Shopify Shop Campaigns Analytics: How Merchants Can Measure ROAS, CAC & AOV

Launching a Shop Campaign is only half the job. The other half is a harder question: did the spend actually work? "We spent $1,000" tells you nothing on its own — what matters is how much revenue that spend generated, what it cost to acquire customers, how valuable the orders were, and which way it's all trending.

As of August 10, 2026, Shopify made that easier. Shop Campaigns performance data is now available through ShopifyQL, alongside the reporting already in the Shopify admin. That opens up a clearer set of business metrics — ROAS, CAC, AOV, sales, and customer counts. But the metrics are only useful if you understand what each one measures, and how they read together. This guide is about that.

What changed with Shop Campaign analytics in 2026?

On August 10, 2026, Shopify added the shop_campaign_insights schema to ShopifyQL. Two things are worth separating:

• Shopify admin reporting — the built-in view of your Shop Campaign performance. Good for a straightforward look at how a campaign is doing.
• ShopifyQL access — the same performance data, now queryable so it can flow into custom reports, dashboards, and analytics or BI tools. Reporting apps can query it on behalf of authorized merchants using the existing shopifyqlQuery field with the read_reports scope, with no new scopes required if they already use ShopifyQL.

In short, the numbers didn't just become visible — they became portable, so you can analyze Shop Campaign performance next to your other data instead of in isolation.

What metrics can merchants see?

The shop_campaign_insights schema exposes six core metrics, with Shopify's own definitions:

One note: the announcement mentions "orders," and the admin shows order counts, but the ShopifyQL schema exposes only the six metrics above — there's no separate "orders" field to query.

ROAS: what does it actually tell you?

Return on ad spend is the headline number, and the simplest:

ROAS = Shop Campaign sales ÷ Shop Campaign ad spend

If a campaign generates $3,000 in attributed sales from $1,000 in ad spend, ROAS is 3 — the orders associated with the campaign produced three times the ad spend in sales.

Here's the trap: a 3x ROAS does not mean the campaign is profitable. ROAS compares sales to ad spend, and nothing else. Sitting outside it are all the costs that decide whether you actually made money:

• Product margins
• Discounts applied to those orders
• Shipping and other operating costs
• Returns and refunds (Shopify's "sales" figure already excludes refunds, but not your fulfilment or return-handling costs)
• The customer acquisition cost itself

A store with 70% margins and a 3x ROAS is in a very different position from a store with 20% margins and the same ROAS. Treat ROAS as an efficiency signal, not a profit statement.

CAC: the cost of acquiring customers

Average customer acquisition cost answers a different question — not "how much sales?" but "how much did each customer cost?"

CAC = Shop Campaign ad spend ÷ Shop Campaign customers

Shopify attributes those customers when their last paid click happened within seven days. So this is specifically the Shop Campaign figure, not a whole-business CAC across every channel.

Why look at CAC when you have ROAS? Because they can disagree. A campaign can post a healthy ROAS while acquiring very few, high-spending customers, or a modest ROAS while efficiently bringing in many new buyers. If growing your customer base matters — not just this month's sales — CAC tells you whether acquisition is affordable. ROAS alone can hide that.

AOV: are your campaigns bringing valuable orders?

Average order value adds a third dimension. Shopify defines it as:

AOV = Shop Campaign sales ÷ Shop Campaign customers

Read that carefully: it's calculated per customer, not per order. Treat it as "how much value did each acquired customer bring," rather than a strict per-transaction average.

AOV is what stops CAC from being misleading. Consider two campaigns:

• Campaign A — lower CAC, lower AOV. Cheap to acquire customers, but each brings a smaller order.
• Campaign B — higher CAC, higher AOV. More expensive to acquire, but each customer is worth more.

Neither is automatically "better." A store selling considered, high-margin products might happily pay B's higher CAC; another might prefer A's volume. The point isn't to pick a winner from one number — it's to weigh the combination against your own economics.

Reading ROAS, CAC and AOV together

The three headline metrics each answer a different question. The mistake is expecting one of them to answer all three.

Read across the row, not down a single column. A campaign is "working" when the whole picture — ROAS, CAC, AOV, plus the underlying sales and customer counts — fits your business, not when one metric looks good.

Customer segments matter too

Shop Campaign data can be broken out by customer segment — where a lot of the insight lives. Shopify's segments are a defined set:

• New customers
• All other customers
• Lapsed – 3 months
• Lapsed – 12 months

These are Shopify's own segments for Shop Campaigns, not arbitrary custom audiences. The split is useful: a campaign that looks average overall might be acquiring new customers cheaply while barely moving lapsed ones — or the reverse. Segment-level reporting tells you who is responding, so you can judge a campaign against what it was meant to do instead of one blended average.

Time-based reporting: don't judge from one snapshot

Shop Campaign data is available across time dimensions from hourly to yearly, in your store's timezone. Use that range.

A single day's ROAS or CAC can be noisy — a slow Tuesday, a post-payday spike, a one-off large order. What matters is the trend: how performance moves over a week, how it shifts after you change a budget or an offer, and how seasonality plays in. There's no magic "wait N days" rule here; just look at a meaningful window and the direction of travel, not a single point.

How ShopifyQL widens what you can do

Inside the admin, you review a campaign. With the data in ShopifyQL, you can analyze it. Because shop_campaign_insights is queryable — by campaign name, by customer segment, and across time — it can feed custom reports and dashboards, business-intelligence workflows, third-party analytics tools that support ShopifyQL, and reporting that sits alongside your other channels.

You don't need to write ShopifyQL yourself — a capable analytics tool or a developer can do that. What matters for merchants is that Shop Campaign performance is no longer trapped in one screen; it can live wherever you already do your reporting.

A practical Shop Campaign analytics workflow

A repeatable way to read a campaign:

1. Check total spend — what did it cost?
2. Review attributed sales and orders — what did that spend generate?
3. Inspect ROAS — sales relative to spend.
4. Review CAC — what you paid per customer.
5. Check AOV — the value each customer brought.
6. Break it down by segment — new vs. all other vs. lapsed.
7. Compare across time — trend, not a single day.
8. Weigh it against your own margins and targets — the numbers only mean something inside your economics.
9. Decide the adjustment — budget, audience, offer, or creative — if one is warranted.

Resist doubling or cutting a budget off one metric. A great ROAS with a punishing CAC, or a low CAC with an AOV below break-even, are both reasons to look closer first.

Common mistakes when reading Shop Campaign data

The errors that quietly lead to bad decisions:

1. Looking only at ROAS. It's the loudest metric and the easiest to misread as profit.
2. Ignoring CAC. A good ROAS can hide expensive customer acquisition.
3. Ignoring AOV. Cheap customers who place tiny orders aren't always a win.
4. Confusing sales with profit. Shop Campaign "sales" include taxes and shipping and exclude refunds — but not your margins or costs. And attribution credits a sale to the campaign; it doesn't prove the campaign caused it.
5. Comparing campaigns with different objectives. A new-customer campaign and a lapsed-win-back campaign shouldn't be judged on the same bar.
6. Ignoring customer type. A blended number hides which segment actually responded.
7. Judging on too short a window. One day is noise, not a trend.
8. Comparing across platforms without checking definitions. CAC here uses a last-paid-click, seven-day window, and Shop Campaign AOV is per customer; another tool's numbers may be defined differently.

Shopify admin vs ShopifyQL: which do you need?

Both, depending on the question.

Shopify admin reporting is enough when you simply want to review a campaign's performance and make a call.

ShopifyQL earns its place when a team needs more: custom analysis, broader dashboards, data integrations, or cross-channel views that combine Shop Campaigns with everything else. If reconciling campaign sales back to real money matters, it pairs naturally with reading your Shopify Payments activity, and it fits the same "when native reporting isn't enough" moment as maturing your product and store data.

Not every merchant needs ShopifyQL — use the level of reporting that matches your business.

The bottom line

Shop Campaign analytics now give merchants real visibility into how ad spend becomes sales, customers, and order value — in the admin, and through ShopifyQL for anyone who wants to go further. ROAS, CAC, AOV, sales, and customer counts each answer a different question, and the value comes from reading them together, against your own margins and goals.

The goal isn't to chase one perfect metric. It's to understand whether a campaign's spend is producing the customers and orders your business actually needs.

If your Shopify store needs custom reporting, analytics dashboards, or integrations that go beyond Shopify's native reporting, AD Digitech can help design and build a solution around your existing Shopify stack. See our Shopify store development work, or talk to us about your reporting workflow.