Final Round AI
HashMatrix optimized non-brand search campaigns for Final Round AI, improving conversion efficiency and reducing acquisition costs. Through keyword restructuring, traffic filtering, and campaign segmentation, the campaign achieved higher ROAS and significantly improved conversion performance within a short period.
Final Round AI Ads Case
Improving ROAS Through Search Optimization
Introduction
Final Round AI is an AI-powered career development platform targeting users seeking job and career growth solutions.
The campaign focused on optimizing Google non-brand search ads in the US market to improve conversion efficiency and reduce customer acquisition costs.
Challenge
Before HashMatrix took over, the campaign faced several issues:
ROAS remained low at 0.9 despite continuous spend
Traffic quality was inconsistent, with significant wasted spend
Conversion rate was low due to mismatched search intent and landing pages
Campaign structure lacked segmentation, limiting optimization potential
These challenges resulted in inefficient budget usage and limited scalability.
Strategy
1. Keyword Optimization & Expansion
Reduced total keywords from 32 to 23 high-performing terms
Introduced new high-intent keywords (40% new additions)
Focused on keywords with proven conversion potential
This improved overall search efficiency and relevance
2. Traffic Quality Control
Implemented negative keyword strategies to filter irrelevant traffic
Improved targeting accuracy to reduce wasted spend
Ensured higher-quality traffic entering the funnel
3. Campaign Restructuring
Rebuilt campaign structure based on product segmentation
Aligned each campaign with specific landing pages
Matched keywords with the most relevant conversion paths
This significantly improved conversion rates
4. Conversion-Oriented Optimization
Shifted focus from traffic volume to conversion efficiency
Continuously optimized based on real performance data
Prepared the account for long-term scaling
Execution
The campaign optimization was executed in phases:
Audit of historical performance and keyword effectiveness
Restructuring of campaigns and keyword sets
Implementation of traffic filtering mechanisms
Continuous monitoring and iterative optimization
Results
ROAS increased by 7% despite a 54% increase in ad spend
Conversion rate improved by 39%
Customer acquisition cost decreased by 9%
ROAS remained higher than the previous 2-month baseline even during scaling
The campaign demonstrated improved efficiency and scalability potential
Why It Worked
Better Keyword Efficiency
Fewer but more effective keywords drove higher-quality traffic.
Strong Intent Matching
Search terms and landing pages were tightly aligned, improving conversions.
Reduced Waste
Negative keywords filtered out low-value traffic.
Scalable Structure
The new campaign setup enabled sustainable growth and optimization.
Conclusion
This case demonstrates how structured optimization of search campaigns can significantly improve performance without sacrificing scale.
By focusing on keyword quality, traffic accuracy, and conversion alignment, HashMatrix helped Final Round AI achieve stronger ROAS and more efficient user acquisition.
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