What Is Revenue Attribution? Who Gets Credit

One $120 Order, Three Different Answers

Say a shopper clicks your Instagram ad on Monday. Two days later they click a Google Shopping ad, and on Friday they open your email and place a $120 order. So who gets the credit? That comes down to the model you pick.

Last click: the email gets all $120.
First click: Instagram gets all $120.
Linear: each of the three steps gets $40 ($120 split three ways).

You're looking at the same order and the same data every time. The only thing that changed is the rule.

Why the Model You Pick Moves Your Ad Budget

Most stores use attribution to decide where to spend. If last click says email made every sale, Instagram looks like a waste. But Instagram may be how many shoppers first found you. Cut it and your email list may grow more slowly, and the report won't show you that link.

Where Attribution Shows Up for a Shopify Store

Shopify's marketing reports show which channels brought in sales. Every one of those numbers comes from an attribution model, so check which model a report uses before you act on it.

Ad platforms keep their own score too. Meta, Google and your email app may each count that same $120 order as theirs. Add their reports together and you could see $360 of “revenue” from one $120 sale. Your Shopify orders are what you actually sold.

UTM tags help every tool tell your sources apart. A UTM tag is a short label added to the end of a link, such as ?utm_source=instagram. If you're wondering which other reports are worth reading, check out our guide to Shopify store analytics and reporting.

Getting Credit Isn't Proof of Cause

The most common mistake is reading attribution as proof that a channel caused a sale. All it does is record which steps came before the order. Picture a dedicated buyer who already knows your brand. They search your store name, click your own Google ad and buy. Last click gives the ad full credit, but they may well have bought without it.

Want to know if a channel really adds sales? You'll need a test. Show it to one group of shoppers and hold it back from a similar group, then compare the results. That's the basic idea behind A/B testing.