If you sell products from multiple brands in Google Shopping, you're likely treating your campaigns as one homogeneous entity. But here's the reality: different brands perform very differently. Some deliver 5x ROAS while others drain budget with minimal returns. Without brand-level analysis, you're flying blind—unable to double down on winners or fix underperformers.
This guide shows you how to measure, compare, and optimize brand performance in Google Shopping. You'll learn which metrics matter, how to segment your data by brand, and actionable strategies to improve portfolio-wide results.
Why Brand Performance Analysis Matters
E-commerce retailers typically carry products from dozens or even hundreds of brands. As Think with Google research shows, consumers increasingly search by brand when shopping online. Each brand has its own characteristics that affect Shopping performance:
- Brand recognition — Well-known brands often have higher CTR and conversion rates
- Price positioning — Premium vs budget brands attract different shoppers
- Competition density — Some brand searches face more advertisers than others
- Margin structure — Your profit per sale varies by brand
- Seasonality patterns — Fashion brands spike differently than electronics
When you analyze performance at the brand level, you can make smarter decisions about budget allocation, bidding, and inventory. Instead of applying blanket changes across your catalog, you target specific brands that need attention.
Key Insight
Retailers who segment Shopping campaigns by brand typically see 15-25% improvement in overall ROAS because they can allocate budget more efficiently to high-performing brands.
Key Metrics for Brand Comparison
Not all metrics carry equal weight when comparing brands. Here are the essential KPIs to track, organized by what they measure. For complete definitions, see our metrics glossary:
Profitability Metrics
| Metric | What It Tells You | Target |
|---|---|---|
| ROAS | Revenue returned per euro spent | Varies by margin (typically 3-5x) |
| CPA | Cost to acquire one conversion | Below your profit per order |
| Revenue Share | Brand's contribution to total revenue | Aligned with strategic goals |
Efficiency Metrics
| Metric | What It Tells You | Benchmark |
|---|---|---|
| Conversion Rate | Percentage of clicks that convert | 1-3% for Shopping |
| CTR | Ad relevance and appeal | 0.5-2% for Shopping |
| Avg CPC | Competition level for brand terms | Category dependent |
Visibility Metrics
| Metric | What It Tells You | Goal |
|---|---|---|
| Impression Share | How often you show vs could show | 70%+ for priority brands |
| Click Share | Your share of available clicks | Higher = more dominant |
| Top Impression Share | How often you appear in top positions | 50%+ for high-margin brands |
How to Segment Shopping Data by Brand
Google Ads doesn't provide brand-level reporting out of the box. Here are your options for getting brand-segmented data:
Option 1: Custom Labels in Your Feed
The most common approach is using custom labels to tag products by brand. In your Merchant Center feed, populate one of the five custom label fields (custom_label_0 through custom_label_4) with the brand name.
Once tagged, you can:
- Create product groups by custom label in your campaigns
- Run dimension reports filtered by custom label
- Set different bids or budgets by brand
The downside: this requires feed setup, and reporting still requires manual aggregation across product groups.
Option 2: Campaign Segmentation
Create separate campaigns or ad groups for each major brand. This gives you clean reporting at the campaign level but creates management overhead—especially if you carry many brands.
Best for: Retailers with 5-10 key brands that represent the majority of revenue.
Option 3: Third-Party Analytics Tools
Tools like SKU Analyzer automatically aggregate performance data by brand using your Merchant Center feed. The brand field from your feed is matched against Google Ads performance data (using Shopping segments for granular filtering), giving you instant brand-level analytics without manual setup.
This approach provides:
- Automatic brand aggregation from feed data
- Side-by-side ROAS, CPA, and impression share comparison
- Treemap visualizations showing revenue distribution
- Trend analysis to spot brands gaining or losing momentum
Building a Brand Performance Dashboard
Whether you use native Google Ads reporting or a third-party tool, your brand dashboard should answer these questions at a glance:
The Brand Comparison View
Create a table showing all brands with these columns:
- Brand name
- Revenue (and % of total)
- Cost (and % of total)
- ROAS
- Conversions
- Avg Impression Share
- Product Count (number of SKUs)
Sort by revenue to see your biggest brands, then by ROAS to see efficiency. Look for mismatches—brands with high revenue but low ROAS may need optimization, while brands with high ROAS but low revenue may deserve more budget.
The Brand Quadrant Analysis
Plot brands on a scatter chart with:
- X-axis: Revenue (or conversions)
- Y-axis: ROAS (or conversion rate)
This creates four quadrants:
| Quadrant | Characteristics | Action |
|---|---|---|
| Stars | High revenue, high ROAS | Protect and scale |
| Opportunities | Low revenue, high ROAS | Increase visibility/budget |
| Workhorses | High revenue, low ROAS | Optimize or reduce spend |
| Drains | Low revenue, low ROAS | Fix or deprioritize |
The Brand Trend View
Track how brand performance changes over time. A brand's ROAS can fluctuate due to:
- Seasonality (fashion brands in spring, electronics in Q4)
- Competitor activity (new entrants, pricing changes)
- Your own changes (feed updates, bid adjustments)
- Market trends (brand falling out of favor)
Review 30-day rolling trends weekly to catch issues before they compound. For tips on building effective dashboards, see our reporting guide.
Strategies for Optimizing Brand Performance
Once you have visibility into brand-level data, here's how to act on it:
1. Reallocate Budget to Winners
If Brand A delivers 6x ROAS and Brand B delivers 1.5x, shift budget accordingly. This sounds obvious but rarely happens without brand-level data.
Implementation options:
- Separate campaigns: Give high-ROAS brands their own budget
- Portfolio bid strategies: Group brands with similar targets using Smart Bidding
- Custom label priorities: Use campaign priority settings
2. Investigate Underperformers
Before cutting an underperforming brand, diagnose why it's struggling:
- Price competitiveness: Are you priced higher than competitors? Check price benchmark data
- Feed quality: Are titles, images, and descriptions optimized for this brand?
- Impression share: Is the brand even getting visibility, or losing auctions?
- Landing page: Does the brand page convert as well as others?
Watch Out
Don't immediately pause brands with low ROAS. Some brands have strategic value—they might bring in new customers who later buy higher-margin products, or they fill important catalog gaps that keep shoppers on your site.
3. Optimize Feed by Brand
Apply brand-specific feed optimizations:
- Title structure: Put the brand name first for well-known brands, product type first for lesser-known brands (see our product titles guide for detailed strategies)
- Image quality: Ensure premium brands have premium imagery
- Product types: Match the brand's product type and category structure for better relevance
4. Use Brand-Level Bid Modifiers
If you can't create separate campaigns, use product group bids strategically:
- Bid up on high-ROAS brands to capture more impression share
- Bid down on low-ROAS brands to reduce waste
- Set bid ceilings for brands where you're already dominant
5. Monitor Competitive Position
Use auction insights to understand your competitive position for each brand. If a competitor is dominating a brand you carry, you may need to:
- Increase bids to compete
- Improve feed quality to boost ad rank
- Accept lower share if the economics don't work
Common Mistakes in Brand Analysis
Mistake 1: Using Too Short a Time Window
Brand performance can be volatile day-to-day. Use at least 30 days of data before making strategic decisions, accounting for conversion lag. For major changes like pausing a brand, use 60-90 days.
Mistake 2: Ignoring Product Count
A brand with 5 products will naturally have less revenue than one with 500. Normalize comparisons by looking at revenue per product or ROAS rather than absolute numbers. Refer to our Google Ads reporting documentation for tips on building effective brand-level reports.
Mistake 3: Treating All Brands the Same
Your own private label brands may have different margin structures than resold brands. Apply different ROAS targets based on actual margins.
Mistake 4: Missing Seasonality
Compare brands to their own historical performance, not just to each other. A swimwear brand will look terrible in January but excellent in June.
Key Takeaway
Brand-level analysis transforms Google Shopping from a black box into a manageable portfolio. By comparing performance across brands, you can make surgical optimizations that improve overall ROAS without broad changes that hurt your best performers.
Frequently Asked Questions
How do I segment Google Shopping data by brand?
Use custom labels to tag products by brand in your feed, then create product groups segmented by custom label in your campaigns. Alternatively, use third-party tools like SKU Analyzer that automatically aggregate performance metrics by brand from your Merchant Center feed data.
What metrics should I compare across brands?
The most important brand comparison metrics are ROAS (profitability), conversion rate (purchase intent), impression share (visibility), CPA (acquisition cost), and revenue contribution (portfolio share). Together these show which brands drive profitable growth.
Should I pause underperforming brands in Google Shopping?
Not necessarily. First investigate why a brand underperforms—it could be pricing, feed quality, or seasonality. Consider reducing bids or budget allocation before pausing entirely. Some brands may have strategic value despite lower ROAS, such as attracting new customers or filling catalog gaps.
How often should I review brand performance?
Review brand performance weekly for tactical adjustments and monthly for strategic decisions. Use 30-day windows minimum to account for conversion lag. During peak seasons like Q4, increase review frequency to catch trends faster.
Conclusion
Brand performance analysis is essential for retailers managing multi-brand Google Shopping portfolios. By segmenting your data by brand, you can identify your stars, fix your underperformers, and allocate budget where it generates the best returns.
Start with these steps:
- Set up brand segmentation using custom labels or analytics tools
- Build a brand comparison dashboard with ROAS, revenue share, and impression share
- Identify your quadrant positions (stars, opportunities, workhorses, drains)
- Take targeted actions on each brand based on its position
- Review trends weekly to catch changes early
Tools like SKU Analyzer make brand analysis easier by automatically aggregating performance data by brand, complete with treemaps, radar charts, and trend visualizations. Whether you build your own reports or use a dedicated tool, brand-level visibility is the key to portfolio-wide optimization.