Key Takeaways

1. Evolution of Google Ads:

   • Numerous game-changing releases since 2015 (Customer Match, ETAs, PMax, etc.)
   • Increasing reliance on AI (RSAs, PMax, potential future keywordless campaigns)
   • Control taken away from advertisers (exact match changes, search terms restrictions, etc.)

2. Changing role of PPC professionals:

   • From PPC teachers to PPC doctors to PPC pilots
   • Increased focus on data management and strategic decision-making

3. The challenge of balancing efficiency and growth:

   • Clients want improved ROAS, lower CPC and CPA, without cutting volume
   • Traditional solutions (bid strategies, ad copy, keyword mix) may not always suffice

4. Case study: Strategically editing conversion value data

   • Problem: CPC and CPA inflation due to unusually high-value bookings
   • Solution: Capped the value distribution at the 95th percentile
   • Result: 26% increase in ROAS, 9% decrease in CPC, 13% decrease in CPA

5. Concept of Perverse Incentives:

   • Historical examples: Great Hanoi Rat Massacre, Four Pests Campaign
   • In PPC: How sending "correct" data can lead to undesired outcomes

6. The importance of strategic data management:

   • What you choose to tell (and not tell) Google is fundamental
   • Balancing first-party data, COGS, budget, etc. against Google's AI-driven features

Action Items

1. Analyze your conversion value distribution:

   • Identify any outliers or unusual patterns that might be skewing your data

2. Hypothesize potential perverse incentives in your account:

   • Look for situations where "correct" data might lead to undesired outcomes

3. Consider testing value capping strategies:

   • Identify high-percentile conversion values that might be inflating CPCs
   • Develop a hypothesis for how capping these values might impact performance

4. Implement a controlled test of value capping:

   • Apply value capping to a portion of your account or campaigns
   • Monitor key metrics (ROAS, CPC, CPA, conversion volume) closely

5. Analyze the results of your test:

   • Compare performance metrics before and after implementing value capping
   • Assess whether the strategy improved overall efficiency without significant volume loss

6. Review your data sharing strategy with Google:

   • Evaluate what data you're currently sharing and its impact on performance
   • Consider if there are areas where sharing less or modified data might be beneficial

7. Stay informed about Google Ads updates:

   • Regularly review new features and changes to understand how they might impact your control over campaigns

8. Develop a framework for identifying and addressing perverse incentives:

   • Create a process for regularly reviewing account performance for unexpected outcomes
   • Establish guidelines for when and how to intervene with data manipulation strategies

9. Educate your team or clients about this approach:

   • Explain the concept of strategic data management and its potential benefits
   • Emphasize the importance of hypothesis-driven testing and careful monitoring

Remember: This strategy may not work for all businesses. Always start with a clear hypothesis, test carefully, and closely monitor results. The goal is to find a balance that improves overall performance while maintaining the integrity of your advertising efforts.

For more information and case studies, visit: https://we-discover.com/case-study/outlier-value-capping-adjusting-google-ads-conversion-value/