Case Study: Scaling Paid Search with Smarter Automation

Paid Search | Google Ads | GA4 | Value-Based Bidding | Experimentation

Client Type: High Growth eCommerce
Role: Paid Search Strategy & Management
Initiative: Led a structured Paid Search experiment pairing a smarter bidding strategy with broader query coverage to scale automation and improve efficiency.

Executive Summary

Objective

  • Unlock incremental scale and improve conversion quality through a smarter automation strategy.

Challenge

  • Signal sparsity, restrictive matching, and volatility limited sustainable growth.

Solution

  • Value-based bidding +

  • Broad match +

  • Aggregated GA4 signals.

Outcome

  • Conversion volume ↑

  • Revenue ↑

  • ROAS ↑

Hypothesis: Expanding conversion signal scope by pairing value-based bidding with broader query coverage would improve conversion value while improving campaign efficiency.

Test Design

  • Connecting the Google Ads account to a GA4 property with more robust data would strengthen automation signals and lead to higher-quality conversions.

  • Value-based bidding would improve conversion quality by shifting optimization away from lowest cost and toward higher-value outcomes.

    • Use Google’s broad match type automation to capture demand at lower costs instead of expanding the keyword list with redundant branded keywords.

    • Pause unbranded keywords so only high-intent users reach the site, while Performance Max captures unbranded search queries.

  • Launched during Peak-season to accelerate learning

Framework

  • ROAS maintained above 2.5

  • Cost per purchase maintained below $40

  • Spend increases capped during the learning phase to reduce volatility

Primary KPIs

  • Purchase volume

  • Total revenue

  • ROAS

*Primary success was defined by incremental conversion value and sustained efficiency at scale.

Results

YoY Revenue Data

Data shown is illustrative.

  • Simplified campaign structure and improved signal quality enabled Google’s algorithms to exit the learning phase faster, stabilize performance, and scale more efficiently.

  • Increased 42% YoY

  • Reduced campaign spend by 50% year over year while driving a 30% increase in revenue.

  • Increased 85% Year-Over-Year

Key Takeaways & Future Optimizations
Performance Insights & Next Steps

  1. Work with Web/Dev team to improve user experience, and ad quality.

  2. Test new creative combinations and identify top-performing messages.

  3. Test AI Max to continue utilizing Google’s innovative automated tools.