Background

An EdTech company offering live courses and upskilling programs was running multiple Google Ads campaign types simultaneously — including Search, Performance Max (PMax), and Demand Gen. Over time, the account faced performance inconsistencies:

• Branded Search campaigns saw a drop in impressions and conversions.
  
  
• Cost per acquisition (CPA) rose with unclear attribution.
  
  
• Multiple campaigns were targeting the same user base, creating overlap and inefficiencies.
  
  

Challenge

The core issue was cross-cannibalisation — multiple campaigns were bidding on similar keywords, audiences, or placements, resulting in:

• Internal competition for the same users.
  
  
• Inflated CPCs.
  
  
• Diluted campaign insights.
  
  
• Difficulty scaling while maintaining profitability.
  
  

Strategy & Solution

To address this, I restructured the Google Ads account using a layered, funnel-based approach to separate intent, audience, and goals across campaign types:

🔧 Campaign Structuring & Execution

1. Search Campaign Optimisation

• Used exact match targeting for high-intent and branded queries to preserve control over Search campaigns.
  
  
• Created custom negative keyword lists and shared them with the PMax campaign via a Google Ads representative to prevent brand traffic hijacking.
  
  
• Split non-brand and brand search campaigns to isolate performance and bidding.
  
  

2. Performance Max (PMax) Control & Segmentation

• Separated PMax into two campaigns:
  
  
  • Conversion-focused PMax for hot leads.
    
    
  • Prospecting-focused PMax with cold audience signals (e.g., interests, demographics).
    
    
• Created distinct asset groups based on product categories (e.g., tech courses, career prep) to improve audience segmentation and reduce overlap.
  
  
• Excluded customer lists (e.g., past converters) from top-of-funnel campaigns to avoid redundancy.
  
  

3. Demand Gen Differentiation

• Developed custom intent audiences focused on upper-funnel users (e.g., users interested in career growth, skill-building).
  
  
• Applied audience exclusions to ensure Demand Gen didn’t remarket to users already targeted in PMax or Search.
  
  
• Crafted unique video and visual creatives to align with the platform and audience stage.
  
  

4. Attribution & Reporting Enhancements

• Implemented GA4 audience overlap reports to monitor and adjust targeting.
  
  
• Used labelling in Google Ads (Top, Mid, Bottom Funnel) for clearer performance tracking.
  
  
• Monitored Search Term Insights in PMax and regularly reviewed shared impressions with Search campaigns.
  
  

Results After Restructuring

• 📉 Branded Search impressions increased by 40%, with a 28% drop in CPA.
  
  
• ⚖️ Balanced traffic distribution between Search and PMax, ensuring better budget allocation.
  
  
• 🔍 Improved attribution clarity, allowing smarter budget shifts between prospecting and retargeting.
  
  
• 🎯 Demand Gen delivered 23% higher CTR after excluding overlap and refreshing creatives.
  
  

Conclusion

By proactively managing audience overlaps, keyword bidding conflicts, and creative differentiation, I successfully reduced internal competition, improved campaign performance, and scaled effectively across the funnel in a complex Google Ads environment. This structure now serves as a scalable model for similar multi-channel accounts in the EdTech and online learning space.