Performance Max product bucketing: How to segment products for control and scale

If your Performance Max account feels like it has a favorite child, you’re not imagining it. A single campaign will usually concentrate spending on a narrow slice of your catalog, which means your bestsellers get all the oxygen while everything else fights over scraps.
Performance Max product bucketing is how you take that control back. It means separating products into different campaigns based on performance tier, business priority, or strategic value.
Instead of running a single campaign, run distinct campaigns for high performers, mid-tier products, new arrivals, and low performers, each with its own budget, Target ROAS, and bidding approach. This gives you control over which products receive aggressive budgets and which get limited spend.
In this chapter, you'll learn how to structure Performance Max campaigns correctly by understanding why single campaigns concentrate spend on a narrow set of products, how to segment your catalog into performance-based buckets, and when bucketing improves results versus when it fragments data and slows learning.
Key Takeaways

• Single Performance Max campaigns bias budget toward high-performing products, leaving new products and strategic inventory underfunded.
• Product bucketing separates products into campaigns by performance tier or business priority, giving you control over budget distribution and ROAS targets per segment.
• Reactive bucketing uses historical performance data to separate winners from losers. Proactive bucketing uses custom labels for margin, seasonality, or strategic value before performance data exists.
• Bucketing only works with sufficient conversion volume (30+ per month per campaign) and can break data if you create too many campaigns too early.

Why product bucketing matters in Performance Max

In the previous chapter, we saw how PMax campaigns concentrate the budget on products it predicts will convert.

But to understand why product bucketing matters at scale, we’ll look at two parts of how Performance Max allocates budget and learns over time:

Why top performers naturally dominate spend
How the algorithm's learning process locks in that bias

Why a single Performance Max campaign favors top-performing products

Google's AI automatically distributes budget toward products where it predicts the highest return. But this creates a natural bias.

Let’s say a furniture retailer runs one Performance Max campaign with 300 products. Their oak dining table converted 150 times last month at a consistent ROAS. The algorithm sees reliable performance and directs 40% of the daily budget to that product and a few similar winners.

They launch 50 new barstools with a better margin. These lack conversion history. The algorithm allocates under 5% of the spend across all 50 items. Without a budget, they can't get clicks, and no clicks means no conversions. Without conversions, they stay underfunded.

So, the algorithm is doing exactly what you asked: optimizing for conversion value at your Target ROAS. Since established products have the most reliable data, they appear to be the safest way to hit that goal, so they continue to win more budget.

Meanwhile, your priorities around margin, inventory, and seasonality are not part of that decision process. Unless you separate products into different campaigns or asset groups with their own budgets and targets, the system has no reason to allocate additional spend to newer or more strategic products.

How product-level learning affects budget distribution

We’ve already seen that you need at least 30 conversions in the past 30 days for Target ROAS strategies to function reliably, and conversion values must reflect actual business outcomes. Products that convert quickly during this phase receive more budget. The algorithm gains confidence in their performance and increases their exposure.
Let’s say that there’s an electronics retailer that has 1,200 SKUs in a single Performance Max campaign. Their flagship wireless earbuds drive 40 conversions per day across branded and generic searches. A new line of noise-cancelling headphones sells 3 units per day.
Within a couple of weeks, the earbuds have hundreds of conversions tied to specific audiences, queries, devices, and price points. The algorithm can see clear patterns and becomes confident about when and where to bid aggressively on that product.
The headphones, on the other hand, never reach that level of data. Their signals stay noisy. A handful of conversions spread across many audiences and surfaces. The system treats them as higher risk and keeps bids and exposure more conservative to protect your overall Target ROAS.
So even when the campaign as a whole has “enough” conversions, most of the learning is concentrated around a small group of high-volume products.

Bucketing creates separate campaigns where similar products learn independently, so seasonal items or new launches aren't competing against proven bestsellers for algorithmic attention.

💡 Not sure if Performance Max is right for your catalog size or conversion volume? We break down when it works (and when it doesn't) in our guide to
Performance Max pros and cons

2 Ways you can approach Performance Max product bucketing

Performance Max campaign segmentation can happen in two ways.
The first approach uses historical performance to separate winners from losers. The second uses business data like margin, seasonality, or strategic value to set priorities upfront. Both work, but they solve different problems and require different data inputs.

1. Reactive product bucketing based on performance data

Reactive bucketing, also called performance-based bucketing, means you run one Performance Max campaign first, collect conversion data, and then create separate campaigns for each tier using listing groups to filter products by performance level:

• High performers get their own campaign with an aggressive budget and a lower Target ROAS
• Moderate performers get a separate campaign with standard targets
• Low performers either get a minimal-budget campaign or get paused entirely
  
  Here is what that looks like in practice. An electronics retailer launches one Performance Max campaign with 800 products and a $500 daily budget. After six weeks, they analyze the product performance report.

They find:

• 50 products generated 70% of revenue at 600% ROAS (wireless headphones, smartwatches)
• 200 products drove 25% of revenue at 300% ROAS (phone cases, cables)
• 550 products contributed 5% of revenue at 150% ROAS (low-demand accessories)

They create three new campaigns using listing groups organized by Item ID:​

• Campaign A: Top 50 performers, $300/day budget, 400% Target ROAS
• Campaign B: Mid-tier 200 products, $150/day budget, 300% Target ROAS
• Campaign C: Remaining 550 products, $50/day budget, 250% Target ROAS

Now budget flows intentionally toward proven winners instead of spreading evenly across the entire catalog.

2. Proactive product bucketing using first-party and business data

Proactive bucketing means you separate products into campaigns before any performance data exists. You assign custom labels (0-4) in your product feed based on business logic.

• Custom label 0 might tag products by margin tier: "High Margin," "Standard Margin," "Low Margin."
• Custom label 1 might indicate seasonality: "Year-Round," "Summer," "Holiday."
• Custom label 2 could flag inventory priority: "Clearance," "New Launch," "Bestseller."

In a typical setup, a fashion retailer launches 300 new products with no conversion history. They can't use reactive bucketing because there's no data yet. Instead, they add custom labels in their Merchant Center feed based on margin and inventory strategy:

• Custom label 0 = Margin: "Premium" (60%+ margin), "Standard" (40-60%), "Budget" (<40%)
• Custom label 1 = Priority: "Strategic Launch," "Core Inventory," "Clearance"

They create three Performance Max campaigns using listing groups filtered by custom labels:

• Campaign A: Premium margin + Strategic launches, $200/day budget, 300% Target ROAS
• Campaign B: Standard margin + Core inventory, $150/day budget, 400% Target ROAS
• Campaign C: Budget margin + Clearance, $50/day budget, 200% Target ROAS

Strategic launches and high-margin items get budget immediately, even with zero sales history. The retailer doesn't wait 6 weeks to see what works. They allocate spending based on what matters to the business from day one.

As performance data accumulates, they can switch to reactive bucketing or keep both approaches running in parallel.

When reactive product bucketing makes sense

Reactive bucketing only works when you fulfill the right conditions. You need enough conversion data to identify patterns, stable product behavior that justifies separate campaigns, and business goals that align with letting past performance guide budget allocation.

You already have reliable historical performance data

Reactive bucketing requires conversion volume to separate high performers from low performers confidently. If you're working with 10 conversions per month across your entire catalog, splitting into multiple campaign fragments is too thin for Smart Bidding to optimize.

Your conversion tracking must be consistent and accurate. If tracking broke midway through testing, or conversion values don't reflect actual business outcomes, your performance data misrepresents which products truly work. Bucketing based on bad data creates campaigns optimized for the wrong products.

Product performance differences are clear and stable

If your top 50 products consistently deliver 500%+ ROAS while the next 200 hover around 300%, that gap justifies separate campaigns with different budgets and targets.
If product performance shifts constantly, today's winner becomes next week's underperformer. Reactive bucketing creates endless maintenance work. You're manually moving products between campaigns every week instead of letting one campaign adapt automatically.
Seasonality also complicates this approach. A product that performed well during Q4 holiday shopping looks like a winner in your historical data. Create a high-priority campaign around it in January, and it stops converting. But performance was seasonal, not inherent to the product.

You want to control visibility for new or strategic products

Historical data also shows which items consistently fail to meet ROAS targets or generate minimal conversions.

Instead of letting those products compete equally for budget in a single campaign, you isolate them into a low-priority campaign with a strict budget cap. This protects spending for products that actually deliver results.

However, this approach only works when low performers stay low performers. If products fluctuate constantly, dropping in and out of your "low performer" bucket, you're adding complexity without solving the underlying problem of inconsistent product performance.

Business priorities don’t align with past performance

If your business needs to push new product launches, clear excess inventory, or prioritize high-margin items regardless of conversion history, reactive bucketing works against you. The algorithm concentrates spending on last quarter's bestsellers while your strategic priorities get underfunded.

In those cases, proactive bucketing using custom labels for margin, inventory status, or strategic value delivers better results than waiting for performance data to dictate budget allocation.

3 Performance Max product bucketing mistakes and how to avoid them

Products shift between performance tiers, business priorities change, and catalogs expand. These three mistakes turn bucketing from a scaling advantage into a maintenance burden.

Treating bucketing as a one-time setup

Product performance changes over time. High-priority campaigns waste budget on products that no longer perform, while low-priority campaigns starve items that have started converting well, and budget allocation becomes arbitrary instead of strategic.

How to avoid this:

• Review product performance monthly and move items between campaigns based on updated data.
• Use listing groups organized by custom labels that automatically update based on performance thresholds. For example, if you tag products as "High Performer" when ROAS exceeds 500% over 30 days, products automatically qualify or disqualify as performance changes, and you reorganize campaigns accordingly.​
• Set calendar reminders to audit bucket assignments quarterly.
• Check if products have shifted performance tiers, if new inventory needs bucketing, or if seasonal patterns require temporary campaign adjustments.

Creating too many buckets too early

Google explicitly recommends having as few Performance Max campaigns as possible. Every additional campaign breaks your conversion data, slows Smart Bidding's learning, and increases the management overhead of monitoring budgets and performance across multiple campaigns.

How to avoid this:

• Start with fewer buckets and split only when performance differences justify it. If you're unsure whether to create separate campaigns, run one campaign first, collect 6-8 weeks of data, then evaluate whether clear performance tiers exist that warrant splitting.
• Consolidate multiple campaigns into a single Performance Max campaign. Google recommends increasing the budget by 20% per week during consolidation to ensure smooth transitions and maintain performance.
• Use asset groups within one campaign before creating multiple campaigns. Asset groups let you organize products by theme, audience, or category without fragmenting conversion data across separate campaigns.

Using bucketing to fix feed or structure problems

If products aren't converting because your feed has quality issues, creating separate campaigns doesn't solve the underlying problem. The algorithm still can't match your products to relevant searches or understand which audiences to target if your product data is incomplete or inaccurate.

How to avoid this:

• Fix feed quality before bucketing. Ensure all products have accurate titles, complete required attributes, correct final URLs that match your verified domain, and proper custom labels if you plan to use them for segmentation.
• Google provides Performance Max campaign [setup diagnostics](https://support.google.com/google-ads/answer/10732484?hl=en-EN that flag issues like missing product data, disapproved products, or asset group configuration problems. Resolve these before splitting into multiple campaigns.
• Bucketing amplifies whatever quality exists in your feed. Clean data gets organized effectively. Bad data gets distributed across multiple broken campaigns, making problems harder to diagnose and fix.

How product bucketing supports automation and scaling

When products are grouped by similar characteristics, automated strategies have clearer targets to work toward and signals to act on.
Here's what bucketing enables at scale:

• New products flow into the right campaigns automatically: When you add 500 new SKUs, they enter the appropriate bucket based on custom labels you've already defined. High-margin items go to your premium campaign. Seasonal products enter seasonal buckets.
• Budget changes apply strategically across entire tiers: Need to scale spend on profitable items? Increase the budget on that campaign. The algorithm distributes it across all products in that tier without you specifying which exact SKUs get more exposure.​
• Smart Bidding learns faster within focused segments: A campaign with only high performers accumulates conversion data from similar products, creating stronger optimization patterns.
• Performance shifts get handled systematically: When a product moves from "new launch" to "proven performer" based on conversion thresholds, you reassign it to the high-performer bucket. The new campaign applies appropriate bidding and budget automatically.

Apply Performance Max product bucketing consistently with Channable

Bucketing works when products move between campaigns as performance changes. Doing that manually across thousands of SKUs means constant spreadsheet exports, custom label updates, and campaign restructuring.

Channable automates the entire process from feed tagging to campaign assignment. You only need to define your rules once, and they run every time your feed updates, so products move between buckets based on current performance without manual intervention.

Plus, with Channable Insights, you see which items drive revenue, which drain budget, and which buckets need rebalancing. And Segmentation lets you group products by performance metrics, business attributes, or custom dimensions. You can analyze ROAS by margin tier, conversion rate by product category, or revenue by seasonal tag.

With Channable, your bucketing logic stays consistent as you scale, whether you add 1,000 SKUs or expand into new markets.