The Challenge
A US and EU-based home goods retailer was running Google Shopping and Search campaigns with decent volume but inconsistent profitability. Their main pain points:
- Generic product feeds pulling from their site with minimal optimization
- Single-market approach despite selling across different regions
- Broad keyword and audience targeting eating into margins
- No clear picture of which products actually drove profit
Phase 1: Google Merchant Center Feed Overhaul
Before touching ad spend, we rebuilt the foundation:
Feed Optimization Using DataFeedWatch:
- Restructured product titles using high-intent keywords (added material, size, style descriptors)
- Created custom labels to segment by margin tier, best-sellers, and seasonal items
- Set up dynamic pricing rules for EU markets to account for VAT and shipping
- Added Google product categories manually for 200+ SKUs that were miscategorized
- Implemented GTIN exemption requests for proprietary products
Tools used: DataFeedWatch for feed management, Google Merchant Center Diagnostics, supplemental feeds for custom attributes
Phase 2: Website Conversion Rate Optimization
Worked with their dev team to improve landing page performance:
- Reduced page load time from 4.2s to 1.8s (compressed images, lazy loading)
- Added trust signals: reviews, secure badges, clear return policy above fold
- Simplified checkout from 4 steps to 2
- Implemented exit-intent popups with 10% discount for cart abandoners
- A/B tested product page layouts—winning variant increased CVR by 22%
Tools used: Google PageSpeed Insights, Hotjar for heatmaps, Google Optimize for A/B testing
Phase 3: Granular Campaign Architecture
Geographic Segmentation:
- Split campaigns by US, UK, DE, FR with localized ad copy
- Adjusted bids based on regional margin differences (EU shipping costs were higher)
- Created separate Shopping campaigns for each market with country-specific product feeds
Audience & Remarketing Growth Hacks:
- Built custom affinity audiences based on competitor site visitors
- Layered high-margin product campaigns with in-market audiences
- Set up sequential remarketing: Day 1-7 (product focus), Day 8-30 (discount offer)
- Used Customer Match to suppress existing customers from acquisition campaigns
Smart Bidding Implementation:
- Started with Maximize Conversion Value with a 300% tROAS target
- Gradually increased to 400% tROAS as algorithm learned
- Set up bid adjustments for device, time-of-day, and location based on conversion data
- Used portfolio bid strategies to share learnings across similar campaigns
Phase 4: Growth Hacking Tactics
Shopping Feed Tricks:
- Created duplicate listings for best-sellers with different title variations to capture more queries
- Used promotion feeds to highlight “Free Shipping” during low-traffic periods
- Implemented seasonal custom labels to push inventory during Q4
Bing Ads Arbitrage:
- Imported Google campaigns to Bing with 40% lower CPCs
- Bing delivered 12% of total revenue at 480% ROAS
Dynamic Remarketing:
- Set up product feed-based dynamic ads showing exact products users viewed
- Created urgency with “low stock” annotations for high-margin items
The Results (Over 12 Months)
- $1,212,400 in total conversion value
- $320,000 ad spend
- 410% ROAS (sustained across all markets)
- 31,847 transactions
- Average order value increased from $38 to $41
- Repeat purchase rate up 18% (tracked via Customer Match lists)
Key Takeaways
This wasn’t an overnight success. It took:
- 3 weeks to properly restructure feeds
- 6 weeks for Smart Bidding to stabilize
- Continuous A/B testing and feed refinements
The biggest wins came from feed quality and site speed—not just throwing money at ads. By treating Google Merchant Center as a strategic asset rather than a data dump, we gave the campaigns a fighting chance before optimizing bids.
Tools Stack:
- Google Merchant Center + DataFeedWatch
- Google Ads + Bing Ads
- Google Analytics 4
- Hotjar + Google Optimize for CRO
- Slack integration for real-time ROAS alerts
This project proved that profitable scaling isn’t about budget size—it’s about data hygiene, technical fundamentals, and strategic audience segmentation.