A Guide to Structuring Google P-Max Asset Groups for Shopify Merchants

Google's P-Max is a powerful automated campaign that can deliver ads across every channel — Search, Display, YouTube, and more. To get the most out of its automation, the strategic way you divide your asset groups becomes critical.

The basic concept is to structure each asset group as "a collection of assets related to a specific theme or target audience."

By carefully setting up multiple asset groups within a single campaign, your ads are delivered with more relevant messages and creatives, leading to better results. Recently, P-Max's reporting and control capabilities have been strengthened, making it possible to understand and optimize the performance of each asset group in detail. The effective use of asset groups is expected to become increasingly important going forward.

In this article, we introduce the concepts and examples behind how to divide asset groups in Google Ads P-Max.

For a comprehensive overview of Google Ads for Shopify merchants, please refer to the article below. If you want to understand the full picture, check it out.

How to Think About Dividing Asset Groups

First, as a fundamental principle when splitting asset groups, it is important to give each group a clear theme or purpose. Splitting them too arbitrarily disperses data and makes it difficult to evaluate performance, but conversely, cramming too many diverse elements into a single group will "muddy" your performance analysis.

We keep the following points in mind when grouping.

Group by Theme and Target Audience

Asset groups should be organized around common themes such as product category, brand, messaging, and target audience.

For example, groups like "new arrivals," "sale items," and "women's products" would each have their own relevant ad copy and images. This makes it easier for the assets within each group to align with user interests, and you can expect improvements in click-through rates and conversion rates.

It is also officially recommended to include a diverse range of materials — text, images, videos, etc. — within each asset group so that the ad strength rating reaches "Good" or above. This is to maximize ad placements while making it easier for Google's machine learning to find the optimal combination.

Group According to Business Challenges

You can also select groupings based on your business challenges. For example, if the goal is to maximize revenue, you might create a "best-selling products group"; if acquiring new customers is the challenge, a "popular products for new customers group"; and if you prioritize profitability, a "high-margin products group."

By choosing your grouping criteria based on "what you want to achieve," you can apply appropriate targets and creative strategies to each group. However, you need to be careful about splitting too heavily based on a single metric. For example, grouping by "top-selling products" may seem logical at first glance, but since it relies on data from existing customers, it can be difficult to reach new customers.

Therefore, rather than focusing solely on sales or profit, it is essential to also consider customers' search intent and product characteristics, and to classify by product type and product features.

Divide So There Is No Overlap or Conflict Between Groups

You can create up to 100 asset groups, but "just because you can doesn't mean you should use all of them." Rather, it is preferable to give each group a clear role so there is no overlap or conflicting purposes with other groups. Rather than proliferating similar groups, integrating anything you can't justify the need for into existing groups is also advantageous from a data accumulation standpoint.

On the other hand, having only one asset group per campaign is said to be inefficient (a lost learning opportunity). In our experience as well, having multiple asset groups rather than just one tends to produce better results.

Additionally, if budgets or bidding strategies differ significantly, it is best to split into separate campaigns.

Industry-Specific Examples

At StoreHero, we continuously optimize P-Max asset groups based on the principles described above, using our proprietary Shopify growth platform, StoreHero.

Here, we introduce operational examples of asset groups based on hypothetical Shopify stores selling apparel, furniture/interior goods, and bags/wallets/accessories.

Apparel

New Arrivals, Regular Items, Sale Items, Staple Items
In the fast-moving apparel industry, the types of buyers differ for "new arrivals" (just launched), "regular items" (post-launch), "sale items" (end-of-season clearance), and "staple items" (ongoing sellers). Therefore, dividing asset groups by "new arrivals," "regular items," "sale items," and "staple items" is an effective approach.

When there are many products, using custom labels to classify the listing groups linked to each asset group makes setup and management easier. For example, you might tag products within the first 15 days after launch as "new arrival" in Custom Label 0, and products being sold at clearance prices past their projected sell-through date as "sale item" in Custom Label 1.

For fast-moving apparel stores, it is necessary to combine not just ads but also CRM, the website, social media, and more to handle the flow from new product launches to inventory clearance. With this in mind, we developed the Shopify growth platform StoreHero, believing there was a need for a system that recommends optimal products across all channels based on sales and inventory data. We use StoreHero to manage growth operations for apparel stores where conditions change rapidly.

Design, Color, and Pattern
When you analyze the products purchased by different customer segments through customer surveys, you may find that preferred product designs, colors, and patterns differ by age group and purchase motivation. Once you recognize this, it can be effective to divide groups by design, color, and pattern.

You might create asset groups along the lines of "cute," "elegant," "stylish," "solid color," and "bold." In this case as well, if there are many products, classify the listing groups using custom labels.

The chart below shows performance after dividing P-Max asset groups by design and pattern for an apparel store. The red line represents ROAS, and the blue line represents conversion value (revenue). By dividing the asset groups appropriately, efficiency improved, allowing revenue to grow while maintaining ROAS even as spend increased.

For brands that don't run new arrivals or sales very frequently, classifying by design, color, and pattern tends to produce stable and consistent performance.

Interior / Home Furnishings

Household Composition
A single-person household and a family of four have different home layouts and lifestyles, so even within the same product category, the products purchased will differ.

Therefore, asset groups are divided by item category x household composition — for example, desk x single person, desk x couple, desk x family with children, and so on.

Since Shopify's product type field corresponds to Google Ads' product category, setting item categories in the product type and setting household composition in custom labels allows you to create listing groups that combine category and household composition. Ad headlines, images, and videos are also optimized for the target household composition.

Style and Color
Similar to apparel design, in the interior space, the styles and colors purchased vary by customer demographic. Groups can be divided by style and color, such as "Scandinavian modern," "Japanese style," "minimal," "monochrome," and "bright tones." Listing groups and assets are optimized for the target customer.

Interior products do have seasonal fluctuations in sales, but compared to apparel, many items can be sold over a longer period. This makes it effective to create dedicated landing pages per asset group to improve CVR (conversion rate) from ads.

Bags, Wallets, and Accessories

Age, Gender, and Occupation
For bags, wallets, and accessories, purchasing tendencies vary significantly by age, gender, and occupation. By cross-referencing customer surveys with purchase history, you can identify the popular products within each customer segment.

Asset groups are divided by customer segment, popular products are set as listing groups, and ad headlines, images, and videos are configured accordingly. Custom labels are used to determine popular products.

Use Case
For bags, wallets, and accessories, the products purchased also differ by use case — commuting/school, travel/business trips, home use/gifts, and so on. Asset groups are divided by use case, and messaging is tailored to the interests of each use case (size, functionality, design, price, etc.).

To execute these kinds of initiatives, regardless of industry, collecting buyer attributes and linking them to purchase history is essential. When you know the answers before deciding on your strategy, the precision of your initiatives improves significantly.

As audience signals, it is also effective to not only register a customer purchase list but also to reflect the interests and affinities of your target audience.

Summary

The way you structure asset groups in Google Ads P-Max campaigns allows for a highly strategic approach. Rather than simply dividing by product category or keywords, you can boost performance by designing your groups with your business goals and customer insights woven in.

In recent years, P-Max has been updated in the direction of enabling detailed data analysis at the asset group level, giving operators more opportunities to demonstrate their expertise. Additionally, Shopify customer data can now be natively integrated as Google Ads audience data, enhanced conversions can be set up without any code, and the integration between Shopify and Google continues to evolve every day.

Using this article along with Google's official documentation and the latest case studies, refine your own unique P-Max asset group strategy.

If you're unsure about running Google Ads on Shopify, feel free to consult StoreHero, your Shopify growth operations partner. We'd be happy to help.