Case Study: Driving E-commerce Profitability Through Strategic Custom Labels in Google Ads

Leveraging Custom Labels for Enhanced Ad Campaign Performance and Profitability

Introduction and Business Context

In the evolving landscape of e-commerce, businesses face the challenge of optimizing digital advertising eff orts to enhance profi tability. This case study explores how one merchant successfully leveraged custom labels in Google Ads to overcome signifi cant challenges and drive profitability.

The Challenge: Lack of Granular Control and Revenue-Based Optimization

The merchant's marketing team faced several hurdles in managing their Google Ads campaigns, particularly with the adoption of Performance Max (PMax):

• One-Size-Fits-All Algorithmic Optimization:

The PMax algorithms were adept at maximizing overall revenue but often prioritized high-volume, quick-conversion products over items with higher margins but lower sales volumes. This lack of diff erentiation between revenue types hindered profi t maximization.

• Difficulty Promoting Strategic Segments:

Automated systems struggled to prioritize specifi c product segments needing increased visibility, such as new arrivals, seasonal products, or high-margin items critical for profi tability.

• ROAS as a Misleading Metric:

Focusing solely on ROAS masked profi tability issues, as campaigns with high ROAS could still generate minimal or negative profi ts if skewed towards low-margin products.

• Limited Transparency and Control in PMax:

The "black box" nature of PMax restricted visibility into targeting mechanisms and budget allocation, complicating eff orts to optimize campaigns eff ectively.

The Solution: Strategic Implementation of Custom Labels and Profi t-Based Optimization

To address these challenges, the team embarked on a strategic project, utilizing custom labels (custom_label_0 through custom_label_4) in their Google Merchant Center feed to segment the product catalog and inform campaign structure and bidding strategies.

Phase 1: Defining the Labeling Strategy

Given the constraints of fi ve custom labels, the team prioritized:

1. custom_label_0: Margin Tier - Categorized products into HighMargin, MidMargin, and LowMargin.
2. custom_label_1: Historical Performance - Labeled products as Bestseller, AveragePerformer, PoorPerformer.
3. custom_label_2: Promotional Status/Seasonality - Included labels such as OnSale, BlackFriday, NewArrival.
4. custom_label_3: Price Bucket - Grouped products by price ranges.
5. custom_label_4: Variant/Stock Availability - Managed spend with labels like HighStock, LowStock, LimitedVariants.

Phase 2: Technical Implementation of Labels

The team used:

• Feed Rules in Google Merchant Center: For attributes present in the primary feed.
• Supplemental Feeds: For calculated or external data, using tools like Google Sheets.

Phase 3: Campaign Restructuring and Bidding Strategies

Based on the custom labels, campaigns were restructured, particularly within PMax, into:

• Separate PMax Campaigns: For margin tiers (PMax - High Margin, PMax - Mid Margin, PMax - Low Margin).
• Bidding Adjustments: tROAS was set lower for High Margin and higher for Low Margin campaigns to optimize spend toward profi tability.
• Asset Groups: Created within campaigns using other labels (e.g., Bestseller, NewArrival).

Results Achieved

The strategic use of custom labels and profi t-based optimization yielded impressive results over 6-12 months:

• Increased Net Profi tability: A 40% increase in net profit from Google Ads campaigns, with a modest 15% increase in total revenue.
• Improved ROAS on Key Segments: A+96% increase in ROAS for the HighMargin and Bestseller segments.
• Reduced Inefficient Spend: A 15% reduction in spending on low-performing or low-stock items.
• Better Alignment with Business Goals: The focus on profi t-based metrics improved communication and alignment between marketing and company leadership.

Analysis and Discussion

This case study underscores the importance of moving beyond surface-level metrics like revenue-based ROAS and implementing granular segmentation strategies based on internal business intelligence.

• Strategic Value of Custom Labels:

Custom labels were essential for translating business objectives into actionable structures that Google Ads algorithms could leverage.

• PMax Segmentation:

Segmenting at the campaign level for margin was crucial for budget and profitability control, with asset groups further refining subgroups.

• Automation and Maintenance:

Dynamic label management required automated processes, as manual maintenance was unsustainable.

• Guiding the Algorithm:

The strategy aimed to enhance algorithm performance through strategic signals rather than replace it.

Conclusion and Future Implications

This case study illustrates the transformative impact of strategic custom label use in Google Ads for e-commerce businesses. By shifting from generic optimization to profi t-focused management, the company not only improved its bottom line but gained greater control over strategic priorities.

Key Takeaways:

• Align success metrics with real business goals (profit vs. revenue).
• Use custom labels for product catalog segmentation.
• Structure campaigns to refl ect strategic segmentations with diff erentiated budget and bid control.
• Adopt advanced bidding strategies like tPOAS for direct profi tability optimization.
• Invest in automation for dynamic feed management.

Future steps involve refi ning performance label scoring models, exploring additional labeling dimensions, and integrating Customer Lifetime Value (CLV) more deeply into bidding strategies. This case highlights the enduring importance of human strategy and business insight in guiding technology toward optimal results.