With the digital marketing space continuously evolving, it goes without saying that it’s crucial for marketers to stay ahead of the curve. With around 8.5 billion Google searches per day, and each one carrying unique signals—such as location, device type, and user intent— the task of manually optimising search campaigns is increasingly complex.

As Google leans into AI and machine learning, businesses like yours have a choice—embrace automation to stay competitive or risk falling behind. So, how can AI help you streamline your search campaigns and deliver better results?

This blog post explores the opportunities around embracing AI in Google search, what steps can be taken to overcome challenges, and how we can help unlock the full potential of AI in search advertising.

A shift towards automation

While most traditional best practices, like single keyword ad groups (SKAGs), were designed to give advertisers more control in the auction to focus on funnelling and directing traffic to the right keyword (and manual cost per click (CPC)), recent changes in Google have shifted the paradigm towards bid automation. As a result, finding the right balance between aggregation, insights and smart segmentation is essential for optimising performance.

The challenges of automated campaigns

There are several challenges associated with Google’s shift towards AI and automation. One key concern is the loss of control over campaign performance and the increased reliance (and need to trust in) AI.

Consolidating campaign structures can streamline management but it risks oversimplifying targeting and segmentation. While Google recommends this approach, it is not a one size fits solution. Adapting and continuous testing are essential to ensure it drives value, especially if current campaigns are performing well.

For example, manual versus automated brand bidding strategies might return different results depending on campaign goals and volume levels. But with Google’s move towards automation, manually testing CPC could be counterproductive.

At RocketMill, we tested an automated conversion bid strategy. While traffic and conversion volume remained relatively unchanged, average CPCs decreased by 40%, improving efficiency and allowing for budget reallocation.

Another challenge is that AI’s effectiveness depends heavily on data quality and quantity. Insufficient or poor-quality data can hinder performance and lead to ineffective ad delivery.

Overcoming AI and automation challenges in search

Immediate actions to take

Simplify account structure

• Consolidate campaigns and ad groups: Group campaigns and ad groups based on performance objectives or theme, instead of traditional segmentation like match types. Larger ad group volume allows Google’s AI, like responsive search ads (RSA), to learn faster and more effectively, ultimately delivering better performance.
• Test broad match Keywords: Start testing broad match keywords to help unlock additional scale, especially when paired with smart bidding. Broad match now uses AI-based keyword prioritisation based upon other keywords in the ad group, landing page and creative to ensure relevant ad group matches.

Leverage AI-driven features

• Adopt responsive search ads: Utilise all available ad slots or at least two RSAs per ad group for optimal performance.

Implement smart bidding

• Prioritise smart bidding: Use machine learning to optimise for conversions or conversion value in every action.

Start testing and experimenting

• Adapt your approach: There is no one approach that fits all. Experiment and adjust to find what works best for your specific goals.
• Implement a phased approach: Restructure in batches and conduct A/B tests to validate learnings and minimise disruption.

Actions to drive value long-term

Invest in data quality and collection

• Enhance first party data: With AI’s reliance on data quality and quantity, prioritise improving your first-party data.
• Implement enhanced conversions and consent mode: By enabling consent mode and implementing enhanced conversions, you will provide stronger conversion data for Google’s machine learning algorithms.
• Continuously monitor and refine: Regularly monitor and refine campaigns using AI – AI systems learn over time so maintaining high-quality data inputs will improve outcomes.

Scale AI adoption across campaigns

• Expanding AI integration: Expand AI usage to all search campaigns and look to utilise Performance Max for broader AI benefits.

Upskill your team

• Ongoing training: With rapid AI evolution, provide continuous training to keep your team updated on the latest AI tools and techniques.
• Change in strategy: Encourage your team to shit from day-to-day campaign management to a more strategic focus, aligning with the increased automation.

Getting AI ready

The sheer volume of search data and signals makes manual account optimisation increasingly impractical. The future of search advertising is undeniably AI-driven, making it essential to prepare your search account for automation.

By following the steps outlined in this blog, such as simplifying account structures, leveraging AI-driven features, and conducting thorough testing, you can ensure your paid search campaigns continue to deliver value.

Need assistance in AI-readying your search account? Our team is here to help.