How BigQuery ML unlocks better targeting, bidding, ROI in Google Ads

Success in Google Ads hinges on how well you use your data.

With AI-driven features like Smart Bidding, traditional PPC tactics like campaign structure and keyword selection don’t carry the same weight.

However, Google Ads provides a goldmine of insights into performance, user behavior, and conversions. 

The challenge? Turning that data into action.

Enter Google’s BigQuery ML – a powerful yet underused tool that can help you optimize campaigns and drive better results.

What is BigQuery ML?

BigQuery ML is a machine learning tool within the Google Cloud Platform that lets you build and deploy models directly in your BigQuery data warehouse.

What makes it stand out is its speed and ease of use – you don’t need to be a machine learning expert or write complex code.

With simple SQL queries, you can create predictive models that enhance your Google Ads campaigns.

Why you should use BigQuery ML for Google Ads

Instead of relying on manual analysis, BigQuery ML automates and optimizes key campaign elements – ensuring better results with less guesswork. 

Enhanced audience targeting

• Predictive customer segmentation: BigQuery ML analyzes customer data to uncover valuable audience segments. These insights help create highly targeted ad groups, ensuring your ads reach the most relevant users.
• Lookalike audience expansion: By training a model on your high-value customers, you can identify similar users who are likely to convert, allowing you to expand your reach and tap into new profitable segments.

Improved campaign optimization

• Automated bidding strategies: BigQuery ML predicts conversion likelihood for different keywords and ad placements, helping you automate bidding and maximize ROI.
• Ad copy optimization: By analyzing historical performance, BigQuery ML identifies the most effective ad variations, allowing you to refine your creatives and improve click-through rates.

Personalized customer experiences

• Dynamic ad content: BigQuery ML personalizes ad content in real-time based on user behavior and preferences, making your ads more relevant and increasing conversion chances.
• Personalized landing pages: By integrating with your landing page platform, BigQuery ML tailors the user experience to match individual preferences, boosting conversion rates.

Fraud detection

• Anomaly detection: BigQuery ML identifies unusual patterns in your campaign data that could indicate fraud. This allows you to take proactive measures to protect your budget and ensure your ads reach real users.

Real-world applications of BigQuery ML in Google Ads

By applying machine learning to your Google Ads data, you can uncover trends, refine targeting, and maximize ROI with greater precision.

• Predicting customer lifetime value: Identify high-value customers and tailor your campaigns to maximize their long-term engagement.
• Forecasting campaign performance: Anticipate future trends and adjust your strategies accordingly.
• Optimizing campaign budget allocation: Distribute your budget across campaigns and ad groups based on predicted performance.
• Identifying high-performing keywords: Discover new keywords that are likely to drive conversions.
• Reducing customer acquisition cost: Optimize your campaigns to acquire customers at the lowest possible cost.

We ran propensity models for a higher education client, and the results were striking. 

The high-propensity segment converted at 17 times the rate of medium- and low-propensity audiences. 

Beyond boosting performance, these models provided valuable insights into more effective budget allocation, both within campaigns and across channels.

4 quick steps to getting started with BigQuery ML for Google Ads

Our organization’s data cloud engineering team helps gather, organize, and run these models – a skill set many companies have yet to integrate into their paid search strategies.

However, this is changing. If you’re ready to get started, here are four key steps:

• Link your Google Ads account to BigQuery: Gain access to your campaign data within BigQuery.
• Explore your data: Use SQL queries to analyze trends and identify patterns.
• Build a machine learning model: Create a predictive model using BigQuery ML.
• Deploy your model: Integrate it with Google Ads to automate optimization and personalization.

For comprehensive guides, checklists, and case studies to assist in deploying BigQuery ML models effectively, explore the Instant BQML resources.

These materials provide step-by-step instructions and best practices to enhance your campaign’s performance.

Maximizing BigQuery ML for Google Ads

In the era of data-driven advertising, BigQuery ML is a game-changer. 

By applying machine learning to your Google Ads data, you can unlock powerful insights that enhance targeting, optimize bidding, and improve personalization.

Here are the best practices for success:

• Data quality is key: Ensure your data is clean, accurate, and up-to-date for reliable predictions.
• Start small: Focus on a specific use case before scaling your approach.
• Continuous optimization: Regularly monitor and refine your models for the best results.

By leveraging BigQuery ML, you can take your Google Ads strategy to the next level – building a competitive edge and driving better results with data-driven decision-making.