specific tracking

When it works, it's beautiful. The ad platform can say, "Hey, this person saw your ad, clicked it, went to your site, and bought something. You're welcome!"

And you, as the brand owner, can make seemingly logical decisions: "Cool, I spent $10 on that ad and made $100. Let's double down!"

But here's where things get interesting (and by interesting, I mean potentially problematic)...

When Attribution Goes Wrong (And It Usually Does)

Let's dive into why your attribution data might be lying to you (and what to do about it).

The Signal Loss Problem

Remember when iOS 14.5 dropped and every Facebook advertiser lost their mind? There's a reason for that.

Privacy policies and tech changes are making it harder for platforms to track user behavior.

Think about it - someone sees your ad, loves it, buys your product... but the platform is blind to it.

The scary part? You might be cutting spend on campaigns that are actually crushing it, just because the data is missing.

The Multi-Device Maze

Here's a scenario that happens all day, every day: Someone sees your ad on their phone during lunch break, thinks "neat, I'll check that out later," and then buys from their laptop after dinner. 

Meanwhile, your attribution model is having an existential crisis trying to connect these dots.

Sure, cross-device tracking exists, but it's about as reliable as a chocolate teapot.

The Double-Counting Trap

This is where things get really messy. Let me paint you a picture of a typical customer journey:

1. A customer spots your product on Instagram and clicks through to check it out.
2. Facebook immediately starts planning how to spend their commission. 
3. The next day, that same customer Googles your brand name and clicks on a search ad before making their $100 purchase.

Now your dashboards are telling you different stories: 

• Facebook proudly reports $100 in revenue. 
• Google's also claiming that same $100. 

Meanwhile, your Shopify account is sitting there with just $100 in actual revenue, wondering why everyone's fighting over its lunch money.

Each platform is claiming they're driving value, when in reality, they're just squabbling over who gets credit for a purchase that could have happened anyway.

Understanding Attribution Models: A No-BS Guide

Let's cut through the marketing jargon and talk about how these models actually work in the real world.

Click-Through vs. View-Through: The Battle for Credit

Facebook's default attribution setup (7-day click, 1-day view) sounds fancy, but here's what it really means: Facebook wants to take credit for as much as possible. Shocking, right?

Think of view-through attribution like that friend who says they "basically introduced" the couple at a wedding, just because they happened to be at the bar where they met.

Here's the real deal with views: They're not completely useless - TV ads have worked this way forever. 

But Facebook knows who's already been to your website, making it really easy for them to strategically show ads to people who were probably going to buy anyway.

The Click Wars: First vs. Last

Another set of models to be aware of, which you'll see on Google, are going to be first-click versus last-click versus linear versus data-driven. 

Let's say someone discovers your brand through a Google search for "best water bottles," clicks your ad, checks out your site, and leaves. 

A week later, they Google your brand name directly, click another ad, and finally buy.

Who deserves the credit?

Under first-click attribution, that initial "best water bottles" ad gets the gold star. 

With last-click, it's the branded search that wins. 

Plot twist: They're both wrong. And right. 

Now, you can decide which ad actually was more impactful in terms of driving that person to make a purchase from you.

I would argue in this case the first ad; however, in different user journeys, it's not always that simple.

In a scenario where a user has multiple ad touches, meaning that they interact with several ads before making a purchase, you also have linear or data-driven modeling. 

Linear just means that it'll split the credit of that user's purchase evenly across all the ad touches, and data-driven is just Google's modeling to decide which one of those touches should deserve more credit.

The Attribution Window Game

Here's something that'll make your head spin: Facebook gives themselves 7 days to take credit for a purchase after someone clicks an ad. 

Google? They help themselves to a generous 30 days.

Understanding the differences between these windows and how users interact with each one of those ad platforms can help you interpret the data a lot better.

If both platforms show a 3X ROAS but Facebook's working with a much shorter window, you might want to rethink which platform is really bringing home the bacon.

It's possible that a person who had never heard of you will see an ad on Facebook, click on it, go to your website, leave, and then come back again through a Google branded search. 

In that case, you could argue that Facebook really deserves the credit. 

However, it's also possible that a person did a non-branded search, saw an ad, went to your website, then left, and then Facebook started delivering a whole bunch of remarketing ads to them, which they eventually clicked on and then made a purchase. 

So taking all that into consideration, you can see how it can be difficult to interpret attribution data easily to make good decisions. 

Making Sense of the Attribution Mess: A Practical Guide

So everyone's wrong, the platforms are greedy and half-blind - why even bother with attribution?

Well you're still spending real money right? 

And you need some way to figure out if it's working.

• Yes, Facebook and Google are basically competing car salesmen trying to take credit for the same sale. 
• Yes, iOS updates have made tracking about as reliable as weather forecasts. 
• And yes, your beautiful attribution models probably have more holes than Swiss cheese.

There's only one number that really matters: incremental revenue. 

That's the extra money your business makes specifically because of your ad spend. 

Not the revenue Facebook claims credit for. 

Not the sales Google says they drove. 

But the actual additional revenue you wouldn't have gotten without spending those ad dollars.

The goal isn't to find perfect attribution - it's to create a feedback loop that helps you make better decisions about where to put your money.

What would happen if you stop advertising?

Think about it this way: If you turned off all your ads tomorrow, you wouldn't lose all your sales (hopefully).

Your loyal customers would still buy. People would still find you through organic search. 

Your email list would still convert. The only revenue you'd lose is the incremental revenue your ads were driving.

That's why it's crucial to look beyond what the platforms tell you. 

Sure, Facebook might claim they drove $100k in sales last month, but if $80k of that was from customers who would have bought anyway, you're not really getting the full picture.

The Real Tools for Measuring Incremental Revenue

Let's talk about how to actually figure out if your ads are driving new revenue or just taking credit for sales that would have happened anyway.

Platform Data vs Reality

Yes, look at what Facebook and Google are reporting, but remember - they're not measuring incremental revenue. 

They're measuring correlation ("this person saw an ad and bought something") not causation ("this person bought because of our ad").

The Media Efficiency Reality Check

No single platform can tell you the truth about incremental revenue. 

You want to look at your overall media efficiency ratio to see that you're spending an adequate amount for the amount of revenue that you're making. 

Your True North: New Customer Revenue Ratio

Take all your new customer revenue (total revenue from first-time buyers) and divide it by your total ad spend. 

Simple, but powerful. Here's why:

• It ignores all the noise about which platform deserves credit
• It tells you if you're actually growing your customer base
• It keeps you honest about your true customer acquisition cost

And if you take all of that data and apply a bunch of experience and judgment, hopefully, you can make better decisions to optimize your marketing. 

Better Measurement Tools (That Actually Matter)

Third-party tools like Triple Whale or Northbeam are useful, but not because they solve attribution. 

They're valuable because they help you spot trends in new customer acquisition and actual revenue growth.

Here's what to watch for:

• Sudden drops in new customer revenue (even if total revenue stays stable)
• Rising ad costs without corresponding rises in new customer revenue
• Channels that consistently bring in actual new customers (not just claiming credit for existing ones)

The Incremental Testing Framework

Want to really know if your ads are driving incremental revenue? Try this:

1. Pick a test market or time period
2. Significantly change your ad spend (up or down)
3. Watch what happens to new customer revenue
4. Compare against a control group or period

Yes, it's messy. Yes, it's imperfect. But it's a hell of a lot better than trusting platform attribution blindly.

Remember: The goal isn't to track every dollar perfectly. It's to know if your marketing spend is actually growing your business or just making the ad platforms richer.

Best eCommerce Attribution Model?

Look, at the end of the day, you need to make decisions about where to put your money. 

Here's how to actually use all this stuff without driving yourself crazy:

Stop chasing perfect attribution - it doesn't exist. 

Instead, focus on whether your total business is growing profitably.

The platforms will always fight over credit. Your job is to make sure your bank account is actually growing while they argue.

Remember what actually matters:

Your baseline revenue (what you'd make with zero ad spend) + your incremental revenue (what the ads actually add) = total revenue

If that equation isn't working in your favor, all the fancy attribution models in the world won't help you.

Some Real Talk About Growth

You're probably spending more on ads than you should on some channels and not enough on others. 

That's okay. The goal is to get better at spotting the difference.

Watch for these signs that your ads are actually driving incremental growth:

• New customer revenue is growing faster than ad spend
• You can cut ad spend on a channel and your revenue doesn't immediately tank
• Your customer acquisition cost makes sense for your lifetime customer value

If you're still feeling uncertain about your marketing strategy, that's normal. This stuff is hard. 

Remember - it's better to be roughly right about incremental revenue than precisely wrong about attribution.

The brands that win aren't the ones with perfect attribution. They're the ones who understand which marketing efforts actually drive new customer growth.

So if you're still feeling uncertain about your marketing strategy, feel free to reach out. I work with brands to help them optimize for growth. 

And in the meantime, if you're looking to increase your revenue without spending so much on ads, you might enjoy this video on how to optimize your Shopify store's SEO.