7 ways to segment Performance Max and Shopping campaigns

When setting up Performance Max or Shopping campaigns, structure and segmentation are often among the first considerations.

Your approach depends on the account’s size, conversion volume and overall business objectives. 

This article explores the advantages and disadvantages of the most common campaign segmentation methods, including:

• A single “one size fits all” campaign.
• Segmentation by best sellers, product attributes, location, profitability and user type.
• A comprehensive product matrix approach. 

1. One campaign fits all

This approach is common for small accounts that don’t generate high levels of conversions, making segmentation difficult to justify.

I find that 50 conversions every 30 days is a fair threshold for sustaining ROAS and CPA targets.

Using just one campaign is also common for medium-sized accounts, particularly those with high-end products, high average order values (AOV) or in service-based industries like SaaS and insurance, where CPCs and CPAs are high.

Unless you have a large budget or are bidding on micro-conversions, achieving 50 conversions every 30 days can be a challenging target.

Account managers often segment for the sake of segmentation.

More campaigns can make the account appear more structured and justify our role. However, when campaign conversion numbers fall to between 10-20 per month, consolidating back to one campaign often improves advertising conversion data.

Advantages

• Data rules in PPC, and when one campaign has all the data, it has plenty of power. That bit of jewelry in “The Lord of the Rings” had more cumulative power than the smaller rings combined before being melted down into one. (There’s one sentence I never thought I’d ever write, but hopefully you get the point.)
• If you have a relatively small product catalog and a low variance of pricing/margins on your best sellers, there will be less or a need to segment. This is often the case with D2C accounts.

Disadvantages

• Effectively, there is no product differentiation in your advertising. Even when using smart bidding, you are still lumping the poorest-performing products in with the best sellers. This results in wasted spend.
• You have little to no control regarding product prioritization and are handing the keys over to Google’s algorithm with smart bidding. Google knows a lot, but not everything about your product catalog.

2. Segment by best sellers

If your main goal is to generate the most revenue, focusing on best sellers makes sense.

This approach is common when you have enough conversion data.

Prioritize promoting top-selling products and limit spending on those that don’t perform well.

Advantages

• Much more control of pushing products that have sold well or that you are expecting to sell well due to seasonality.
• Can efficiently integrate promotional products and assets via tailored asset groups, promo extensions, price extensions, etc. No need for separate campaigns.
• Relatively low effort to tier segments via Custom labels.
• The product analysis process can range from using only Google Ads data to including GA4 or platform data.
• An effective strategy to scale conversion numbers and value.

Disadvantages

• Mostly based on previous data rather than current/future.
• May not suit businesses where revenue is largely price-sensitive and pricing/promotions are updated regularly. What sold well last month may not perform well this month.
• Not all businesses’ main priority is to drive revenue. Focus on best sellers wouldn’t facilitate their key objective.

3. Segment by product attributes

This is often the easiest entry point into campaign segmentation. Data such as brand and product category are mandatory feed attributes, making them readily available without the need for custom labels. 

Many businesses also allocate budgets based on brand or product type, so structuring campaigns this way is often the most natural starting point.

Advantages

• Collective data within the Ads platform allows for minimum time allocation with product analysis and segmentation.
• For accounts with large product catalogs that cannot realistically be managed at an SKU level, grouping the data by brand or category makes it easier to manage.
• More streamlined integration with campaign assets based on product attributes, such as Brand promotion extensions, product category structure snippets, etc.
• Advanced shopping reports on best sellers and price competitiveness are available in the Merchant Center for brands and product categories.

Disadvantages

• SKU performance can vary significantly within each attribute, so grouping data not based on priority products can lead to wasted spend on poor performers and overreliance on best sellers.
• More focus on scaling individual attribute performance (i.e., specific brands) rather than the overall account.
• Product attribute segmentation is more commonly applied at the asset group level, often making campaign segmentation arbitrary.

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4. Segment by location

Segmenting by country is often necessary to handle different currencies, languages, landing pages, and feeds. It’s also common to target different regions within the same country to optimize shop visits.

Advantages 

• There are more options available to accommodate regional differences and tailor the user journey (e.g., a custom-made landing page for each location). This is popular with non-feed service Performance Max campaigns.
• Easy budget management if different locations have different allocations. 
• Ideal strategic approach for exploring new market opportunities.

Disadvantages 

• Multiple regional feeds are often more difficult to manage and maintain quality. 
• Often unable to segment further based on the client’s performance objective (best sellers, profit, etc.) because conversion data is already spread across multiple campaigns, reducing growth opportunities within existing markets.

5. Segment by profit

Business CEOs and CFOs don’t often discuss ROAS or CPA when requesting updates on their online advertising performance. That’s because it’s more of a marketing KPI than a business KPI.

Without profitability, what are we really doing here?

Integrating profit data into your campaigns is becoming more common practice, enabling you to use third-party tools such as Profit Metrics or Google’s brand-new profit optimizer feature (in beta as of writing).

Advantages

• More accurate alignment of the client’s actual business goals than revenue growth. 
• Greater opportunity to scale performance of account when optimizing toward true marketing objective.
• Creates greater client trust and understanding of campaign strategy and structure when you are speaking their language.
• Despite growth in approach, adaptability is still very low. This is an opportunity to have a competitive edge.

Disadvantages 

• Some businesses are wary of sharing profit data, while others don’t have the resources to provide cost of goods sold (COGS) data to utilize profit optimization tools.
• The strategy relies on accurate and consistent COGS data added by the client, which is difficult to maintain, especially with large catalogs.
• Reallocating more budget toward products with higher margins can often reduce spend on best sellers (often low margin). It could have an initial detrimental impact on revenue growth.
• Switching from ROAS to POAS (profit on ad spend) is an advanced strategy and, without proper planning and gradual rollout, can derail campaign performance.

6. Segment by user

Performance Max has faced criticism for focusing heavily on returning users to drive results, making it challenging to measure incrementality compared to its predecessor, Smart Shopping. 

However, by concentrating on acquiring new customers, companies can better assess the true value of their ads and measure incrementality more accurately, especially if growing the customer base is a key advertising objective.

Advantages 

• If businesses know their lifetime value (LTV) per customer, this strategy allows them to efficiently target campaign CPAs within that remit.
• Dedicated budgets, assets and messaging targeting new and returning customers.
• Unlocking product insights for both new and returning users allows you to integrate this information across other channels. For example, accessories might be popular among returning customers, so you can set up dedicated email flows targeting previous buyers with these insights.

Disadvantages 

• Even with Google holding fire on cookie deprecation, it’s still difficult for them to completely distinguish new vs. returning users. Do you trust the accuracy of GA4 audience reporting?
• If you use the new customer acquisition goal, the added value you assign to a new user conversion isn’t the actual value of their purchase but what you consider the long-term LTV. So, when included with other campaigns that don’t have the same campaign goal, conversion data can seem overinflated and uneven.
• Plenty of businesses rely on the sales of returning users to drive the majority of their revenue streams, so too much focus on new user acquisition can be detrimental to performance.

7. Segment with product matrix

Advertising strategies for most businesses are multifaceted, aiming to improve various metrics such as revenue, gross profit, AOV, new and returning users, ROAS, LTV and more. To address these priorities effectively, segmentation should be based on multiple data points and levels of granularity. 

Structure your product analysis and tiering like a comprehensive grading system to prioritize each product according to marketing and business objectives.

Below is a small-scale example of how we apply this approach in our enhanced Performance Max product.

Advantages 

• A strategy most likely to achieve multiple advertising priorities businesses expect from their marketing.
• The granular product analysis approach helps reduce wasted spend and makes your budget work more efficiently across your entire catalog.
• Despite the advanced nature of this approach, automated products can save hours in manual analysis and tiering implementation.
• Flexible in adjusting priority data points based on changing market conditions (e.g., emphasizing gross profit in a stagnant market or focusing on revenue during seasonal sales periods).

Disadvantages 

• This advanced strategy involves complete client collaboration to maximize data points and business priorities (not as easy to achieve as you’d imagine).
• Limited suitability toward ecommerce retailers with large catalogs and diverse product variation.

These are the most common segmentation strategies I’ve observed in Performance Max and Shopping campaigns, each with its own success stories. 

A key theme in these successes has been transparent communication between the client and account manager about business objectives, ensuring that the campaign strategy aligns closely with those goals. 

Ultimately, the effectiveness of these strategies depends on the availability of conversion and product data, which must be available to fully leverage the chosen approach.