How to Optimize Google Shopping Product Types for Maximum ROAS

If you’ve checked your Google Shopping results and wondered why you’re spending more but not seeing ROAS grow, you’re not alone. Many ecommerce brands tweak bids, test Performance Max, add negatives, or adjust budgets hoping for a breakthrough. Sometimes performance improves briefly, then flattens again. That’s because the real issue often isn’t inside the campaign—it’s inside the product feed. One of the most overlooked levers is the product_type attribute. It’s not just an optional field for compliance. Used strategically, it gives Google clearer context, smarter grouping, and better matching power, helping your products reach the right searches more consistently.

Why Product Type Is Your Most Underrated ROAS Lever

If you’re serious about improving ROAS in Google Shopping, you have to start thinking like both a marketer and a machine-learning system. Google doesn’t “read” your store the way a human does—it relies on your feed data to understand what you sell, who it’s for, and which searches your products should appear for.

That’s why Google Merchant Center optimization isn’t just about fixing disapprovals. It’s about giving Google the cleanest signals possible. And among all those signals, product_type is one of the most flexible—and most underused.

The Difference Between Google Product Category (Compliance) vs Product Type (Strategy)

This confusion shows up in almost every account audit:

• Google Product Category = mandatory classification based on Google’s taxonomy.

• Product Type = your own custom category structure.

Think of Google Product Category like a form you must submit. It helps Google place you in the right “general” bucket, but you don’t get much control.

Product Type is your strategy layer. You can shape it around:

• Your website navigation/breadcrumbs

• Your merchandising priorities

• How do you want to segment campaigns and budgets

This flexibility is exactly why product_type can improve:

• search relevance (better matching to high-intent queries)

• campaign control (clean segmentation for bidding and budgets)

How Google’s Algorithm Reads Product Types

Even though product_type isn’t visible to shoppers, it can be heavily weighted for relevance. A shallow value like:

Furniture

…doesn’t tell Google much.

But a structured value like:

Home & Garden > Furniture > Kitchen & Dining > Dining Tables

…adds context and intent. You’re telling Google:

• broad department

• subcategory

• specific buying intent

That helps in two major ways:

1. Better long-tail matching
   Long-tail searches often convert best because they’re purchase-ready. Deeper product types can support relevance for these searches by adding more accurate signals.

2. Stronger behavior in automation (Performance Max)
   With Performance Max feed optimization, Google’s system explores your catalog and decides what to push. If your product types are broad or messy, you often see uneven spend and wasted budget. Clean product_type structure creates clearer clusters—so you get better distribution between winners and underperformers.

The ROAS Framework – Structuring Product Types for Profit

Here’s the truth: you can’t scale Google Shopping ROAS if your catalog is one big bucket. When everything sits inside “All Products,” your best items can get held back while low performers quietly burn budget. A ROAS-first product_type framework has three goals:

1. Build a multi-level hierarchy that gives Google context

2. Use that structure to segment campaigns and budgets

3. Use real keyword intent to improve relevance

The 3–5 Level Hierarchy Model (Broad → Specific → Purchase Intent)

Google recommends a breadcrumb-style structure that goes from broad to specific using the “ > ” symbol with spaces.

Example:

Electronics > Computers > Laptops

Levels 1–2 (Broad):
Use these for high-level segmentation and budget allocation, like:

• Home & Garden > Furniture

• Electronics > Audio

• Apparel & Accessories > Women

Level 3+ (Specific):
This is where you capture intent and long-tail traffic:

• Home & Garden > Furniture > Kitchen & Dining > Dining Tables

• Apparel & Accessories > Women > Shoes > Running Shoes

Aim for 3 to 5 levels:

• Too shallow = generic traffic and weaker control

• Too deep = messy, unless your catalog is very consistent

A simple best practice: align product_type with your website navigation/breadcrumbs to keep your data consistent across teams and systems.

Profit-Based Segmentation Strategy

Once product_type is structured, use it to subdivide your product groups instead of relying on “All Products.”

Performance-Based Bidding:
Separate high-margin or top-selling product types into their own campaigns with higher budgets or different ROAS targets. This protects your winners and gives you predictable scaling.

Isolate Underperformers:
Identify product types with high spend and weak conversion. Move these into a “catch-all” campaign with lower bids or stricter ROAS targets—so they don’t drain budget from your top performers.

This is one of the fastest ways to improve Google Shopping campaign optimization without changing landing pages or rebuilding the entire account.

Turning Search Term Data into Product Type Intelligence

This is an advanced move many competitors skip: Use your Search Terms Report to improve product_type depth. If search terms show recurring intent patterns like:

• “extendable dining table”

• “round marble dining table”

• “solid wood dining table”

You can reflect that intent at deeper levels—only where accurate—such as:

Home & Garden > Furniture > Kitchen & Dining > Dining Tables > Extendable

Key rules:

• Reflect search intent, but keep it truthful

• Avoid filler like “Free Shipping” or “Best Price.”

• Don’t stuff synonyms that don’t describe the product

• Keep internal consistency with your site navigation

Advanced Optimization Inside Merchant Center

Once you have the framework, the next step is scaling it. For large catalogs, manual product_type updates don’t work. This is where Merchant Center becomes a real performance tool.

Using Feed Rules to Scale Without Manual Work

Feed Rules let you automatically generate or modify product_type values inside Merchant Center without changing your source feed.

Examples:

• Use brand, title, or existing categories to assign product_type

• Standardize inconsistent naming across the catalog

• Add missing hierarchy depth at scale

This is a major unlock for Google Shopping feed optimization because it keeps the structure consistent and scalable.

Seasonal & Trend-Based Product Type Expansion

Many brands change budgets seasonally, but forget feed structure. Advanced advertisers adjust deeper product_type levels to match seasonal intent.

Examples:

• Apparel > Outerwear > Winter Jackets > Holiday Gifts

• Electronics > Audio > Headphones > Black Friday

These temporary layers can help capture timely demand and improve relevance when competition is highest.

Testing & Scaling with Supplemental Feeds

Use Supplemental Feeds to test different product_type hierarchies on a subset of SKUs without touching your main feed.

You can safely test:

• 3-level vs 5-level hierarchies

• Intent-based depth for top categories

• New seasonal structures

Then scale what works across the full catalog.

Campaign Architecture That Converts Structure Into ROAS

A strong feed won’t improve ROAS if your campaign setup ignores it. Your product_type hierarchy should directly influence how you split product groups, campaigns, budgets, and bidding strategies.

Building Campaigns Around Product Types

Instead of targeting “All Products,” subdivide product groups by product_type clusters:

• Electronics > Audio

• Electronics > Computers

• Home & Garden > Furniture

This gives you clean levers to:

• fund winners

• control spend on weaker segments

• report performance in a way that makes business sense

Bidding Strategy Alignment (tROAS, Manual, Performance Max)

Use your structure to align bidding:

• Isolate high performers (high margin or stable ROAS) into dedicated campaigns with protected budgets

• Contain underperformers in a low-priority/catch-all campaign to prevent budget leaks

• In Performance Max, separate campaigns or asset groups by product_type clusters so the algorithm isn’t forced to average winners with losers

Conclusion: Structure First, Then Scale

To maximize Return on Ad Spend (ROAS) using the product_type attribute, you have to move beyond basic setup and treat it as a strategic tool.

When you implement structured, multi-level product types (3–5 levels deep), you unlock three big benefits:

1. Better context for Google’s algorithm and search relevance

2. Cleaner segmentation for campaign and budget control

3. Smarter bidding decisions—especially in automated systems

Your action plan:

• Expand product_type into a consistent breadcrumb hierarchy

• Align it with your site navigation and real search intent

• Segment campaigns based on product_type performance

• Isolate winners and contain underperformers

• Use Feed Rules and Supplemental Feeds to automate and test

Structure drives segmentation. Segmentation drives bidding control. Bidding control drives ROAS.  If you want maximum ROAS, don’t just optimize bids—optimize the foundation that tells Google what you actually sell.