Google Shopping strategies can feel like a jungle. There are many ways to structure campaigns, and it’s not always obvious which setup fits your product catalog best.

One approach we’ve been testing recently is a Sale vs. Non-Sale campaign structure.

Instead of letting all products compete in the same campaign, we split them based on pricing status. Some products are on sale, while others remain at regular price. In our case, we also had products with member pricing.

After running this setup for a few months, we saw higher revenue and improved ROAS.

Here’s how the structure works and when it’s worth testing.

Why split Shopping campaigns by pricing status?

Many Shopping accounts group every product into a single campaign. The setup looks simple, but it can create challenges.

Products with different margins, price levels, and conversion compete for the same budget.

When everything sits in one campaign, Google decides where the spend goes. That can lead to some price groups attracting higher CPCs while others receive lower CPCs.

A simple solution is to separate campaigns based on price status:

• Products on sale
• Products at regular price
• Products with special pricing (for example, member discounts)

This structure gives you more control over bids, budget allocation, and visibility.

When a sale vs. non-sale shopping structure makes sense

This approach works best if your catalog naturally mixes discounted and full-price products

Before restructuring anything, I recommend starting with Custom Labels in your feed.

That way you can group products by pricing type and see how they perform.

For example:

• Sale price
• Regular price
• Member price

Let the data collect for two to three months. That usually provides enough volume to compare metrics such as:

• ROAS
• Revenue
• Conversion rate

If the price groups behave differently, that’s your signal that a campaign split could help.

What the data revealed

We first noticed the opportunity after analyzing performance through price-based labels.

Three patterns stood out:

• Member-price products had relatively high CPCs but poor ROAS
• Sale-price products had strong ROAS but weren’t getting enough exposure
• Regular-price products also had strong ROAS but relatively low CPCs

In short, CPCs were high for member-price products while sale-price and regular-price products showed strong ROAS with lower CPCs.

What happened after we split the campaigns

We rebuilt the structure around two main groups: Sale and Non-Sale.

The goal was straightforward: increase exposure for the price groups with strong ROAS and reduce CPCs for member-price products.

After about three months, we saw a shift in how spend was distributed.

Sale-price and regular-price products received a larger share of the budget, while member-price products used less spend and moved closer to the ROAS target.

Revenue increased by nearly 20%.

That happened mainly because we were finally able to scale the segments with high ROAS more aggressively than Google’s algorithm had done when everything was grouped together.

At the same time, non-brand ROAS improved, which the team attributed to better control over member-price products.

A smarter way to analyze Shopping performance

Another improvement came from how we evaluated the data.

Standard Google Ads metrics are helpful, but they don’t always reflect how a business measures success.

Custom Columns allow you to tailor reporting to your real KPIs.

Examples include:

• Conversion-time metrics that show when conversions actually occur
• Incremental value metrics that reveal how performance changes as spend grows
• Projection columns that estimate end-of-month results

This type of analysis inside Google Ads saves a lot of time compared with exporting and rebuilding reports elsewhere.

How to test a Sale vs. Non-Sale campaign structure

If your catalog mixes discounted and full-price items, this experiment may be worth testing.

A practical testing process looks like this:

• Add Custom Labels for price groups (Sale, Regular, Member)
• Let the data run for two to three months
• Compare ROAS, revenue, and exposure by label

If you start seeing the same type of pattern I described earlier,where some price groups show high ROAS but relatively low CPCs, that’s usually a good signal that splitting the campaigns could improve results.

💡Also Read: How to Segment Products in Your Google Shopping Ads Like a Pro

Where Optmyzr’s Smart Product Labeler can help

One practical challenge with this strategy is keeping product labels updated.

Products move in and out of sales, and manual feed updates quickly become tedious.

That’s where Optmyzr’s Smart Product Labeler automates this process by assigning labels based on feed attributes and rules.

For example, you could set a rule like:

• If SalePrice > 0, label the product as Sale
• If no sale price exists, the system treats it as 0, which allows the product to be categorized as Non-Sale

Once those labels exist, you can build separate Shopping campaigns around them.

Smart Product Labeler helps you:

• Automatically assign labels based on feed attributes
• Segment products into groups like Sale, Regular, or Member pricing
• Keep labels updated automatically as your feed changes

Your campaign structure then reflects the latest pricing data without manual updates.

What you can do next

If your product catalog mixes discounted and full-price items, review how those price groups perform.

Start with Custom Labels that separate products by pricing type. After a few months of data collection, check whether certain groups consistently outperform others in ROAS, revenue, or conversion rate.

If high-performing groups receive limited exposure, split your campaigns into Sale vs. Non-Sale segments. The extra control over budget and CPCs often unlocks stronger performance.

Want to see how the Smart Product Labeler can help in this process? Book a 14-day free trial of Optmyzr and test it today.

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